notebooks/00_Data Analysis.ipynb

{"cells":[{"cell_type":"code","execution_count":39,"metadata":{"executionInfo":{"elapsed":476,"status":"ok","timestamp":1720679526275,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"uWKRSV6eZsCn"},"outputs":[{"name":"stdout","output_type":"stream","text":["The autoreload extension is already loaded. To reload it, use:\n","  %reload_ext autoreload\n"]}],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":40,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"eb33b19f-1206-41ee-84e2-e6258a12eef7","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2534,"status":"ok","timestamp":1720679529344,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"xwFh14uiZBrI","outputId":"d767799c-34c2-46a5-f052-378146a55321"},"outputs":[],"source":["from pathlib import Path\n","\n","if \"workding_dir\" not in locals():\n","    try:\n","        from google.colab import drive\n","\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":41,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"6d394937-6c99-4a7c-9d32-7600a280032f","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"G5pNu3zgZBrL","outputId":"160a554f-fb08-4aa0-bc00-0422fb7c1fac"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /Users/inflaton/code/engd/projects/logical-reasoning\n"]}],"source":["import os\n","import sys\n","from pathlib import Path\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":42,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["working dir: /Users/inflaton/code/engd/projects/logical-reasoning\n"]}],"source":["# haotian comp\n","import os\n","import sys\n","from pathlib import Path\n","\n","if \"workding_dir\" not in locals():\n","    workding_dir = str(Path.cwd().parent)\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"working dir:\", workding_dir)"]},{"cell_type":"code","execution_count":43,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"hPCC-6m7ZBrM","outputId":"c7aa2c96-5e99-440a-c148-201d79465ff9"},"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":43,"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":44,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"f1597656-8042-4878-9d3b-9ebfb8dd86dc","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"1M3IraVtZBrM","outputId":"29ab35f6-2970-4ade-d85d-3174acf8cda0"},"outputs":[],"source":["model_orders = {\n","    \"internlm2_5-7b-chat-1m\": 10,\n","    \"Qwen2-7B-Instruct\": 20,\n","    \"Llama3.1-8B-Chinese-Chat\": 30,\n","    \"Llama3.1-70B-Chinese-Chat\": 40,\n","    \"Qwen2-72B-Instruct\": 50,\n","}"]},{"cell_type":"code","execution_count":45,"metadata":{},"outputs":[],"source":["markers = [\n","    \"o\",\n","    \"x\",\n","    \"^\",\n","    \"s\",\n","    \"d\",\n","    \"P\",\n","    \"X\",\n","    \"*\",\n","    \"v\",\n","    \">\",\n","    \"<\",\n","    \"p\",\n","    \"h\",\n","    \"H\",\n","    \"+\",\n","    \"|\",\n","    \"_\",\n","]\n","model_markers = {k: markers[i] for i, k in enumerate(model_orders.keys())}"]},{"cell_type":"code","execution_count":46,"metadata":{},"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>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>0.0</td>\n","      <td>internlm/internlm2_5-7b-chat-1m_torch.bfloat16_lf</td>\n","      <td>0.510667</td>\n","      <td>0.743214</td>\n","      <td>0.510667</td>\n","      <td>0.535733</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.2</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-35_...</td>\n","      <td>0.784333</td>\n","      <td>0.797765</td>\n","      <td>0.784333</td>\n","      <td>0.786494</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.4</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-70_...</td>\n","      <td>0.783667</td>\n","      <td>0.799698</td>\n","      <td>0.783667</td>\n","      <td>0.788688</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.6</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-105...</td>\n","      <td>0.724333</td>\n","      <td>0.817117</td>\n","      <td>0.724333</td>\n","      <td>0.756580</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.8</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-140...</td>\n","      <td>0.803000</td>\n","      <td>0.803141</td>\n","      <td>0.803000</td>\n","      <td>0.802806</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1.0</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-175...</td>\n","      <td>0.767667</td>\n","      <td>0.810844</td>\n","      <td>0.767667</td>\n","      <td>0.784319</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>1.2</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-210...</td>\n","      <td>0.773667</td>\n","      <td>0.809167</td>\n","      <td>0.773667</td>\n","      <td>0.787687</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>1.4</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-245...</td>\n","      <td>0.762333</td>\n","      <td>0.806229</td>\n","      <td>0.762333</td>\n","      <td>0.777669</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1.6</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-280...</td>\n","      <td>0.755333</td>\n","      <td>0.808620</td>\n","      <td>0.755333</td>\n","      <td>0.775559</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>1.8</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-315...