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"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install xgboost"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 这是一段典型的“多模态特征 + 二分类”实验脚本，核心目的：\n",
"\n",
"# 用 XGBoost 等模型对样本进行二分类；\n",
"# 计算并绘制 Precision-Recall 曲线，找出在 precision≈0.8 时的阈值与召回率；\n",
"# 分别评估单特征与融合特征的贡献。\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import sklearn\n",
"from sklearn.ensemble import BaggingClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.preprocessing import StandardScaler\n",
"import csv\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.ensemble import GradientBoostingClassifier\n",
"from sklearn.linear_model import LogisticRegression\n",
"from xgboost import XGBClassifier\n",
"\n",
"def precision_recall(y_true, y_score, test_material_cnt=0):\n",
" precision, recall = [], []\n",
" for idx, threshold in enumerate(np.linspace(0, 1, num=100)):\n",
" y_pred = np.where(y_score >= threshold, 1, 0)\n",
" tp, fp, fn = test_material_cnt, 0, 0\n",
" for i in range(len(y_true)):\n",
" if y_pred[i] == 1 and y_true[i] == 1:\n",
" tp += 1\n",
" elif y_pred[i] == 1 and y_true[i] == 0:\n",
" fp += 1\n",
" elif y_pred[i] == 0 and y_true[i] == 1:\n",
" fn += 1\n",
" if (tp + fp) == 0:\n",
" precision.append(0)\n",
" else:\n",
" precision.append(tp / (tp + fp))\n",
" if (tp + fn) == 0:\n",
" recall.append(0)\n",
" else:\n",
" recall.append(tp / (tp + fn))\n",
" # 过滤掉 precision=0 的元素及其对应的 recall\n",
" filtered_pairs = [(p, r) for p, r in zip(precision, recall) if p != 0]\n",
"\n",
" # 拆分回两个列表\n",
" precision_filtered, recall_filtered = zip(*filtered_pairs) # 返回元组，如需列表可转换\n",
" auc = np.trapz(precision[::-1], recall[::-1])\n",
" return precision, recall, auc\n",
"\n",
"def calculate_classification_report(confusion_matrix):\n",
" TP = confusion_matrix[1, 1] # True Positive\n",
" TN = confusion_matrix[0, 0] # True Negative\n",
" FP = confusion_matrix[0, 1] # False Positive\n",
" FN = confusion_matrix[1, 0] # False Negative\n",
"\n",
" precision_0 = TN / (TN + FN) if (TN + FN) != 0 else 0\n",
" recall_0 = TN / (TN + FP) if (TN + FP) != 0 else 0\n",
" f1_0 = 2 * precision_0 * recall_0 / (precision_0 + recall_0) if (precision_0 + recall_0) != 0 else 0\n",
"\n",
" precision_1 = TP / (TP + FP) if (TP + FP) != 0 else 0\n",
" recall_1 = TP / (TP + FN) if (TP + FN) != 0 else 0\n",
" f1_1 = 2 * precision_1 * recall_1 / (precision_1 + recall_1) if (precision_1 + recall_1) != 0 else 0\n",
"\n",
" precision_macro = (precision_0 + precision_1) / 2\n",
" recall_macro = (recall_0 + recall_1) / 2\n",
" f1_macro = (f1_0 + f1_1) / 2\n",
"\n",
" support_0 = TN + FP\n",
" support_1 = TP + FN\n",
" total_support = support_0 + support_1\n",
"\n",
" precision_weighted = (precision_0 * support_0 + precision_1 * support_1) / total_support\n",
" recall_weighted = (recall_0 * support_0 + recall_1 * support_1) / total_support\n",
" f1_weighted = (f1_0 * support_0 + f1_1 * support_1) / total_support\n",
"\n",
" print(f\"{'':<12}{'Precision':<12}{'Recall':<12}{'F1-Score':<12}{'Support':<12}\")\n",
" print(f\"{'Class 1':<12}{precision_1:.2f}{'':<4}{recall_1:.2f}{'':<4}{f1_1:.2f}{'':<4}{support_1:<12}\")\n",
"\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"def calc_precision_test(feats, labels, xgb_model):\n",
" feats = [ele[0:7] + ele[8:12] for ele in feats]\n",
" preds = xgb_model.predict_proba(feats)\n",
