From 76ebd72fb32a5dbdd78693f5c10b4c7d887b63a1 Mon Sep 17 00:00:00 2001
From: liaozhaorun <1300336796@qq.com>
Date: Mon, 2 Jun 2025 00:13:32 +0800
Subject: [PATCH] =?UTF-8?q?=E6=96=B0=E7=8E=AF=E5=A2=832?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 main/train/Classify2.ipynb                    |  456 +++-
 .../catboost_info/catboost_training.json      | 2203 ++++++++--------
 .../catboost_info/learn/events.out.tfevents   |  Bin 58005 -> 62900 bytes
 main/train/catboost_info/learn_error.tsv      | 1881 +++++++-------
 .../catboost_info/test/events.out.tfevents    |  Bin 58005 -> 62900 bytes
 main/train/catboost_info/test_error.tsv       | 1855 ++++++-------
 main/train/catboost_info/time_left.tsv        | 2203 ++++++++--------
 main/train/predictions_test.tsv               | 2304 ++++++++---------
 8 files changed, 5770 insertions(+), 5132 deletions(-)

diff --git a/main/train/Classify2.ipynb b/main/train/Classify2.ipynb
index c6faca4..de4d694 100644
--- a/main/train/Classify2.ipynb
+++ b/main/train/Classify2.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "79a7758178bafdd3",
    "metadata": {
     "ExecuteTime": {
@@ -18,8 +18,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n",
       "/mnt/d/PyProject/NewStock/main/train\n"
      ]
     }
@@ -46,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "4a481c60",
    "metadata": {},
    "outputs": [],
@@ -58,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "a79cafb06a7e0e43",
    "metadata": {
     "ExecuteTime": {
@@ -158,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "cac01788dac10678",
    "metadata": {
     "ExecuteTime": {
@@ -226,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "c4e9e1d31da6dba6",
    "metadata": {
     "ExecuteTime": {
@@ -326,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "a735bc02ceb4d872",
    "metadata": {
     "ExecuteTime": {
@@ -342,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "id": "53f86ddc0677a6d7",
    "metadata": {
     "ExecuteTime": {
@@ -409,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "id": "dbe2fd8021b9417f",
    "metadata": {
     "ExecuteTime": {
@@ -437,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "id": "85c3e3d0235ffffa",
    "metadata": {
     "ExecuteTime": {
@@ -465,7 +463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "id": "92d84ce15a562ec6",
    "metadata": {
     "ExecuteTime": {
@@ -746,7 +744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "id": "b87b938028afa206",
    "metadata": {
     "ExecuteTime": {
@@ -784,7 +782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "id": "f4f16d63ad18d1bc",
    "metadata": {
     "ExecuteTime": {
@@ -1010,7 +1008,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "id": "40e6b68a91b30c79",
    "metadata": {
     "ExecuteTime": {
@@ -1330,7 +1328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "id": "47c12bb34062ae7a",
    "metadata": {
     "ExecuteTime": {
@@ -1364,7 +1362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 15,
    "id": "29221dde",
    "metadata": {},
    "outputs": [
@@ -1407,7 +1405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 16,
    "id": "03ee5daf",
    "metadata": {},
    "outputs": [],
@@ -1420,7 +1418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 17,
    "id": "b76ea08a",
    "metadata": {},
    "outputs": [
@@ -1441,7 +1439,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 137/137 [00:16<00:00,  8.35it/s]\n"
+      "MAD Filtering: 100%|██████████| 137/137 [00:14<00:00,  9.48it/s]\n"
      ]
     },
     {
@@ -1456,7 +1454,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MAD Filtering: 100%|██████████| 137/137 [00:11<00:00, 12.05it/s]\n"
+      "MAD Filtering: 100%|██████████| 137/137 [00:15<00:00,  8.83it/s]\n"
      ]
     },
     {
@@ -1638,7 +1636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 18,
    "id": "3ff2d1c5",
    "metadata": {},
    "outputs": [],
@@ -1779,7 +1777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 19,
    "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81",
