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记录ID拍卖ID竞标者倾向竞标比率连续竞标上次竞标竞标量拍卖起拍早期竞标胜率拍卖持续时间（小时）
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" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 9 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:28:43.271751Z", "start_time": "2025-05-14T06:28:43.268190Z" } }, "cell_type": "code", "source": [ "y = data.iloc[:, -1] # 标签数据\n", "y" ], "id": "cd9249ade901dbc2", "outputs": [ { "data": { "text/plain": [ "0 0\n", "1 0\n", "2 0\n", "3 0\n", "4 0\n", " ..\n", "6316 1\n", "6317 0\n", "6318 0\n", "6319 0\n", "6320 0\n", "Name: 类别, Length: 6321, dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 10 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:28:45.332753Z", "start_time": "2025-05-14T06:28:45.330224Z" } }, "cell_type": "code", "source": "from sklearn.model_selection import train_test_split", "id": "8dc41c605cd3157e", "outputs": [], "execution_count": 11 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:28:56.522996Z", "start_time": "2025-05-14T06:28:56.503381Z" } }, "cell_type": "code", "source": "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)", "id": "9f85e76994ed4850", "outputs": [], "execution_count": 13 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:29:39.357247Z", "start_time": "2025-05-14T06:29:39.346520Z" } }, "cell_type": "code", "source": [ "print(\"训练集特征数量：\", len(X_train))\n", "print(\"测试集特征数量：\", len(X_test))\n", "print(\"训练集标签数量：\", len(y_train))\n", "print(\"测试集标签数量：\", len(y_test))" ], "id": "9e7a486068bba773", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "训练集特征数量： 5056\n", "测试集特征数量： 1265\n", "训练集标签数量： 5056\n", "测试集标签数量： 1265\n" ] } ], "execution_count": 14 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:32:48.037308Z", "start_time": "2025-05-14T06:32:48.032154Z" } }, "cell_type": "code", "source": "from sklearn.decomposition import PCA", "id": "310a87e99029912f", "outputs": [], "execution_count": 16 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:33:16.933711Z", "start_time": "2025-05-14T06:33:16.923897Z" } }, "cell_type": "code", "source": "pca = PCA(n_components=0.999)", "id": "b2a983bb0cafe05e", "outputs": [], "execution_count": 17 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:33:18.638792Z", "start_time": "2025-05-14T06:33:18.615773Z" } }, "cell_type": "code", "source": "X_train_pca = pca.fit_transform(X_train)", "id": "1e4efd6f383f884d", "outputs": [], "execution_count": 18 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:33:19.872466Z", "start_time": "2025-05-14T06:33:19.869305Z" } }, "cell_type": "code", "source": "X_test_pca = pca.transform(X_test)", "id": "3726f7c7adee3b78", "outputs": [], "execution_count": 19 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:33:29.743054Z", "start_time": "2025-05-14T06:33:29.740149Z" } }, "cell_type": "code", "source": [ "print(\"降维后训练集大小:\", X_train_pca.shape)\n", "print(\"降维后测试集大小:\", X_test_pca.shape)" ], "id": "3a492d1ae08c79d7", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "降维后训练集大小: (5056, 2)\n", "降维后测试集大小: (1265, 2)\n" ] } ], "execution_count": 20 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:34:33.158908Z", "start_time": "2025-05-14T06:34:33.156943Z" } }, "cell_type": "code", "source": [ "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import accuracy_score" ], "id": "90b993d18fc3c8fd", "outputs": [], "execution_count": 22 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:34:34.787786Z", "start_time": "2025-05-14T06:34:34.708147Z" } }, "cell_type": "code", "source": [ "model = LogisticRegression()\n", "model.fit(X_train_pca, y_train)" ], "id": "555998c0a33ff564", "outputs": [ { "data": { "text/plain": [ "LogisticRegression()" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 23 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:34:44.692219Z", "start_time": "2025-05-14T06:34:44.689245Z" } }, "cell_type": "code", "source": [ "y_pred = model.predict(X_test_pca)\n", "accuracy = accuracy_score(y_test, y_pred)" ], "id": "550e99b2717dd70a", "outputs": [], "execution_count": 24 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:34:48.357752Z", "start_time": "2025-05-14T06:34:48.355434Z" } }, "cell_type": "code", "source": "print(f\"模型在测试集上的准确率: {accuracy * 100:.2f}%\")", "id": "d371ece25527b933", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "模型在测试集上的准确率: 89.57%\n" ] } ], "execution_count": 25 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:35:35.499762Z", "start_time": "2025-05-14T06:35:34.902637Z" } }, "cell_type": "code", "source": [ "from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt" ], "id": "30954934fe9605f0", "outputs": [], "execution_count": 26 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:35:44.931054Z", "start_time": "2025-05-14T06:35:44.926161Z" } }, "cell_type": "code", "source": "precision = precision_score(y_test, y_pred)", "id": "b1e217f2b2899fd5", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Volumes/Data/Environment/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n" ] } ], "execution_count": 27 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:35:53.539971Z", "start_time": "2025-05-14T06:35:53.536968Z" } }, "cell_type": "code", "source": "recall = recall_score(y_test, y_pred)", "id": "d319c92518dfa777", "outputs": [], "execution_count": 28 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:36:02.345297Z", "start_time": "2025-05-14T06:36:02.342210Z" } }, "cell_type": "code", "source": "f1 = f1_score(y_test, y_pred)", "id": "a99ded51edcc3c2a", "outputs": [], "execution_count": 