{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{},"inputWidgets":{},"nuid":"0ea8b46b-839b-445b-8043-ccdf4e920ace","showTitle":false,"title":""},"id":"YLH80COBzi_F"},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"63B5exAuzq4M"},"outputs":[],"source":["from pathlib import Path\n","\n","try:\n","    from google.colab import drive\n","    drive.mount('/content/drive')\n","    workding_dir = \"/content/drive/MyDrive/logical-reasoning/\"\n","except ModuleNotFoundError:\n","    workding_dir = str(Path.cwd().parent)"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":368,"status":"ok","timestamp":1719461634865,"user":{"displayName":"Donghao Huang","userId":"00463591218503521679"},"user_tz":-480},"id":"zFulf0bg0H-9","outputId":"debdd535-c828-40b9-efc0-8a180e5830dd"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /Users/inflaton/code/engd/projects/logical-reasoning\n"]}],"source":["import os\n","import sys\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":4,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":589,"status":"ok","timestamp":1719462011879,"user":{"displayName":"Donghao Huang","userId":"00463591218503521679"},"user_tz":-480},"id":"DIUiweYYzi_I","outputId":"e16e9247-9077-4b0c-f8ea-17059f05a1c4"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: /Users/inflaton/code/engd/projects/logical-reasoning/.env\n"]},{"data":{"text/plain":["True"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["from dotenv import find_dotenv, load_dotenv\n","\n","found_dotenv = find_dotenv(\".env\")\n","\n","if len(found_dotenv) == 0:\n","    found_dotenv = find_dotenv(\".env.example\")\n","print(f\"loading env vars from: {found_dotenv}\")\n","load_dotenv(found_dotenv, override=True)"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"W2QyVreqhOGM","outputId":"68b9590e-1ac6-4c6f-e0c4-e273ec816419"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>label</th>\n","      <th>title</th>\n","      <th>puzzle</th>\n","      <th>truth</th>\n","      <th>internlm/internlm2_5-7b-chat-1m</th>\n","      <th>internlm/internlm2_5-7b-chat-1m_checkpoint-175</th>\n","      <th>internlm/internlm2_5-7b-chat-1m_checkpoint-350</th>\n","      <th>internlm/internlm2_5-7b-chat-1m_checkpoint-525</th>\n","      <th>internlm/internlm2_5-7b-chat-1m_checkpoint-700</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>甄加索是自杀吗</td>\n","      <td>不是</td>\n","      <td>海岸之谜</td>\n","      <td>在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...</td>\n","      <td>甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>甄加索有身体上的疾病吗</td>\n","      <td>是</td>\n","      <td>海岸之谜</td>\n","      <td>在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...</td>\n","      <td>甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>画作是甄的</td>\n","      <td>是</td>\n","      <td>海岸之谜</td>\n","      <td>在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...</td>\n","      <td>甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...</td>\n","      <td>不是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>甄有心脏病吗</td>\n","      <td>是</td>\n","      <td>海岸之谜</td>\n","      <td>在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...</td>\n","      <td>甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>车轮是凶手留下的</td>\n","      <td>不是</td>\n","      <td>海岸之谜</td>\n","      <td>在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...</td>\n","      <td>甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2995</th>\n","      <td>哭泣者必须在晚上祭奠吗</td>\n","      <td>是</td>\n","      <td>甄庄哭声</td>\n","      <td>在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...</td>\n","      <td>原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...</td>\n","      <td>是</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","      <td>不重要</td>\n","      <td>不重要</td>\n","    </tr>\n","    <tr>\n","      <th>2996</th>\n","      <td>尸体在湖里吗</td>\n","      <td>不是</td>\n","      <td>甄庄哭声</td>\n","      <td>在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...</td>\n","      <td>原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","    </tr>\n","    <tr>\n","      <th>2997</th>\n","      <td>哭泣者和死者有特殊关系吗</td>\n","      <td>是</td>\n","      <td>甄庄哭声</td>\n","      <td>在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...</td>\n","      <td>原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","    </tr>\n","    <tr>\n","      <th>2998</th>\n","      <td>是帽子的主人去世了吗</td>\n","      <td>不是</td>\n","      <td>甄庄哭声</td>\n","      <td>在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...