    "metadata": {
     "ExecuteTime": {
@@ -1813,7 +1811,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f2be3dc13c844232880f85edebee22c9",
+       "model_id": "306af53af6f24cce84719f44ae19a9b0",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1828,20 +1826,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0:\tlearn: 0.6889525\ttest: 0.6893155\tbest: 0.6893155 (0)\ttotal: 30.4ms\tremaining: 45.6s\n",
-      "100:\tlearn: 0.5065717\ttest: 0.5493420\tbest: 0.5493420 (100)\ttotal: 12.6s\tremaining: 2m 54s\n",
-      "200:\tlearn: 0.4728006\ttest: 0.5277064\tbest: 0.5277064 (200)\ttotal: 25.2s\tremaining: 2m 42s\n",
-      "300:\tlearn: 0.4572238\ttest: 0.5226125\tbest: 0.5225487 (298)\ttotal: 40s\tremaining: 2m 39s\n",
-      "400:\tlearn: 0.4479278\ttest: 0.5208197\tbest: 0.5208197 (400)\ttotal: 54.5s\tremaining: 2m 29s\n",
-      "500:\tlearn: 0.4413356\ttest: 0.5201745\tbest: 0.5201414 (499)\ttotal: 1m 8s\tremaining: 2m 16s\n",
-      "600:\tlearn: 0.4356760\ttest: 0.5205238\tbest: 0.5201414 (499)\ttotal: 1m 22s\tremaining: 2m 2s\n",
-      "700:\tlearn: 0.4293049\ttest: 0.5203200\tbest: 0.5201414 (499)\ttotal: 1m 35s\tremaining: 1m 48s\n",
-      "800:\tlearn: 0.4227554\ttest: 0.5200567\tbest: 0.5198929 (756)\ttotal: 1m 48s\tremaining: 1m 34s\n",
-      "900:\tlearn: 0.4162998\ttest: 0.5200167\tbest: 0.5198929 (756)\ttotal: 2m 3s\tremaining: 1m 21s\n",
-      "1000:\tlearn: 0.4097120\ttest: 0.5200234\tbest: 0.5198929 (756)\ttotal: 2m 17s\tremaining: 1m 8s\n",
-      "bestTest = 0.5198929335\n",
-      "bestIteration = 756\n",
-      "Shrink model to first 757 iterations.\n"
+      "0:\tlearn: 0.6889525\ttest: 0.6893155\tbest: 0.6893155 (0)\ttotal: 1.83s\tremaining: 45m 41s\n",
+      "100:\tlearn: 0.5065717\ttest: 0.5493420\tbest: 0.5493420 (100)\ttotal: 14.7s\tremaining: 3m 23s\n",
+      "200:\tlearn: 0.4728006\ttest: 0.5277064\tbest: 0.5277064 (200)\ttotal: 27.7s\tremaining: 2m 59s\n",
+      "300:\tlearn: 0.4572238\ttest: 0.5226125\tbest: 0.5225487 (298)\ttotal: 43s\tremaining: 2m 51s\n",
+      "400:\tlearn: 0.4478623\ttest: 0.5208601\tbest: 0.5208601 (400)\ttotal: 57.6s\tremaining: 2m 37s\n",
+      "500:\tlearn: 0.4408993\ttest: 0.5198696\tbest: 0.5198644 (463)\ttotal: 1m 12s\tremaining: 2m 23s\n",
+      "600:\tlearn: 0.4349650\ttest: 0.5201926\tbest: 0.5198644 (463)\ttotal: 1m 26s\tremaining: 2m 9s\n",
+      "700:\tlearn: 0.4287194\ttest: 0.5200216\tbest: 0.5198644 (463)\ttotal: 1m 40s\tremaining: 1m 54s\n",
+      "800:\tlearn: 0.4220811\ttest: 0.5194681\tbest: 0.5194258 (799)\ttotal: 1m 54s\tremaining: 1m 40s\n",
+      "900:\tlearn: 0.4156361\ttest: 0.5195695\tbest: 0.5193854 (845)\ttotal: 2m 9s\tremaining: 1m 26s\n",
+      "1000:\tlearn: 0.4083819\ttest: 0.5195108\tbest: 0.5193854 (845)\ttotal: 2m 25s\tremaining: 1m 12s\n",
+      "1100:\tlearn: 0.4002510\ttest: 0.5196046\tbest: 0.5193854 (845)\ttotal: 2m 41s\tremaining: 58.5s\n",
+      "bestTest = 0.5193854246\n",
+      "bestIteration = 845\n",
+      "Shrink model to first 846 iterations.\n"
      ]
     }
    ],
@@ -1863,7 +1862,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 20,
    "id": "5d1522a7538db91b",
    "metadata": {
     "ExecuteTime": {
@@ -1901,7 +1900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 21,
    "id": "09b1799e",
    "metadata": {},
    "outputs": [
@@ -1923,7 +1922,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 22,
    "id": "e53b209a",
    "metadata": {},
    "outputs": [
@@ -1956,7 +1955,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 23,
    "id": "364e821a",
    "metadata": {},
    "outputs": [],
@@ -2040,7 +2039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 24,
    "id": "1f6e6336",
    "metadata": {},
    "outputs": [
@@ -2048,7 +2047,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6e+04-9e+04\n",
+      "6e+04-9e+04"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
       "9e+04-1e+05\n",
       "1e+05-1e+05\n",
       "1e+05-1e+05\n",
@@ -2059,21 +2065,21 @@
