{"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":{},"outputs":[],"source":["P1 = \"\"\"你是一个逻辑游戏的主持人。游戏规则如下:\n","\n","1. 参与者会得到一个谜题。\n","2. 参与者可以通过提问来获取线索,尝试解开谜题。\n","3. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。\n","4. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n","5. 参与者需要根据回答来推理,并最终找出谜题的正确答案。\n","\n","请严格按照这些规则回答参与者提出的问题。\n","\n","谜题: {}\n","\n","实际情况: {}\n","\n","参与者提出的问题: {}\n","\"\"\""]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[],"source":["P2 = \"\"\"你是一个情景猜谜游戏的主持人。游戏规则如下:\n","\n","1. 参与者会得到一个谜面,谜面会描述一个简单又难以理解的事件。\n","2. 主持人知道谜底,谜底是谜面的答案。\n","3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n","4. 对于每个问题,主持人将根据实际情况回答以下五个选项之一:是、不是、不重要、回答正确、问法错误。各回答的判断标准如下:\n"," - 若谜面和谜底能找到问题的答案,回答:是或者不是\n"," - 若谜面和谜底不能直接或者间接推断出问题的答案,回答:不重要\n"," - 若参与者提问不是一个封闭式问题或者问题难以理解,回答:问法错误\n"," - 若参与者提问基本还原了谜底真相,回答:回答正确\n","5. 回答中不能添加任何其它信息,也不能省略选项中的任何一个字。例如,不可以把“不是”省略成“不”。\n","\n","请严格按照这些规则回答参与者提出的问题。\n","\n","**谜面:** {}\n","\n","**谜底:** {}\n","\n","**参与者提出的问题:** {}\n","\"\"\""]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"text/html":["
\n","\n","
\n"," \n"," \n"," | \n"," epoch | \n"," model | \n"," accuracy | \n"," precision | \n"," recall | \n"," f1 | \n","
\n"," \n"," \n"," \n"," 0 | \n"," 0 | \n"," internlm/internlm2_5-7b-chat-1m | \n"," 0.759667 | \n"," 0.741854 | \n"," 0.781014 | \n"," 0.758887 | \n","
\n"," \n"," 1 | \n"," 1 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-44 | \n"," 0.761667 | \n"," 0.810873 | \n"," 0.761667 | \n"," 0.780018 | \n","
\n"," \n"," 2 | \n"," 2 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-88 | \n"," 0.741333 | \n"," 0.816182 | \n"," 0.741333 | \n"," 0.769524 | \n","
\n"," \n"," 3 | \n"," 3 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-132 | \n"," 0.755000 | \n"," 0.809829 | \n"," 0.755000 | \n"," 0.775657 | \n","
\n"," \n"," 4 | \n"," 4 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-176 | \n"," 0.719000 | \n"," 0.803307 | \n"," 0.719000 | \n"," 0.750319 | \n","
\n"," \n","
\n","
"],"text/plain":[" epoch model accuracy precision \\\n","0 0 internlm/internlm2_5-7b-chat-1m 0.759667 0.741854 \n","1 1 internlm/internlm2_5-7b-chat-1m_checkpoint-44 0.761667 0.810873 \n","2 2 internlm/internlm2_5-7b-chat-1m_checkpoint-88 0.741333 0.816182 \n","3 3 internlm/internlm2_5-7b-chat-1m_checkpoint-132 0.755000 0.809829 \n","4 4 internlm/internlm2_5-7b-chat-1m_checkpoint-176 0.719000 0.803307 \n","\n"," recall f1 \n","0 0.781014 0.758887 \n","1 0.761667 0.780018 \n","2 0.741333 0.769524 \n","3 0.755000 0.775657 \n","4 0.719000 0.750319 "]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","df_p1 = pd.read_csv(\"results/mgtv-results_p1_full_metrics.csv\")\n","df_p1"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/html":["\n","\n","
\n"," \n"," \n"," | \n"," epoch | \n"," model | \n"," accuracy | \n"," precision | \n"," recall | \n"," f1 | \n","
\n"," \n"," \n"," \n"," 0 | \n"," 0 | \n"," internlm/internlm2_5-7b-chat-1m | \n"," 0.766000 | \n"," 0.747969 | \n"," 0.787526 | \n"," 0.764922 | \n","