" preds = [1 if pred >= 0.3838 else 0 for pred in preds[:, 1]]\n",
" matches = sum(1 for p, m in zip(preds, labels) if p == m)\n",
" total = len(preds)\n",
" match_ratio = matches / total\n",
"\n",
" return matches, total, match_ratio\n",
"\n",
"# mn, tt, mr = calc_precision_test(man_lb1_feats, man_lb1, clf_xgb_4tier_fe)\n",
"# print(\"细粒度主题相似，相同数量：{}，总数量：{}, 准确率：{}\".format(mn, tt, round(mr, 6)))\n",
"\n",
"# mn, tt, mr = calc_precision_test(man_lb4_feats, man_lb4, clf_xgb_4tier_fe)\n",
"# print(\"大众主题相似，相同数量：{}，总数量：{}, 准确率：{}\".format(mn, tt, round(mr, 6)))\n",
"\n",
"def calc_pr_test(feats, labels, xgb_model, flag=''):\n",
" # feats = [ele[0:8] + ele[8:12] for ele in feats]\n",
" # print(feats)\n",
" predict_scores = xgb_model.predict_proba(feats)\n",
" precision, recall, auc = precision_recall(labels, predict_scores[:, 1])\n",
" for i in range(len(precision)):\n",
" if abs(precision[i] - 0.8) < 0.01:\n",
" print(\"{} xgboost thres: \".format(flag), np.linspace(0, 1, num=100)[i], \"recall: \", recall[i], \"precision: \", precision[i])\n",
" return precision, recall, auc\n",
"\n",
"def plot_ue_pr(features_test, labels_test, xgb, feat_names=['mm', 'visual', 'text', 'mmcf', 'ocr', 'asr', 'rq'], colors=['navy', 'darkorange', 'darkgreen', 'purple', 'magenta', 'cyan', 'lime', 'gold'], flag=''):\n",
" idx = 0\n",
" plt.figure(figsize=(16, 12))\n",
" for feat_name in feat_names:\n",
" score = [float(feature[idx]) for feature in features_test]\n",
" print('feat_name:{}'.format(feat_name))\n",
" print(score[0:100])\n",
" precision, recall, auc = precision_recall(labels_test, score)\n",
" for i in range(len(precision)):\n",
" if abs(precision[i] - 0.8) < 0.01:\n",
" print(\"{} {} thres: \".format(flag, feat_name), np.linspace(0, 1, num=100)[i], \"recall: \", recall[i], \"precision: \", precision[i])\n",
" \n",
" plt.plot(recall, precision, lw=2, color=colors[idx], label='{} PR curve AUC = {}'.format(feat_name, auc))\n",
" idx += 1\n",
"\n",
" precision_xgb, recall_xgb, auc_xgb = calc_pr_test(features_test, labels_test, xgb, flag)\n",
" \n",
" plt.plot(recall_xgb, precision_xgb, lw=2, color='gold', label='xgboost PR curve (AUC = %0.2f)' % auc_xgb)\n",
" plt.xlabel('Recall')\n",
" plt.ylabel('Precision')\n",
" plt.xlim([0.0, 1.0])\n",
" plt.ylim([0.0, 1.05])\n",
" plt.title('{} Precision-Recall curve'.format(flag))\n",
" plt.legend(loc='best')\n",
" plt.show()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 获取数据\n",
"\n",
"!hdfs dfs -get hdfs://harunasg/home/byte_data_rec_content_sg_training/tiktok_proactive_publish_instance/wy_test/train_dataset/rwc_data/train_features_ar_jp_merge_202507_202510_add_interest_neg_ratio_20_add_interest.json\n",
"!hdfs dfs -get hdfs://harunasg/home/byte_data_rec_content_sg_training/tiktok_proactive_publish_instance/wy_test/train_dataset/rwc_data/train_labels_ar_jp_merge_202507_202510_add_interest_neg_ratio_20_add_interest.json\n",
"!hdfs dfs -get hdfs://harunasg/home/byte_data_rec_content_sg_training/tiktok_proactive_publish_instance/wy_test/train_dataset/rwc_data/test_features_20251119_eu_filter_interest4.json\n",
"!hdfs dfs -get hdfs://harunasg/home/byte_data_rec_content_sg_training/tiktok_proactive_publish_instance/wy_test/train_dataset/rwc_data/test_labels_20251119_eu_filter_interest4.json\n",
"\n",
"import json\n",
"\n",
"with open('train_features_ar_jp_merge_202507_202510_add_interest_neg_ratio_20_add_interest.json', 'r') as f:\n",