       "2e+05-3e+05\n",
       "3e+05-3e+05\n",
       "二分类评估指标:\n",
-      "accuracy: 0.6521\n",
-      "precision: 0.4678\n",
-      "recall: 0.2293\n",
-      "f1: 0.3077\n",
-      "roc_auc: 0.6188\n",
-      "fpr: (array of length 7419)\n",
-      "tpr: (array of length 7419)\n",
-      "thresholds: (array of length 7419)\n",
-      "score_return_correlation: -0.0420\n",
-      "mv_roc_auc: {'6e+04-9e+04': np.float64(0.5368389780154487), '9e+04-1e+05': np.float64(0.5842281184370101), '1e+05-1e+05': np.float64(0.5813697835731819), '1e+05-2e+05': np.float64(0.5755756816649003), '2e+05-2e+05': np.float64(0.6025259170254149), '2e+05-3e+05': np.float64(0.6160037489958047), '3e+05-3e+05': np.float64(0.6159040696993592)}\n"
+      "accuracy: 0.6525\n",
+      "precision: 0.4625\n",
+      "recall: 0.2437\n",
+      "f1: 0.3192\n",
+      "roc_auc: 0.6190\n",
+      "fpr: (array of length 7459)\n",
+      "tpr: (array of length 7459)\n",
+      "thresholds: (array of length 7459)\n",
+      "score_return_correlation: -0.0381\n",
+      "mv_roc_auc: {'6e+04-9e+04': np.float64(0.5297001153402537), '9e+04-1e+05': np.float64(0.5480807161280534), '1e+05-1e+05': np.float64(0.5803400535039577), '1e+05-2e+05': np.float64(0.5801592577513709), '2e+05-2e+05': np.float64(0.6041226723862076), '2e+05-3e+05': np.float64(0.6108816749042437), '3e+05-3e+05': np.float64(0.6029078699377564)}\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -2109,7 +2115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 25,
    "id": "7e9023cc",
    "metadata": {},
    "outputs": [],
@@ -2309,20 +2315,318 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 27,
    "id": "a0000d75",
    "metadata": {},
    "outputs": [
     {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'seaborn'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[31]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mseaborn\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msns\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mstats\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m spearmanr\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtqdm\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m tqdm \u001b[38;5;66;03m# 用于显示进度条 (可选)\u001b[39;00m\n",
-      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'seaborn'"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n"
      ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "开始分析 'score' 在 'circ_mv' 和 'future_return' 下的表现...\n",
+      "准备数据，处理 NaN 值...\n",
+      "原始数据 17280 行，移除 NaN 后剩余 16971 行用于分析。\n",
+      "对 'circ_mv' 和 'future_return' 进行 100 分位数分箱...\n",
+      "按二维分箱分组计算 Spearman Rank IC...\n",
+      "整理结果用于绘图...\n",
+      "circ_mv_bin        0   1   2   3   4   5   6   7   8   9   ...  90  91  92  \\\n",
+      "future_return_bin                                          ...               \n",
+      "0                 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ... NaN NaN NaN   \n",
+      "1                 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ... NaN NaN NaN   \n",
+      "2                 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ... NaN NaN NaN   \n",
+      "3                 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ... NaN NaN NaN   \n",
+      "4                 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ... NaN NaN NaN   \n",
+      "...                ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ...  ..  ..  ..   \n",
+      "95                NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ... NaN NaN NaN   \n",
+      "96                NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ... NaN NaN NaN   \n",
+      "97                NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ... NaN NaN NaN   \n",
+      "98                NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ... NaN NaN NaN   \n",
+      "99                NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ... NaN NaN NaN   \n",
+      "\n",