</td>\n","      <td>0.748000</td>\n","      <td>0.817200</td>\n","      <td>0.748000</td>\n","      <td>0.773991</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>2.0</td>\n","      <td>internlm/internlm2_5-7b-chat-1m/checkpoint-350...</td>\n","      <td>0.756000</td>\n","      <td>0.812688</td>\n","      <td>0.756000</td>\n","      <td>0.777781</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>Qwen/Qwen2-7B-Instruct_torch.float16_lf</td>\n","      <td>0.619333</td>\n","      <td>0.755570</td>\n","      <td>0.619333</td>\n","      <td>0.672630</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.2</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-35_torch.flo...</td>\n","      <td>0.725000</td>\n","      <td>0.784017</td>\n","      <td>0.725000</td>\n","      <td>0.748995</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.4</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-70_torch.flo...</td>\n","      <td>0.759000</td>\n","      <td>0.800530</td>\n","      <td>0.759000</td>\n","      <td>0.774875</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.6</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-105_torch.fl...</td>\n","      <td>0.692667</td>\n","      <td>0.803918</td>\n","      <td>0.692667</td>\n","      <td>0.733248</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.8</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-140_torch.fl...</td>\n","      <td>0.725000</td>\n","      <td>0.795272</td>\n","      <td>0.725000</td>\n","      <td>0.747624</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1.0</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-175_torch.fl...</td>\n","      <td>0.675667</td>\n","      <td>0.781015</td>\n","      <td>0.675667</td>\n","      <td>0.708654</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>1.2</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-210_torch.fl...</td>\n","      <td>0.701333</td>\n","      <td>0.796956</td>\n","      <td>0.701333</td>\n","      <td>0.736268</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>1.4</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-245_torch.fl...</td>\n","      <td>0.732667</td>\n","      <td>0.792254</td>\n","      <td>0.732667</td>\n","      <td>0.755402</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1.6</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-280_torch.fl...</td>\n","      <td>0.698333</td>\n","      <td>0.785127</td>\n","      <td>0.698333</td>\n","      <td>0.729225</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>1.8</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-315_torch.fl...</td>\n","      <td>0.678333</td>\n","      <td>0.785391</td>\n","      <td>0.678333</td>\n","      <td>0.716413</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>2.0</td>\n","      <td>Qwen/Qwen2-7B-Instruct/checkpoint-350_torch.fl...</td>\n","      <td>0.689000</td>\n","      <td>0.792972</td>\n","      <td>0.689000</td>\n","      <td>0.725999</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat_torch.fl...</td>\n","      <td>0.236667</td>\n","      <td>0.745718</td>\n","      <td>0.236667</td>\n","      <td>0.339624</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.2</td>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...</td>\n","      <td>0.625667</td>\n","      <td>0.827414</td>\n","      <td>0.625667</td>\n","      <td>0.693570</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.4</td>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...</td>\n","      <td>0.762000</td>\n","      <td>0.789946</td>\n","      <td>0.762000</td>\n","      <td>0.766701</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.6</td>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...</td>\n","      <td>0.680333</td>\n","      <td>0.798030</td>\n","      <td>0.680333</td>\n","      <td>0.721244</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.8</td>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...</td>\n","      <td>0.752333</td>\n","      <td>0.807426</td>\n","      <td>0.752333</td>\n","      <td>0.773644</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1.0</td>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...</td>\n","      <td>0.737000</td>\n","      <td>0.809059</td>\n","      <td>0.737000</td>\n","      <td>0.763784</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>Qwen/Qwen2-72B-Instruct_torch.bfloat16_4bit_lf</td>\n","      <td>0.747333</td>\n","      <td>0.804122</td>\n","      <td>0.747333</td>\n","      <td>0.760783</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.2</td>\n","      <td>Qwen/Qwen2-72B-Instruct/checkpoint-35_torch.bf...</td>\n","      <td>0.758333</td>\n","      <td>0.819993</td>\n","      <td>0.758333</td>\n","      <td>0.782751</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.4</td>\n","      <td>Qwen/Qwen2-72B-Instruct/checkpoint-70_torch.bf...</td>\n","      <td>0.736667</td>\n","      <td>0.822487</td>\n","      <td>0.736667</td>\n","      <td>0.770063</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.6</td>\n","      <td>Qwen/Qwen2-72B-Instruct/checkpoint-105_torch.b...