" features_train_q3_afev1 = json.load(f)\n",
"\n",
"with open('train_labels_ar_jp_merge_202507_202510_add_interest_neg_ratio_20_add_interest.json', 'r') as f:\n",
" labels_train_q3_afev1 = json.load(f)\n",
"\n",
"\n",
"# 读取时使用\n",
"with open('test_features_20251119_eu_filter_interest4.json', 'r') as f:\n",
" features_test_q3_afev1 = json.load(f)\n",
"\n",
"with open('test_labels_20251119_eu_filter_interest4.json', 'r') as f:\n",
" labels_test_q3_afev1 = json.load(f)\n",
"\n",
"\n",
"flag = '4tier_feat'\n",
"feats_tr_lst = []\n",
"feats_te_lst = []\n",
"features_train = features_train_q3_afev1\n",
"labels_train = labels_train_q3_afev1\n",
"features_test = features_test_q3_afev1\n",
"labels_test = labels_test_q3_afev1\n",
"for ele in features_train:\n",
" feat = ele\n",
" feats_tr_lst.append(feat)\n",
"\n",
"for ele in features_test:\n",
" feat = ele\n",
" feats_te_lst.append(feat)\n",
"\n",
"clf_xgb_4tier_fe = XGBClassifier(n_estimators=20, learning_rate=0.1, max_depth=5)\n",
"clf_xgb_4tier_fe.fit(feats_tr_lst, labels_train) \n",
"predict_results=clf_xgb_4tier_fe.predict(feats_te_lst)\n",
"conf_mat = confusion_matrix(labels_test, predict_results)\n",
"\n",
"print(conf_mat)\n",
"calculate_classification_report(conf_mat)\n",
"\n",
"predict_scores = clf_xgb_4tier_fe.predict_proba(feats_te_lst)\n",
"precision_xgb_4tier_fe, recall_xgb_4tier_fe, auc_xgb_4tier_fe = precision_recall(labels_test, predict_scores[:, 1])\n",
"\n",
"print(auc_xgb_4tier_fe)\n",
"\n",
"for i in range(len(precision_xgb_4tier_fe)):\n",
" if abs(precision_xgb_4tier_fe[i] - 0.80) < 0.01:\n",
" print(\"xgb_4tier_fe thres: \", np.linspace(0, 1, num=100)[i], \"recall: \", recall_xgb_4tier_fe[i], \"precision: \", precision_xgb_4tier_fe[i])\n",
"\n",
"plot_ue_pr(feats_te_lst, labels_test, clf_xgb_4tier_fe, flag='Q3Q4ROW训练数据（正样本5w）')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(feats_te_lst[0])\n",
"print(len(feats_te_lst[0]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": "import joblib\nimport tempfile\nimport subprocess\nimport os\nimport shutil\n\n# 保存模型到本地临时文件\nwith tempfile.NamedTemporaryFile(suffix='.pkl', delete=False) as tmp_file:\n local_model_path = tmp_file.name\n joblib.dump(clf_xgb_4tier_fe, local_model_path)\n\n# 本地保存路径\nlocal_save_dir = \"/mnt/bn/bohanzhainas1/jiashuo/active_proaction/xgboost_attribution_model_v3\"\nlocal_model_save_path = os.path.join(local_save_dir, \"model.pkl\")\n\ntry:\n # 确保本地目录存在\n os.makedirs(local_save_dir, exist_ok=True)\n \n # 复制模型文件到本地目录\n shutil.copy(local_model_path, local_model_save_path)\n \n print(f\"模型已成功保存到: {local_model_save_path}\")\n \n # 可选：同时保存模型配置信息\n model_info = {\n 'n_estimators': 20,\n 'learning_rate': 0.1,\n 'max_depth': 5,\n 'feature_names': feature_names if 'feature_names' in locals() else None\n }\n \n info_path = local_model_path + '_info.pkl'\n joblib.dump(model_info, info_path)\n shutil.copy(info_path, os.path.join(local_save_dir, \"model_info.pkl\"))\n \nfinally:\n # 清理临时文件\n os.unlink(local_model_path)\n if os.path.exists(info_path):\n os.unlink(info_path)"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!hdfs dfs -ls hdfs://harunasg/home/byte_data_rec_content_sg_training/tiktok_proactive_publish_instance/wy_test/xgboost_attribution_model_v3/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import joblib\n",
"import pandas as pd\n",
"import numpy as np\n",
"from xgboost import XGBClassifier\n",