\n"," \n"," 1 | \n"," 1 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-88 | \n"," 0.796333 | \n"," 0.808232 | \n"," 0.796333 | \n"," 0.798160 | \n","
\n"," \n"," 2 | \n"," 2 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-176 | \n"," 0.781333 | \n"," 0.804716 | \n"," 0.781333 | \n"," 0.788581 | \n","
\n"," \n"," 3 | \n"," 3 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-264 | \n"," 0.759000 | \n"," 0.805502 | \n"," 0.759000 | \n"," 0.777237 | \n","
\n"," \n"," 4 | \n"," 4 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-352 | \n"," 0.730333 | \n"," 0.790676 | \n"," 0.730333 | \n"," 0.753716 | \n","
\n"," \n"," 5 | \n"," 5 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-440 | \n"," 0.730333 | \n"," 0.790420 | \n"," 0.730333 | \n"," 0.753750 | \n","
\n"," \n"," 6 | \n"," 6 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-528 | \n"," 0.716000 | \n"," 0.789892 | \n"," 0.716000 | \n"," 0.744833 | \n","
\n"," \n","
\n","
"],"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-88 0.796333 0.808232 \n","2 2 internlm/internlm2_5-7b-chat-1m_checkpoint-176 0.781333 0.804716 \n","3 3 internlm/internlm2_5-7b-chat-1m_checkpoint-264 0.759000 0.805502 \n","4 4 internlm/internlm2_5-7b-chat-1m_checkpoint-352 0.730333 0.790676 \n","5 5 internlm/internlm2_5-7b-chat-1m_checkpoint-440 0.730333 0.790420 \n","6 6 internlm/internlm2_5-7b-chat-1m_checkpoint-528 0.716000 0.789892 \n","\n"," recall f1 \n","0 0.787526 0.764922 \n","1 0.796333 0.798160 \n","2 0.781333 0.788581 \n","3 0.759000 0.777237 \n","4 0.730333 0.753716 \n","5 0.730333 0.753750 \n","6 0.716000 0.744833 "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["df_p2 = pd.read_csv(\"results/mgtv-results_p2_full_metrics.csv\")\n","df_p2"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"data":{"text/html":["\n","\n","
\n"," \n"," \n"," | \n"," epoch | \n"," model | \n"," accuracy | \n"," precision | \n"," recall | \n"," f1 | \n","
\n"," \n"," \n"," \n"," 0 | \n"," 0 | \n"," internlm/internlm2_5-7b-chat-1m | \n"," 0.766000 | \n"," 0.747969 | \n"," 0.787526 | \n"," 0.764922 | \n","
\n"," \n"," 1 | \n"," 1 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-175 | \n"," 0.812000 | \n"," 0.812286 | \n"," 0.812000 | \n"," 0.810234 | \n","
\n"," \n"," 2 | \n"," 2 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-350 | \n"," 0.765333 | \n"," 0.806889 | \n"," 0.765333 | \n"," 0.779998 | \n","
\n"," \n"," 3 | \n"," 3 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-525 | \n"," 0.747667 | \n"," 0.812033 | \n"," 0.747667 | \n"," 0.773122 | \n","
\n"," \n"," 4 | \n"," 4 | \n"," internlm/internlm2_5-7b-chat-1m_checkpoint-700 | \n"," 0.717000 | \n"," 0.804642 | \n"," 0.717000 | \n"," 0.751034 | \n","
\n"," \n","
\n","
"],"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":9,"metadata":{},"output_type":"execute_result"}],"source":["df_p2_r2 = pd.read_csv(\"results/mgtv-results_p2_r2_full_metrics.csv\")\n","df_p2_r2"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"data":{"text/html":["\n","\n","
\n"," \n"," \n"," | \n"," epoch | \n"," model | \n"," accuracy | \n"," precision | \n"," recall | \n"," f1 | \n","