29 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:36:11.818015Z", "start_time": "2025-05-14T06:36:11.815129Z" } }, "cell_type": "code", "source": [ "print(f\"精确率: {precision * 100:.2f}%\")\n", "print(f\"召回率: {recall * 100:.2f}%\")\n", "print(f\"F1 值: {f1 * 100:.2f}%\")" ], "id": "eeeb022d06ac0cdd", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "精确率: 0.00%\n", "召回率: 0.00%\n", "F1 值: 0.00%\n" ] } ], "execution_count": 30 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:36:20.417042Z", "start_time": "2025-05-14T06:36:20.411479Z" } }, "cell_type": "code", "source": "cm = confusion_matrix(y_test, y_pred)", "id": "2e7c7fcd91a7da1b", "outputs": [], "execution_count": 31 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:37:03.532720Z", "start_time": "2025-05-14T06:37:03.505902Z" } }, "cell_type": "code", "source": [ "from matplotlib import font_manager as fm\n", "import matplotlib as mpl\n", "\n", "font_path = '/System/Library/Fonts/STHeiti Medium.ttc'\n", "my_font = fm.FontProperties(fname=font_path)\n", "mpl.rcParams['font.family'] = my_font.get_name()\n", "mpl.rcParams['axes.unicode_minus'] = False" ], "id": "d6a1fd6bb39c2e86", "outputs": [], "execution_count": 33 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:37:05.154764Z", "start_time": "2025-05-14T06:37:05.074128Z" } }, "cell_type": "code", "source": [ "plt.figure(figsize=(8, 6))\n", "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", "# annot=True 表示在热力图上显示具体数值，fmt='d' 表示以整数形式显示\n", "plt.xlabel('预测标签')\n", "plt.ylabel('真实标签')\n", "plt.title('混淆矩阵')\n", "plt.show()" ], "id": "8fd3248dde46e851", "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "execution_count": 34 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:41:54.941365Z", "start_time": "2025-05-14T06:41:54.936085Z" } }, "cell_type": "code", "source": [ "from sklearn.model_selection import GridSearchCV\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score" ], "id": "ba37982a3d38c98a", "outputs": [], "execution_count": 44 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:41:57.023385Z", "start_time": "2025-05-14T06:41:57.019735Z" } }, "cell_type": "code", "source": [ "scaler = StandardScaler()\n", "X_train_pca_scaled = scaler.fit_transform(X_train_pca)\n", "X_test_pca_scaled = scaler.transform(X_test_pca)" ], "id": "b3c2d3881eeddf31", "outputs": [], "execution_count": 45 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:41:58.292401Z", "start_time": "2025-05-14T06:41:58.289737Z" } }, "cell_type": "code", "source": [ "param_grid = {\n", " 'C': [0.001, 0.01, 0.1, 1, 10, 100],\n", " 'penalty': ['l1', 'l2']\n", "}" ], "id": "a70b8e803c245dfd", "outputs": [], "execution_count": 46 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:41:59.957375Z", "start_time": "2025-05-14T06:41:59.839417Z" } }, "cell_type": "code", "source": [ "model = LogisticRegression(solver='liblinear')\n", "grid_search = GridSearchCV(model, param_grid, cv=5, scoring='f1')\n", "grid_search.fit(X_train_pca_scaled, y_train)\n" ], "id": "acebcefaaed09b5e", "outputs": [ { "data": { "text/plain": [ "GridSearchCV(cv=5, estimator=LogisticRegression(solver='liblinear'),\n", " param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100],\n", " 'penalty': ['l1', 'l2']},\n", " scoring='f1')" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 47 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:42:09.325733Z", "start_time": "2025-05-14T06:42:09.323292Z" } }, "cell_type": "code", "source": [ "print(\"最优超参数组合:\", grid_search.best_params_)\n", "print(\"最优模型在训练集上的 F1 值:\", grid_search.best_score_)\n" ], "id": "f473f0e3fe11601b", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "最优超参数组合: {'C': 0.001, 'penalty': 'l1'}\n", "最优模型在训练集上的 F1 值: 0.0\n" ] } ], "execution_count": 48 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:42:23.992660Z", "start_time": "2025-05-14T06:42:23.989902Z" } }, "cell_type": "code", "source": [ "best_model = grid_search.best_estimator_\n", "y_pred_best = best_model.predict(X_test_pca_scaled)" ], "id": "31341e379c6efb7f", "outputs": [], "execution_count": 51 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:42:24.868183Z", "start_time": "2025-05-14T06:42:24.860990Z" } }, "cell_type": "code", "source": [ "precision_best = precision_score(y_test, y_pred_best)\n", "recall_best = recall_score(y_test, y_pred_best)\n", "f1_best = f1_score(y_test, y_pred_best)\n", "accuracy_best = accuracy_score(y_test, y_pred_best)" ], "id": "8406ad284d7569a3", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Volumes/Data/Environment/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n" ] } ], "execution_count": 52 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-14T06:42:30.031422Z", "start_time": "2025-05-14T06:42:30.028917Z" } }, "cell_type": "code", "source": [ "print(f\"最优模型在测试集上的准确率: {accuracy_best * 100:.2f}%\")\n", "print(f\"最优模型在测试集上的精确率: {precision_best * 100:.2f}%\")\n", "print(f\"最优模型在测试集上的召回率: {recall_best * 100:.2f}%\")\n", "print(f\"最优模型在测试集上的 F1 值: {f1_best * 100:.2f}%\")" ], "id": "74828d74cc923980", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "最优模型在测试集上的准确率: 89.57%\n", "最优模型在测试集上的精确率: 0.00%\n", "最优模型在测试集上的召回率: 0.00%\n", "最优模型在测试集上的 F1 值: 0.00%\n" ] } ], "execution_count": 53 }, { "metadata": {}, "cell_type": "code", "outputs": [], "execution_count": null, "source": [ "import numpy as np\n", "from sklearn.metrics import classification_report" ], "id": "57ef68abfd6fde0b" } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 5 }