"# import pydoop.hdfs as hdfs # 或者使用 hdfs 库\n",
"\n",
"# 方法2：先将文件下载到本地再加载\n",
"def load_model_with_download(hdfs_path, local_temp_path=\"/tmp/temp_model\"):\n",
" \"\"\"\n",
" 先将 HDFS 文件下载到本地，再加载模型\n",
" \"\"\"\n",
" import subprocess\n",
" import os\n",
" \n",
" # 创建临时目录\n",
" os.makedirs(local_temp_path, exist_ok=True)\n",
" \n",
" # 下载模型文件\n",
" model_hdfs = f\"{hdfs_path}/model.pkl\"\n",
" model_info_hdfs = f\"{hdfs_path}/model_info.pkl\"\n",
" model_local = f\"{local_temp_path}/model.pkl\"\n",
" model_info_local = f\"{local_temp_path}/model_info.pkl\"\n",
" \n",
" # 使用 hdfs dfs -get 命令下载\n",
" subprocess.run([\"hdfs\", \"dfs\", \"-get\", model_hdfs, model_local])\n",
" subprocess.run([\"hdfs\", \"dfs\", \"-get\", model_info_hdfs, model_info_local])\n",
" \n",
" # 加载模型\n",
" model = joblib.load(model_local)\n",
" model_info = joblib.load(model_info_local)\n",
" \n",
" # 可选：清理临时文件\n",
" # os.remove(model_local)\n",
" # os.remove(model_info_local)\n",
" \n",
" return model, model_info\n",
"\n",
"# 模型测试函数\n",
"def test_model(model, X_test, y_test=None):\n",
" \"\"\"\n",
" 测试模型性能\n",
" \"\"\"\n",
" # 预测\n",
" y_pred = model.predict(X_test)\n",
" y_pred_proba = model.predict_proba(X_test)\n",
" \n",
" # 如果有真实标签，计算准确率\n",
" if y_test is not None:\n",
" from sklearn.metrics import accuracy_score, classification_report\n",
" accuracy = accuracy_score(y_test, y_pred)\n",
" print(f\"准确率: {accuracy:.4f}\")\n",
" print(\"\\n分类报告:\")\n",
" print(classification_report(y_test, y_pred))\n",
" \n",
" return y_pred, y_pred_proba\n",
"\n",
"hdfs_path = \"hdfs://harunasg/home/byte_data_rec_content_sg_training/tiktok_proactive_publish_instance/wy_test/xgboost_attribution_model_v3\"\n",
"\n",
"try:\n",
" # 加载模型（选择其中一种方法）\n",
" model, model_info = load_model_with_download(hdfs_path)\n",
" # 或者\n",
" # model, model_info = load_model_with_download(hdfs_path)\n",
" \n",
" print(\"模型加载成功！\")\n",
" print(f\"模型类型: {type(model)}\")\n",
" print(f\"模型参数: {model.get_params()}\")\n",
" print(f\"模型信息: {model_info}\")\n",
" \n",
" # 准备测试数据\n",
" # 这里需要根据你的实际数据格式来准备\n",
" # 示例：创建一些测试数据\n",
" np.random.seed(42)\n",
" X_test = np.random.randn(100, 26) # 假设有10个特征\n",
" y_test = np.random.randint(0, 2, 100) # 二分类标签\n",
" \n",
" # 测试模型\n",
" y_pred, y_pred_proba = test_model(model, X_test, y_test)\n",
" \n",
" # 查看预测结果\n",
" print(f\"\\n预测结果示例（前5个）:\")\n",
" print(f\"预测类别: {y_pred[:5]}\")\n",
" print(f\"预测概率: {y_pred_proba[:5]}\")\n",
" \n",
" # 如果有特征名称，可以查看特征重要性\n",
" if hasattr(model, 'feature_importances_'):\n",
" feature_importance = model.feature_importances_\n",
" print(f\"\\n特征重要性: {feature_importance}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"加载或测试模型时出错: {e}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# prep_feats_lbs 是一个 训练/验证样本构造器：\n",
"theme_mapping = {\n",
" 'Fine-grained thematic similarity': 1,\n",
" 'Unable to see': 2,\n",
" 'Irrelevant': 3,\n",
" 'General thematic similarity': 4,\n",
" 'All are just photos of a person/random shoot': 5,\n",
" 'Not my language': 6,\n",
" 'The two contents are completely the same': 7\n",
"}\n",
"\n",
"mean_value_mm = 0.5844145473966679\n",
"mean_value_visual = 0.6677746468989721\n",
"mean_value_text = 0.7259379792385305\n",
"mean_value_mmcf = 0.7186459371711221\n",
"mean_value_ocr = 0.7142993929646865\n",
"mean_value_asr = 0.8206938029243036\n",
"mean_value_rq = 0.7781203800338651\n",
"\n",