</td>\n","      <td>原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","      <td>是</td>\n","    </tr>\n","    <tr>\n","      <th>2999</th>\n","      <td>死者受伤了吗</td>\n","      <td>不是</td>\n","      <td>甄庄哭声</td>\n","      <td>在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...</td>\n","      <td>原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...</td>\n","      <td>不是</td>\n","      <td>不是</td>\n","      <td>不重要</td>\n","      <td>不重要</td>\n","      <td>不重要</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>3000 rows × 10 columns</p>\n","</div>"],"text/plain":["              text label title  \\\n","0          甄加索是自杀吗    不是  海岸之谜   \n","1      甄加索有身体上的疾病吗     是  海岸之谜   \n","2            画作是甄的     是  海岸之谜   \n","3           甄有心脏病吗     是  海岸之谜   \n","4         车轮是凶手留下的    不是  海岸之谜   \n","...            ...   ...   ...   \n","2995   哭泣者必须在晚上祭奠吗     是  甄庄哭声   \n","2996        尸体在湖里吗    不是  甄庄哭声   \n","2997  哭泣者和死者有特殊关系吗     是  甄庄哭声   \n","2998    是帽子的主人去世了吗    不是  甄庄哭声   \n","2999        死者受伤了吗    不是  甄庄哭声   \n","\n","                                                 puzzle  \\\n","0     在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...   \n","1     在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...   \n","2     在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...   \n","3     在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...   \n","4     在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任...   \n","...                                                 ...   \n","2995  在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...   \n","2996  在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...   \n","2997  在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...   \n","2998  在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...   \n","2999  在一个安静的夜晚，小村庄的湖边突然传来了阵阵哭泣声。第二天早晨，村长甄锐发现湖边的石头上放着...   \n","\n","                                                  truth  \\\n","0     甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...   \n","1     甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...   \n","2     甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...   \n","3     甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...   \n","4     甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在...   \n","...                                                 ...   \n","2995  原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...   \n","2996  原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...   \n","2997  原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...   \n","2998  原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...   \n","2999  原来，这顶破旧的帽子属于一个小男孩，他小时候与爷爷在湖边生活。爷爷教他钓鱼、游泳，还告诉他湖...   \n","\n","     internlm/internlm2_5-7b-chat-1m  \\\n","0                                 不是   \n","1                                  是   \n","2                                 不是   \n","3                                  是   \n","4                                 不是   \n","...                              ...   \n","2995                               是   \n","2996                              不是   \n","2997                               是   \n","2998                               是   \n","2999                              不是   \n","\n","     internlm/internlm2_5-7b-chat-1m_checkpoint-175  \\\n","0                                                不是   \n","1                                                 是   \n","2                                                 是   \n","3                                                 是   \n","4                                                不是   \n","...                                             ...   \n","2995                                             不是   \n","2996                                             不是   \n","2997                                              是   \n","2998                                              是   \n","2999                                             不是   \n","\n","     internlm/internlm2_5-7b-chat-1m_checkpoint-350  \\\n","0                                                不是   \n","1                                                 是   \n","2                                                 是   \n","3                                                 是   \n","4                                                不是   \n","...                                             ...   \n","2995                                             不是   \n","2996                                             不是   \n","2997                                              是   \n","2998                                              是   \n","2999                                            不重要   \n","\n","     internlm/internlm2_5-7b-chat-1m_checkpoint-525  \\\n","0                                                不是   \n","1                                                 是   \n","2                                                 是   \n","3                                                 是   \n","4                                                不是   \n","...                                             ...   \n","2995                                            不重要   \n","2996                                             不是   \n","2997                                              是   \n","2998                                              是   \n","2999                                            不重要   \n","\n","     internlm/internlm2_5-7b-chat-1m_checkpoint-700  \n","0                                                不是  \n","1                                                 是  \n","2                                                 是  \n","3                                                 是  \n","4                                                不是  \n","...                                             ...  \n","2995                                            不重要  \n","2996                                             不是  \n","2997                                              是  \n","2998                                              是  \n","2999                                            不重要  \n","\n","[3000 rows x 10 columns]"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","df = pd.read_csv(\"results/mgtv-results_p2_full_r2.csv\")\n","df"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[],"source":["import matplotlib.pyplot as plt\n","from matplotlib import rcParams\n","\n","def plot_value_counts(df, column):\n","    font_family = rcParams[\"font.family\"]\n","    # Set the font to SimHei to support Chinese characters\n","    rcParams[\"font.family\"] = \"STHeiti\"\n","    rcParams[\"axes.unicode_minus\"] = False  # This is to support the minus sign in Chinese.\n","\n","    plt.figure(figsize=(12, 6))\n","    df[column].value_counts().plot(kind=\"bar\")\n","    # add values on top of bars\n","    for i, v in enumerate(df[column].value_counts()):\n","        plt.text(i, v + 0.1, str(v), ha=\"center\")\n","    plt.show()\n","    \n","    rcParams[\"font.family\"] = font_family\n"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"text/plain":["['text',\n"," 'label',\n"," 'title',\n"," 'puzzle',\n"," 'truth',\n"," 'internlm/internlm2_5-7b-chat-1m',\n"," 'internlm/internlm2_5-7b-chat-1m_checkpoint-175',\n"," 'internlm/internlm2_5-7b-chat-1m_checkpoint-350',\n"," 'internlm/internlm2_5-7b-chat-1m_checkpoint-525',\n"," 'internlm/internlm2_5-7b-chat-1m_checkpoint-700']"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["df.columns.to_list()"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["********** internlm/internlm2_5-7b-chat-1m **********\n","internlm/internlm2_5-7b-chat-1m\n","不是     1691\n","是      1264\n","不重要      45\n","Name: count, dtype: int64\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["********** internlm/internlm2_5-7b-chat-1m_checkpoint-175 **********\n","internlm/internlm2_5-7b-chat-1m_checkpoint-175\n","不是      1596\n","是       1197\n","不重要      162\n","回答正确      32\n","问法错误      13\n","Name: count, dtype: int64\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["********** internlm/internlm2_5-7b-chat-1m_checkpoint-350 **********\n","internlm/internlm2_5-7b-chat-1m_checkpoint-350\n","是       1328\n","不是      1277\n","不重要      308\n","问法错误      57\n","回答正确      30\n","Name: count, dtype: int64\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["********** internlm/internlm2_5-7b-chat-1m_checkpoint-525 **********\n","internlm/internlm2_5-7b-chat-1m_checkpoint-525\n","不是      1328\n","是       1161\n","不重要      414\n","问法错误      60\n","回答正确      37\n","Name: count, dtype: int64\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["********** internlm/internlm2_5-7b-chat-1m_checkpoint-700 **********\n","internlm/internlm2_5-7b-chat-1m_checkpoint-700\n","不是      1308\n","是       1084\n","不重要      510\n","问法错误      61\n","回答正确      37\n","Name: count, dtype: int64\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["for col in df.columns[5:]:\n","    print(\"*\" * 10, col, \"*\" * 10)\n","    print(df[col].value_counts())\n","    plot_value_counts(df, col)"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n","\n","\n","def