\n"," \n"," \n"," \n"," 0 | \n"," 0.0 | \n"," internlm/internlm2_5-7b-chat-1m_torch.bfloat16_lf | \n"," 0.510667 | \n"," 0.751987 | \n"," 0.516695 | \n"," 0.542057 | \n","
\n"," \n"," 1 | \n"," 0.2 | \n"," internlm/internlm2_5-7b-chat-1m/checkpoint-35_... | \n"," 0.784333 | \n"," 0.797765 | \n"," 0.784333 | \n"," 0.786494 | \n","
\n"," \n"," 2 | \n"," 0.4 | \n"," internlm/internlm2_5-7b-chat-1m/checkpoint-70_... | \n"," 0.783667 | \n"," 0.799698 | \n"," 0.783667 | \n"," 0.788688 | \n","
\n"," \n"," 3 | \n"," 0.6 | \n"," internlm/internlm2_5-7b-chat-1m/checkpoint-105... | \n"," 0.724333 | \n"," 0.817117 | \n"," 0.724333 | \n"," 0.756580 | \n","
\n"," \n"," 4 | \n"," 0.8 | \n"," internlm/internlm2_5-7b-chat-1m/checkpoint-140... | \n"," 0.803000 | \n"," 0.803141 | \n"," 0.803000 | \n"," 0.802806 | \n","
\n"," \n"," 5 | \n"," 1.0 | \n"," internlm/internlm2_5-7b-chat-1m/checkpoint-175... | \n"," 0.767667 | \n"," 0.810844 | \n"," 0.767667 | \n"," 0.784319 | \n","
\n"," \n"," 6 | \n"," 1.2 | \n"," internlm/internlm2_5-7b-chat-1m/checkpoint-210... | \n"," 0.773667 | \n"," 0.809167 | \n"," 0.773667 | \n"," 0.787687 | \n","
\n"," \n"," 7 | \n"," 1.4 | \n"," internlm/internlm2_5-7b-chat-1m/checkpoint-245... | \n"," 0.762333 | \n"," 0.806229 | \n"," 0.762333 | \n"," 0.777669 | \n","
\n"," \n"," 8 | \n"," 1.6 | \n"," internlm/internlm2_5-7b-chat-1m/checkpoint-280... | \n"," 0.755333 | \n"," 0.808620 | \n"," 0.755333 | \n"," 0.775559 | \n","
\n"," \n"," 9 | \n"," 1.8 | \n"," internlm/internlm2_5-7b-chat-1m/checkpoint-315... | \n"," 0.748000 | \n"," 0.817200 | \n"," 0.748000 | \n"," 0.773991 | \n","
\n"," \n"," 10 | \n"," 2.0 | \n"," internlm/internlm2_5-7b-chat-1m/checkpoint-350... | \n"," 0.756000 | \n"," 0.812688 | \n"," 0.756000 | \n"," 0.777781 | \n","
\n"," \n","
\n","
"],"text/plain":[" epoch model accuracy \\\n","0 0.0 internlm/internlm2_5-7b-chat-1m_torch.bfloat16_lf 0.510667 \n","1 0.2 internlm/internlm2_5-7b-chat-1m/checkpoint-35_... 0.784333 \n","2 0.4 internlm/internlm2_5-7b-chat-1m/checkpoint-70_... 0.783667 \n","3 0.6 internlm/internlm2_5-7b-chat-1m/checkpoint-105... 0.724333 \n","4 0.8 internlm/internlm2_5-7b-chat-1m/checkpoint-140... 0.803000 \n","5 1.0 internlm/internlm2_5-7b-chat-1m/checkpoint-175... 0.767667 \n","6 1.2 internlm/internlm2_5-7b-chat-1m/checkpoint-210... 0.773667 \n","7 1.4 internlm/internlm2_5-7b-chat-1m/checkpoint-245... 0.762333 \n","8 1.6 internlm/internlm2_5-7b-chat-1m/checkpoint-280... 0.755333 \n","9 1.8 internlm/internlm2_5-7b-chat-1m/checkpoint-315... 0.748000 \n","10 2.0 internlm/internlm2_5-7b-chat-1m/checkpoint-350... 0.756000 \n","\n"," precision recall f1 \n","0 0.751987 0.516695 0.542057 \n","1 0.797765 0.784333 0.786494 \n","2 0.799698 0.783667 0.788688 \n","3 0.817117 0.724333 0.756580 \n","4 0.803141 0.803000 0.802806 \n","5 0.810844 0.767667 0.784319 \n","6 0.809167 0.773667 0.787687 \n","7 0.806229 0.762333 0.777669 \n","8 0.808620 0.755333 0.775559 \n","9 0.817200 0.748000 0.773991 \n","10 0.812688 0.756000 0.777781 "]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["df_p2_r3 = pd.read_csv(\"results/mgtv-results_p2_r3_full_metrics.csv\")\n","df_p2_r3"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[],"source":["def plot_results(df_p1, df_p2, best_p1, best_p2, color_p1=\"red\", color_p2=\"blue\", model_name=\"InternLM2.5_7b\"):\n"," sns.lineplot(\n"," x=\"epoch\",\n"," y=\"accuracy\",\n"," data=df_p1,\n"," ax=ax[0],\n"," color=color_p1,\n"," label=f\"{model_name}: P1\",\n"," )\n"," sns.lineplot(\n"," x=\"epoch\",\n"," y=\"accuracy\",\n"," data=df_p2,\n"," ax=ax[0],\n"," color=color_p2,\n"," label=f\"{model_name}: P2\",\n"," )\n"," sns.scatterplot(\n"," x=\"epoch\", y=\"accuracy\", data=best_p1, ax=ax[0], color=color_p1, s=50\n"," )\n"," sns.scatterplot(\n"," x=\"epoch\", y=\"accuracy\", data=best_p2, ax=ax[0], color=color_p2, s=50\n"," )\n","\n"," sns.lineplot(\n"," x=\"epoch\",\n"," y=\"f1\",\n"," data=df_p1,\n"," ax=ax[1],\n"," color=color_p1,\n"," label=f\"{model_name}: P1\",\n"," )\n"," sns.lineplot(\n"," x=\"epoch\",\n"," y=\"f1\",\n"," data=df_p2,\n"," ax=ax[1],\n"," color=color_p2,\n"," label=f\"{model_name}: P2\",\n"," )\n"," sns.scatterplot(x=\"epoch\", y=\"f1\", data=best_p1, ax=ax[1], color=color_p1, s=50)\n"," sns.scatterplot(x=\"epoch\", y=\"f1\", data=best_p2, ax=ax[1], color=color_p2, s=50)"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[],"source":["# plot the results\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import matplotlib.ticker as ticker\n","\n","def plot_model_results(model_name, df_p1, df_p2, ax):\n"," print(f\"Model: {model_name}\")\n"," sns.set_theme(style=\"whitegrid\")\n","\n"," # print the best results\n"," best_p1 = df_p1[df_p1[\"accuracy\"] == df_p1[\"accuracy\"].max()]\n"," best_p2 = df_p2[df_p2[\"accuracy\"] == df_p2[\"accuracy\"].max()]\n","\n"," print(\"Best P1 accuracy:\")\n"," print(best_p1[\"accuracy\"].values[0])\n"," print(\"Best P2 accuracy:\")\n"," print(best_p2[\"accuracy\"].values[0])\n","\n"," plot_results(df_p1, df_p2, best_p1, best_p2, model_name=model_name)\n","\n"," for a in ax:\n"," for line_index, line in enumerate(a.lines):\n"," # Get the data\n"," line_color = line.get_color()\n"," xdata, ydata = line.get_data()\n"," for index in range(xdata.size):\n"," a.annotate( # Use 'a' instead of 'ax' to refer to the current subplot\n"," f\"{ydata[index]:.3f}\",\n"," xy=(xdata[index], ydata[index]),\n"," textcoords=\"offset points\",\n"," xytext=(\n"," 0,\n"," 1,\n"," # -10 if line_index % 2 == 0 else 10,\n"," ), # Adjusted for better visibility\n"," ha=\"center\",\n"," color=line_color,\n"," )\n","\n"," ax[0].set_title(\"Accuracy\")\n"," ax[1].set_title(\"F1\")\n","\n"," # After plotting your data and before plt.show(), add these lines\n"," ax[0].xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n"," ax[1].xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n","\n"," plt.show()"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: InternLM_2_5-7b\n","Best P1 accuracy:\n","0.7616666666666667\n","Best P2 accuracy:\n","0.7963333333333333\n"]},{"data":{"image/png":"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","text/plain":["