"def prep_feats_lbs(df, df_neg, fine_cnt=1063, is_train=True, POSITIVE_THEME_LABELS={1, 4}, NEGATIVE_THEME_LABELS={3}, rm_sup_tier=False, \\\n",
" feats_names=['mm_similarity'], only_manual=False, pos_ratio=0.16, keep_all_fine=False, abstract_keep_elements=2, filter_null_mm=False, max_pos_cnt=1000000000):\n",
" fine_cnt = 10000000000\n",
" ## 根据主题标签创建label\n",
" # 使用 map 函数创建新列\n",
" if 'similar_theme' in df.columns:\n",
" df['theme_label'] = df['similar_theme'].map(theme_mapping)\n",
" if keep_all_fine:\n",
" fine_cnt = 10000000000\n",
" else:\n",
" fine_cnt = (df['theme_label'] == 4).sum()\n",
" # 检查是否有未映射的值\n",
" unmapped_values = df[df['theme_label'].isna()]['similar_theme'].unique()\n",
" if len(unmapped_values) > 0:\n",
" print(f\"警告：发现未映射的值: {unmapped_values}\")\n",
" # 为未映射的值设置默认值:0\n",
" df['theme_label'] = df['theme_label'].fillna(0)\n",
"\n",
" # 添加随机负样本\n",
" if df_neg is not None:\n",
" if \"view_gid\" not in df_neg.columns:\n",
" df_neg['view_gid'] = df_neg['gid'] \n",
"\n",
" # 合并两列的mm_similarity值，计算均值\n",
" if filter_null_mm:\n",
" # 遍历df和df_neg，如果mm_similarity、visual_similarity、text_similarity、mmcf_similarity、ocr_similarity、asr_similarity、rq_similarity\n",
" # 定义需要检查的多模态相似度特征列\n",
" mm_feature_columns = [\n",
" 'mm_similarity', \n",
" 'visual_similarity', \n",
" 'text_similarity', \n",
" 'mmcf_similarity', \n",
" 'ocr_similarity', \n",
" 'asr_similarity', \n",
" 'rq_similarity'\n",
" ]\n",
" \n",
" # 只保留数据集中实际存在的列\n",
" existing_mm_columns = [col for col in mm_feature_columns if col in df.columns]\n",
" \n",
" if existing_mm_columns:\n",
" # 过滤df中所有多模态特征都不为空的行\n",
" df = df.dropna(subset=existing_mm_columns, how='any')\n",
" if df_neg is not None:\n",
" # 过滤df_neg中所有多模态特征都不为空的行\n",
" df_neg = df_neg.dropna(subset=existing_mm_columns, how='any')\n",
" \n",
" print(f\"过滤空值后，df剩余 {len(df)} 行，df_neg剩余 {len(df_neg)} 行\")\n",
" print(f\"检查的列包括: {existing_mm_columns}\")\n",
"\n",
"\n",
" global mean_value_mm\n",
" global mean_value_visual\n",
" global mean_value_text\n",
" global mean_value_mmcf\n",
" global mean_value_ocr\n",
" global mean_value_asr\n",
" global mean_value_rq\n",
" \n",
" if is_train:\n",
" combined_series_mm = pd.concat([df['mm_similarity'], df_neg['mm_similarity']], axis=0)\n",
" mean_value_mm = combined_series_mm.mean()\n",
" combined_series_visual = pd.concat([df['visual_similarity'], df_neg['visual_similarity']], axis=0)\n",
" mean_value_visual = combined_series_visual.mean()\n",
" combined_series_text = pd.concat([df['text_similarity'], df_neg['text_similarity']], axis=0)\n",
" mean_value_text = combined_series_text.mean()\n",
" combined_series_mmcf = pd.concat([df['mmcf_similarity'], df_neg['mmcf_similarity']], axis=0)\n",
" mean_value_mmcf = combined_series_mmcf.mean()\n",
" combined_series_ocr = pd.concat([df['ocr_similarity'], df_neg['ocr_similarity']], axis=0)\n",
" mean_value_ocr = combined_series_ocr.mean()\n",
" combined_series_asr = pd.concat([df['asr_similarity'], df_neg['asr_similarity']], axis=0)\n",
" mean_value_asr = combined_series_asr.mean()\n",
" combined_series_rq = pd.concat([df['rq_similarity'], df_neg['rq_similarity']], axis=0)\n",
" mean_value_rq = combined_series_rq.mean()\n",
"\n",
" print(f'mean_value_mm:{mean_value_mm}')\n",
" print(f'mean_value_visual:{mean_value_visual}')\n",
" print(f'mean_value_text:{mean_value_text}')\n",
" print(f'mean_value_mmcf:{mean_value_mmcf}')\n",
" print(f'mean_value_ocr:{mean_value_ocr}')\n",