calc_metrics_for_col(df, col):\n","    y_true = df[\"label\"]\n","    y_pred = df[col]\n","\n","    accuracy = accuracy_score(y_true, y_pred)\n","    precision = precision_score(y_true, y_pred, average=\"weighted\", labels=np.unique(y_pred))\n","    recall = recall_score(y_true, y_pred, average=\"weighted\", labels=np.unique(y_pred))\n","    f1 = f1_score(y_true, y_pred, average=\"weighted\", labels=np.unique(y_pred))\n","\n","    return accuracy, float(precision), float(recall), float(f1)"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/var/folders/7x/56svhln929zdh2xhr3mwqg4r0000gn/T/ipykernel_22950/961288552.py:9: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n","  perf_df = pd.concat([perf_df, pd.DataFrame([new_model_metrics])], ignore_index=True)\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>epoch</th>\n","      <th>model</th>\n","      <th>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>internlm/internlm2_5-7b-chat-1m</td>\n","      <td>0.766000</td>\n","      <td>0.747969</td>\n","      <td>0.787526</td>\n","      <td>0.764922</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-175</td>\n","      <td>0.812000</td>\n","      <td>0.812286</td>\n","      <td>0.812000</td>\n","      <td>0.810234</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-350</td>\n","      <td>0.765333</td>\n","      <td>0.806889</td>\n","      <td>0.765333</td>\n","      <td>0.779998</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-525</td>\n","      <td>0.747667</td>\n","      <td>0.812033</td>\n","      <td>0.747667</td>\n","      <td>0.773122</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_checkpoint-700</td>\n","      <td>0.717000</td>\n","      <td>0.804642</td>\n","      <td>0.717000</td>\n","      <td>0.751034</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  epoch                                           model  accuracy  precision  \\\n","0     0                 internlm/internlm2_5-7b-chat-1m  0.766000   0.747969   \n","1     1  internlm/internlm2_5-7b-chat-1m_checkpoint-175  0.812000   0.812286   \n","2     2  internlm/internlm2_5-7b-chat-1m_checkpoint-350  0.765333   0.806889   \n","3     3  internlm/internlm2_5-7b-chat-1m_checkpoint-525  0.747667   0.812033   \n","4     4  internlm/internlm2_5-7b-chat-1m_checkpoint-700  0.717000   0.804642   \n","\n","     recall        f1  \n","0  0.787526  0.764922  \n","1  0.812000  0.810234  \n","2  0.765333  0.779998  \n","3  0.747667  0.773122  \n","4  0.717000  0.751034  "]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","perf_df = pd.DataFrame(columns=[\"epoch\", \"model\", \"accuracy\", \"precision\", \"recall\", \"f1\"])\n","for i, col in enumerate(df.columns[5:]):\n","    accuracy, precision, recall, f1 = calc_metrics_for_col(df, col)\n","    new_model_metrics = {\"epoch\": i, \"model\": col, \"accuracy\": accuracy, \"precision\": precision, \"recall\": recall, \"f1\": f1}\n","\n","    # Convert the dictionary to a DataFrame and concatenate it with the existing DataFrame\n","    perf_df = pd.concat([perf_df, pd.DataFrame([new_model_metrics])], ignore_index=True)\n","\n","perf_df"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1200x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot metrics for each model\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(1, 1, figsize=(12, 5))\n","\n","perf_df.plot(x=\"epoch\", y=[\"accuracy\", \"precision\", \"recall\", \"f1\"], kind=\"bar\", ax=ax)\n","\n","# add values on top of bars\n","for p in ax.patches:\n","    ax.annotate(\n","        f\"{p.get_height():.2f}\",\n","        (p.get_x() + p.get_width() / 2, p.get_height()),\n","        ha=\"center\",\n","        va=\"bottom\",\n","        fontsize=10,\n","    )\n","\n","# add title and labels\n","# ax.set_title(\"Metrics for different settings\")\n","# ax.set_ylabel(\"Value\")\n","ax.set_xlabel(\"Epoch (0: base model, 1-4: fine-tuned models)\")\n","# rotate x labels\n","plt.xticks(rotation=0)\n","\n","# set legend at the right to avoid overlapping with bars\n","plt.legend(loc=\"center left\", bbox_to_anchor=(1.0, 0.5))\n","# plt.tight_layout()\n","\n","plt.show()"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[],"source":["perf_df.to_csv(\"results/mgtv-results_p2_r2_full_metrics.csv\", index=False)"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"pythonIndentUnit":4},"notebookName":"07_MAC_+_Qwen2-7B-Instructi_Unsloth_train","widgets":{}},"colab":{"gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.9"}},"nbformat":4,"nbformat_minor":0}