+      "circ_mv_bin        93  94  95  96  97  98  99  \n",
+      "future_return_bin                              \n",
+      "0                 NaN NaN NaN NaN NaN NaN NaN  \n",
+      "1                 NaN NaN NaN NaN NaN NaN NaN  \n",
+      "2                 NaN NaN NaN NaN NaN NaN NaN  \n",
+      "3                 NaN NaN NaN NaN NaN NaN NaN  \n",
+      "4                 NaN NaN NaN NaN NaN NaN NaN  \n",
+      "...                ..  ..  ..  ..  ..  ..  ..  \n",
+      "95                NaN NaN NaN NaN NaN NaN NaN  \n",
+      "96                NaN NaN NaN NaN NaN NaN NaN  \n",
+      "97                NaN NaN NaN NaN NaN NaN NaN  \n",
+      "98                NaN NaN NaN NaN NaN NaN NaN  \n",
+      "99                NaN NaN NaN NaN NaN NaN NaN  \n",
+      "\n",
+      "[100 rows x 100 columns]\n",
+      "生成热力图...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
+      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "分析完成。\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1600x1200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -2510,28 +2814,6 @@
     "except Exception as e:\n",
     "     print(f\"发生未知错误: {e}\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a436dba4",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'CatBoostClassifier' object has no attribute 'feature_importance'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[29], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(model\u001b[38;5;241m.\u001b[39mfeature_importance)\n",
-      "\u001b[1;31mAttributeError\u001b[0m: 'CatBoostClassifier' object has no attribute 'feature_importance'"
-     ]
-    }
-   ],
-   "source": [
-    "print(model.feature_importance())"
-   ]
   }
  ],
  "metadata": {
diff --git a/main/train/catboost_info/catboost_training.json b/main/train/catboost_info/catboost_training.json
index 07b806d..8d3d694 100644
--- a/main/train/catboost_info/catboost_training.json
+++ b/main/train/catboost_info/catboost_training.json
@@ -1,1061 +1,1150 @@
 {
 "meta":{"test_sets":["test"],"test_metrics":[{"best_value":"Min","name":"Logloss"}],"learn_metrics":[{"best_value":"Min","name":"Logloss"}],"launch_mode":"Train","parameters":"","iteration_count":1500,"learn_sets":["learn"],"name":"experiment"},
 "iterations":[
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-{"learn":[0.6848110763],"iteration":1,"passed_time":0.09825480085,"remaining_time":73.59284584,"test":[0.6859286163]},
-{"learn":[0.6805827281],"iteration":2,"passed_time":0.2496800034,"remaining_time":124.5903217,"test":[0.6833587866]},
-{"learn":[0.6766081895],"iteration":3,"passed_time":0.4036001307,"remaining_time":150.9464489,"test":[0.6798692394]},
-{"learn":[0.672721494],"iteration":4,"passed_time":0.5591500814,"remaining_time":167.1858744,"test":[0.6762909369]},
-{"learn":[0.6689282406],"iteration":5,"passed_time":0.7164798497,"remaining_time":178.4034826,"test":[0.6729278179]},
-{"learn":[0.6650054148],"iteration":6,"passed_time":0.8717593945,"remaining_time":185.9338251,"test":[0.6696961556]},
-{"learn":[0.6612558452],"iteration":7,"passed_time":1.024831013,"remaining_time":191.1309839,"test":[0.6677009598]},
-{"learn":[0.6575285136],"iteration":8,"passed_time":1.17549018,"remaining_time":194.7395399,"test":[0.6650219023]},
-{"learn":[0.6540385641],"iteration":9,"passed_time":1.298246832,"remaining_time":193.438778,"test":[0.6619151117]},
-{"learn":[0.6505256898],"iteration":10,"passed_time":1.451879986,"remaining_time":196.5317545,"test":[0.6588958792]},
-{"learn":[0.647034948],"iteration":11,"passed_time":1.606205217,"remaining_time":199.1694469,"test":[0.6557885698]},
-{"learn":[0.6439326591],"iteration":12,"passed_time":1.631534724,"remaining_time":186.6224719,"test":[0.6530829336]},
-{"learn":[0.6406527299],"iteration":13,"passed_time":1.784599862,"remaining_time":189.4225283,"test":[0.6510523794]},
-{"learn":[0.6374656089],"iteration":14,"passed_time":1.815908142,"remaining_time":179.7749061,"test":[0.6484732912]},