" print(f'mean_value_asr:{mean_value_asr}')\n",
" print(f'mean_value_rq:{mean_value_rq}')\n",
"\n",
" if df_neg is not None:\n",
" null_count = df_neg['visual_similarity'].isna().sum()\n",
" print(f\"visual_similarity 列中的空值数量: {null_count}\")\n",
"\n",
" df['mm_similarity'] = df['mm_similarity'].fillna(mean_value_mm)\n",
" df['visual_similarity'] = df['visual_similarity'].fillna(mean_value_visual)\n",
" df['text_similarity'] = df['text_similarity'].fillna(mean_value_text)\n",
" df['mmcf_similarity'] = df['mmcf_similarity'].fillna(mean_value_mmcf)\n",
" df['ocr_similarity'] = df['ocr_similarity'].fillna(mean_value_ocr)\n",
" df['asr_similarity'] = df['asr_similarity'].fillna(mean_value_asr)\n",
" df['rq_similarity'] = df['rq_similarity'].fillna(mean_value_rq)\n",
"\n",
" man_feats, man_labels = [], []\n",
" man_lb1_feats, man_lb4_feats, man_ir_feats = [], [], []\n",
" man_rel_cnt, man_unrel_cnt = 0, 0\n",
" fine_grain_cnt = 0\n",
" ir_cnt = 0\n",
" total_pos_num = 0\n",
" for index, row in df.iterrows():\n",
" if rm_sup_tier:\n",
" if row['view_g_mt_diversity_tier3'] in [10071, 10005, 10080, 10073]:\n",
" continue\n",
" feature = []\n",
" for feat_name in feats_names:\n",
" feature.append(row[feat_name])\n",
"\n",
" if 'theme_label' in df.columns:\n",
" label = row['theme_label']\n",
"\n",
" if label in NEGATIVE_THEME_LABELS:\n",
" man_unrel_cnt += 1\n",
" man_feats.append(feature)\n",
" man_labels.append(0)\n",
" man_ir_feats.append(feature)\n",
" \n",
" elif label in POSITIVE_THEME_LABELS:\n",
" if label == 1:\n",
" fine_grain_cnt += 1\n",
" if fine_grain_cnt > fine_cnt:\n",
" continue\n",
" man_lb1_feats.append(feature)\n",
" elif label == 4:\n",
" # 抽象主题相似只保留相似元素个数大于1的样本\n",
" if abstract_keep_elements > 0:\n",
" similar_elements = str(row['similar_elements'])\n",
" if similar_elements is not None:\n",
" similar_elements = similar_elements.replace('[', '').replace(']', '').split(',')\n",
" else:\n",
" similar_elements = None\n",
" if len(similar_elements) >= 2:\n",
" man_feats.append(feature)\n",
" man_labels.append(1)\n",
" continue\n",
" else:\n",
" continue\n",
" man_lb4_feats.append(feature)\n",
"\n",
" man_rel_cnt += 1\n",
" man_feats.append(feature)\n",
" man_labels.append(1)\n",
" if man_rel_cnt > max_pos_cnt:\n",
" break\n",
" else:\n",
" continue\n",
" else:\n",
" label = row['target_label']\n",
" if label == 0:\n",
" man_unrel_cnt += 1\n",
" man_feats.append(feature)\n",
" man_labels.append(0)\n",
" man_ir_feats.append(feature)\n",
" else:\n",
" man_rel_cnt += 1\n",
" man_feats.append(feature)\n",
" man_labels.append(1)\n",
" print(\"人工打标训练样本，相关数量: \", man_rel_cnt, \"不相关数量: \", man_unrel_cnt)\n",
"\n",
" if only_manual:\n",
" return man_feats, man_labels\n",
" else:\n",
" neg_feats = []\n",
" neg_lbs = []\n",
" neg_unrel_cnt = 0\n",
" neg_rel_cnt = 0\n",
" neg_sample_cnt = man_rel_cnt / pos_ratio - man_unrel_cnt - man_rel_cnt\n",
" print(\"man_rel_cnt, pos_ratio, man_unrel_cnt, man_rel_cnt, neg_sample_cnt\")\n",
" print(man_rel_cnt, pos_ratio, man_unrel_cnt, man_rel_cnt, neg_sample_cnt)\n",
" df_neg['mm_similarity'] = df_neg['mm_similarity'].fillna(mean_value_mm)\n",
" df_neg['visual_similarity'] = df_neg['visual_similarity'].fillna(mean_value_visual)\n",
" df_neg['text_similarity'] = df_neg['text_similarity'].fillna(mean_value_text)\n",
" df_neg['mmcf_similarity'] = df_neg['mmcf_similarity'].fillna(mean_value_mmcf)\n",
" df_neg['ocr_similarity'] = df_neg['ocr_similarity'].fillna(mean_value_ocr)\n",
" df_neg['asr_similarity'] = df_neg['asr_similarity'].fillna(mean_value_asr)\n",
" df_neg['rq_similarity'] = df_neg['rq_similarity'].fillna(mean_value_rq)\n",
" print(\"before: neg_sample_cnt, neg_unrel_cnt\")\n",
" print(neg_sample_cnt, neg_unrel_cnt)\n",
" for index, row in df_neg.iterrows():\n",
" if index % 10000 == 0:\n",
" print(index)\n",
" if index > neg_sample_cnt:\n",
" break\n",
" feature = []\n",
" for feat_name in feats_names:\n",
" feature.append(row[feat_name])\n",
" # feature = [row['mm_similarity'], row['visual_similarity'], row['text_similarity'], row[\"mmcf_similarity\"], row[\"ocr_similarity\"], row[\"asr_similarity\"], row[\"rq_similarity\"], \\\n",
" # row['equal_tier'], float(row['view_general']), float(row['pub_general']), float(row['sup_view_general']), float(row['sup_pub_general']), \\\n",
" # row['view_group_id'], row['pub_group_id']]\n",
" label = 0\n",
" if label == 0:\n",
" neg_unrel_cnt += 1\n",
" neg_feats.append(feature)\n",
" neg_lbs.append(0)\n",
" elif label == 1:\n",
" neg_rel_cnt += 1\n",
" neg_feats.append(feature)\n",
" neg_lbs.append(1)\n",
" else:\n",
" continue\n",
" print(\"after: neg_sample_cnt, neg_unrel_cnt\")\n",
" print(neg_sample_cnt, neg_unrel_cnt)\n",
" rel_cnt = man_rel_cnt + neg_rel_cnt\n",
" unrel_cnt = man_unrel_cnt + neg_unrel_cnt\n",
" ral_ratio = rel_cnt * 1.0 / (rel_cnt + unrel_cnt)\n",
" features = man_feats + neg_feats\n",
" labels = man_labels + neg_lbs\n",
" print(\"相关数量: \", rel_cnt, \"不相关数量: \", unrel_cnt)\n",
" print(\"相比比例: {}\".format(ral_ratio))\n",
"\n",
" return features, labels, ral_ratio, man_lb1_feats, man_lb4_feats, man_ir_feats, neg_feats"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!hdfs dfs -get hdfs://harunasg/home/byte_data_tt_m/explicit_proactive_publish_dataset/xgboost_attribution_model_eval/embed_standard_evalset_new_feat_add_interest.csv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": "# 增加兴趣点特征\n# interest_feats = [\"keywords_semantic_cluster_tier1_has_overlap\",\"keywords_semantic_cluster_tier1_overlap_cnt\", \"keywords_semantic_cluster_tier2_has_overlap\", \"keywords_semantic_cluster_tier2_overlap_cnt\", \"keywords_semantic_cluster_dedup_tier_has_overlap\",\"keywords_semantic_cluster_dedup_tier_overlap_cnt\",\"keywords_has_overlap\",\"keywords_overlap_cnt\"]\ninterest_feats = [\"keywords_semantic_cluster_tier1_has_overlap\",\"keywords_semantic_cluster_tier1_overlap_cnt\", \"keywords_semantic_cluster_tier2_has_overlap\", \"keywords_semantic_cluster_tier2_overlap_cnt\", \"keywords_semantic_cluster_dedup_tier_has_overlap\",\"keywords_semantic_cluster_dedup_tier_overlap_cnt\",\"keywords_has_overlap\",\"keywords_overlap_cnt\"]\n\n\nori_feats_names=['mm_similarity','visual_similarity','text_similarity','mmcf_similarity','ocr_similarity','asr_similarity','rq_similarity','equal_tier','view_general','pub_general','sup_view_general','sup_pub_general','sticker_intersect','equal_music_id','hashtag_iou','view_is_pgc','pub_is_pgc','equal_group_language']\n\nfeats_names = ori_feats_names+interest_feats\nprint(feats_names)\n\nPOSITIVE_THEME_LABELS = {1, 4}\nNEGATIVE_THEME_LABELS = {3}\nprint('202507-202510 训练样本添加特征情况：')\n\n# 人工标注样本：0702以及多语种样本\nembed_feat = pd.read_csv(\"embed_standard_evalset_new_feat_add_interest.csv\")\n\nembed_feat_neg = pd.read_csv(\"embed_standard_evalset_new_feat_add_interest.csv\")\n# df_train_q3_neg_all['is_choose'] = 