-{"learn":[0.6343184742],"iteration":15,"passed_time":1.845212369,"remaining_time":171.1434472,"test":[0.6465353234]},
-{"learn":[0.6311513199],"iteration":16,"passed_time":1.999531719,"remaining_time":174.4297376,"test":[0.6437495919]},
-{"learn":[0.6282585231],"iteration":17,"passed_time":2.035239268,"remaining_time":167.5680331,"test":[0.6410882136]},
-{"learn":[0.6252338298],"iteration":18,"passed_time":2.187315665,"remaining_time":170.4955,"test":[0.6384847016]},
-{"learn":[0.6224274498],"iteration":19,"passed_time":2.232027111,"remaining_time":165.1700062,"test":[0.6360120078]},
-{"learn":[0.6195728433],"iteration":20,"passed_time":2.39243642,"remaining_time":168.4958793,"test":[0.6341912894]},
-{"learn":[0.6167645089],"iteration":21,"passed_time":2.55510404,"remaining_time":171.6565351,"test":[0.6317692787]},
-{"learn":[0.6139455572],"iteration":22,"passed_time":2.715518467,"remaining_time":174.383512,"test":[0.6294376992]},
-{"learn":[0.6112551743],"iteration":23,"passed_time":2.842342268,"remaining_time":174.8040495,"test":[0.6272684252]},
-{"learn":[0.6086323507],"iteration":24,"passed_time":3.004379425,"remaining_time":177.2583861,"test":[0.6255514275]},
-{"learn":[0.6060992189],"iteration":25,"passed_time":3.056784136,"remaining_time":173.2961468,"test":[0.623264944]},
-{"learn":[0.6035314359],"iteration":26,"passed_time":3.173966042,"remaining_time":173.1574807,"test":[0.6216322317]},
-{"learn":[0.601141663],"iteration":27,"passed_time":3.32829889,"remaining_time":174.9734274,"test":[0.6194030959]},
-{"learn":[0.598654275],"iteration":28,"passed_time":3.489935711,"remaining_time":177.0239804,"test":[0.6179092632]},
-{"learn":[0.596366924],"iteration":29,"passed_time":3.562723837,"remaining_time":174.573468,"test":[0.6158866108]},
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diff --git a/main/train/catboost_info/learn_error.tsv b/main/train/catboost_info/learn_error.tsv
index f8e7fd2..15abd72 100644
--- a/main/train/catboost_info/learn_error.tsv
+++ b/main/train/catboost_info/learn_error.tsv
@@ -1,295 +1,295 @@
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diff --git a/main/train/catboost_info/test/events.out.tfevents b/main/train/catboost_info/test/events.out.tfevents
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diff --git a/main/train/catboost_info/test_error.tsv b/main/train/catboost_info/test_error.tsv
index 87776bd..eaca640 100644
--- a/main/train/catboost_info/test_error.tsv
+++ b/main/train/catboost_info/test_error.tsv
@@ -1,1058 +1,1147 @@
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diff --git a/main/train/catboost_info/time_left.tsv b/main/train/catboost_info/time_left.tsv
index bbd650a..5b04905 100644
--- a/main/train/catboost_info/time_left.tsv
+++ b/main/train/catboost_info/time_left.tsv
@@ -1,1058 +1,1147 @@
 iter	Passed	Remaining
-0	30	45617
-1	98	73592
-2	249	124590
-3	403	150946
-4	559	167185
-5	716	178403
-6	871	185933
-7	1024	191130
-8	1175	194739
-9	1298	193438
-10	1451	196531
-11	1606	199169
-12	1631	186622
-13	1784	189422
-14	1815	179774
-15	1845	171143
-16	1999	174429
-17	2035	167568
-18	2187	170495
-19	2232	165170
-20	2392	168495
-21	2555	171656
-22	2715	174383
-23	2842	174804
-24	3004	177258
-25	3056	173296
-26	3173	173157
-27	3328	174973
-28	3489	177023
-29	3562	174573
-30	3714	176029
-31	3832	175830
-32	3985	177167
-33	4144	178694
-34	4300	179996
-35	4455	181191
-36	4575	180903
-37	4725	181791
-38	4748	177901
-39	4906	179099
-40	5066	180282
-41	5098	176985
-42	5254	178046
-43	5410	179052
-44	5485	177364
-45	5643	178389
-46	5799	179298
-47	5961	180322
-48	5993	177482
-49	6117	177409
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diff --git a/main/train/predictions_test.tsv b/main/train/predictions_test.tsv
index bd72268..9641e06 100644
--- a/main/train/predictions_test.tsv
+++ b/main/train/predictions_test.tsv
@@ -1,1153 +1,1153 @@
 trade_date,score,ts_code
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-2023-01-09,0.41491641622382675,002828.SZ
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