0\nfeatures_processed, labels_processed = prep_feats_lbs(embed_feat, None, 1063, False, {1, 4}, {3}, feats_names=feats_names, keep_all_fine=True, abstract_keep_elements=2, pos_ratio=0.2, only_manual=True,filter_null_mm=True)\n\nfeature_filename = \"train_features_embed_standard_evalset_new_feat_add_interest.json\"\nlabel_filename = \"train_labels_embed_standard_evalset_new_feat_add_interest.json\"\n# 保存到文件\nwith open(feature_filename, 'w') as f:\n json.dump(features_processed, f)\n\nwith open(label_filename, 'w') as f:\n json.dump(labels_processed, f)\n!mkdir -p /mnt/bn/bohanzhainas1/jiashuo/active_proaction/xgboost_attribution_model_eval/\n!cp $feature_filename /mnt/bn/bohanzhainas1/jiashuo/active_proaction/xgboost_attribution_model_eval/\n!cp $label_filename /mnt/bn/bohanzhainas1/jiashuo/active_proaction/xgboost_attribution_model_eval/"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 读取时使用\n",
"with open('train_features_embed_standard_evalset_new_feat_add_interest.json', 'r') as f:\n",
" features_test_stand = json.load(f)\n",
"\n",
"with open('train_labels_embed_standard_evalset_new_feat_add_interest.json', 'r') as f:\n",
" labels_test_stand = json.load(f)\n",
"\n",
"\n",
"flag = '4tier_feat'\n",
"feats_te_lst = []\n",
"features_test = features_test_stand\n",
"labels_test = labels_test_stand\n",
"\n",
"for ele in features_test:\n",
" feat = ele\n",
" feats_te_lst.append(feat)\n",
"\n",
"model, model_info = load_model_with_download(hdfs_path)\n",
"\n",
"# 测试模型\n",
"y_pred, y_pred_proba = test_model(model, feats_te_lst, labels_test_stand)\n",
"\n",
"predict_results=clf_xgb_4tier_fe.predict(feats_te_lst)\n",
"conf_mat = confusion_matrix(labels_test, predict_results)\n",
"\n",
"print(conf_mat)\n",
"calculate_classification_report(conf_mat)\n",
"\n",
"predict_scores = model.predict_proba(feats_te_lst)\n",
"precision_xgb_4tier_fe, recall_xgb_4tier_fe, auc_xgb_4tier_fe = precision_recall(labels_test, predict_scores[:, 1])\n",
"\n",
"print(auc_xgb_4tier_fe)\n",
"\n",
"for i in range(len(precision_xgb_4tier_fe)):\n",
" if abs(precision_xgb_4tier_fe[i] - 0.80) < 0.01:\n",
" print(\"xgb_4tier_fe thres: \", np.linspace(0, 1, num=100)[i], \"recall: \", recall_xgb_4tier_fe[i], \"precision: \", precision_xgb_4tier_fe[i])\n",
"\n",
"\n",
"# 查看预测结果\n",
"print(f\"\\n预测结果示例（前5个）:\")\n",
"print(f\"预测类别: {y_pred[:5]}\")\n",
"print(f\"预测概率: {y_pred_proba[:5]}\")\n",
"\n",
"# 如果有特征名称，可以查看特征重要性\n",
"if hasattr(model, 'feature_importances_'):\n",
" feature_importance = model.feature_importances_\n",
" print(f\"\\n特征重要性: {feature_importance}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"predict_results=clf_xgb_4tier_fe.predict(feats_te_lst)\n",
"conf_mat = confusion_matrix(labels_test, predict_results)\n",
"\n",
"print(conf_mat)\n",
"calculate_classification_report(conf_mat)\n",
"\n",
"predict_scores = clf_xgb_4tier_fe.predict_proba(feats_te_lst)\n",
"precision_xgb_4tier_fe, recall_xgb_4tier_fe, auc_xgb_4tier_fe = precision_recall(labels_test, predict_scores[:, 1])\n",
"\n",
"print(auc_xgb_4tier_fe)\n",
"\n",
"for i in range(len(precision_xgb_4tier_fe)):\n",
" if abs(precision_xgb_4tier_fe[i] - 0.8) < 0.01:\n",
" print(\"xgb_4tier_fe thres: \", np.linspace(0, 1, num=100)[i], \"recall: \", recall_xgb_4tier_fe[i], \"precision: \", precision_xgb_4tier_fe[i])\n"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
} |