</td>\n","      <td>0.757000</td>\n","      <td>0.825382</td>\n","      <td>0.757000</td>\n","      <td>0.784000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.8</td>\n","      <td>Qwen/Qwen2-72B-Instruct/checkpoint-140_torch.b...</td>\n","      <td>0.789333</td>\n","      <td>0.822910</td>\n","      <td>0.789333</td>\n","      <td>0.803312</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1.0</td>\n","      <td>Qwen/Qwen2-72B-Instruct/checkpoint-175_torch.b...</td>\n","      <td>0.737667</td>\n","      <td>0.824365</td>\n","      <td>0.737667</td>\n","      <td>0.769962</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>1.2</td>\n","      <td>Qwen/Qwen2-72B-Instruct/checkpoint-210_torch.b...</td>\n","      <td>0.763000</td>\n","      <td>0.831888</td>\n","      <td>0.763000</td>\n","      <td>0.790108</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"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","0     0.0            Qwen/Qwen2-7B-Instruct_torch.float16_lf  0.619333   \n","1     0.2  Qwen/Qwen2-7B-Instruct/checkpoint-35_torch.flo...  0.725000   \n","2     0.4  Qwen/Qwen2-7B-Instruct/checkpoint-70_torch.flo...  0.759000   \n","3     0.6  Qwen/Qwen2-7B-Instruct/checkpoint-105_torch.fl...  0.692667   \n","4     0.8  Qwen/Qwen2-7B-Instruct/checkpoint-140_torch.fl...  0.725000   \n","5     1.0  Qwen/Qwen2-7B-Instruct/checkpoint-175_torch.fl...  0.675667   \n","6     1.2  Qwen/Qwen2-7B-Instruct/checkpoint-210_torch.fl...  0.701333   \n","7     1.4  Qwen/Qwen2-7B-Instruct/checkpoint-245_torch.fl...  0.732667   \n","8     1.6  Qwen/Qwen2-7B-Instruct/checkpoint-280_torch.fl...  0.698333   \n","9     1.8  Qwen/Qwen2-7B-Instruct/checkpoint-315_torch.fl...  0.678333   \n","10    2.0  Qwen/Qwen2-7B-Instruct/checkpoint-350_torch.fl...  0.689000   \n","0     0.0  shenzhi-wang/Llama3.1-8B-Chinese-Chat_torch.fl...  0.236667   \n","1     0.2  shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...  0.625667   \n","2     0.4  shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...  0.762000   \n","3     0.6  shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...  0.680333   \n","4     0.8  shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...  0.752333   \n","5     1.0  shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoi...  0.737000   \n","0     0.0     Qwen/Qwen2-72B-Instruct_torch.bfloat16_4bit_lf  0.747333   \n","1     0.2  Qwen/Qwen2-72B-Instruct/checkpoint-35_torch.bf...  0.758333   \n","2     0.4  Qwen/Qwen2-72B-Instruct/checkpoint-70_torch.bf...  0.736667   \n","3     0.6  Qwen/Qwen2-72B-Instruct/checkpoint-105_torch.b...  0.757000   \n","4     0.8  Qwen/Qwen2-72B-Instruct/checkpoint-140_torch.b...  0.789333   \n","5     1.0  Qwen/Qwen2-72B-Instruct/checkpoint-175_torch.b...  0.737667   \n","6     1.2  Qwen/Qwen2-72B-Instruct/checkpoint-210_torch.b...  0.763000   \n","\n","    precision    recall        f1  \n","0    0.743214  0.510667  0.535733  \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  \n","0    0.755570  0.619333  0.672630  \n","1    0.784017  0.725000  0.748995  \n","2    0.800530  0.759000  0.774875  \n","3    0.803918  0.692667  0.733248  \n","4    0.795272  0.725000  0.747624  \n","5    0.781015  0.675667  0.708654  \n","6    0.796956  0.701333  0.736268  \n","7    0.792254  0.732667  0.755402  \n","8    0.785127  0.698333  0.729225  \n","9    0.785391  0.678333  0.716413  \n","10   0.792972  0.689000  0.725999  \n","0    0.745718  0.236667  0.339624  \n","1    0.827414  0.625667  0.693570  \n","2    0.789946  0.762000  0.766701  \n","3    0.798030  0.680333  0.721244  \n","4    0.807426  0.752333  0.773644  \n","5    0.809059  0.737000  0.763784  \n","0    0.804122  0.747333  0.760783  \n","1    0.819993  0.758333  0.782751  \n","2    0.822487  0.736667  0.770063  \n","3    0.825382  0.757000  0.784000  \n","4    0.822910  0.789333  0.803312  \n","5    0.824365  0.737667  0.769962  \n","6    0.831888  0.763000  0.790108  "]},"execution_count":46,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","perf_df = None\n","model_perf_dfs = {}\n","for model_name in model_orders.keys():\n","    metrics_csv = f\"data/{model_name}_metrics.csv\"\n","    if not Path(metrics_csv).exists():\n","        continue\n","    df = pd.read_csv(metrics_csv)\n","    model_perf_dfs[model_name] = df\n","    perf_df = df if perf_df is None else pd.concat([perf_df, df])\n","\n","perf_df"]},{"cell_type":"code","execution_count":47,"metadata":{},"outputs":[],"source":["import matplotlib.pyplot as plt\n","from matplotlib.ticker import MultipleLocator\n","\n","def plot_perf(\n","    model_perf_dfs,\n","    model_markers,\n","    x_major_locator=0.2,\n","    y_offset=0.05,\n","):\n","    fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n","\n","    for model_name, perf_df in model_perf_dfs.items():\n","        # Ensure the lengths of perf_df[\"epoch\"], perf_df[\"accuracy\"], and perf_df[\"f1\"] are the same\n","        min_length = min(len(perf_df[\"epoch\"]), len(perf_df[\"accuracy\"]), len(perf_df[\"f1\"]))\n","        perf_df = perf_df.iloc[:min_length]\n","\n","        ax.plot(\n","            perf_df[\"epoch\"], perf_df[\"f1\"], marker=model_markers[model_name], label=model_name\n","        )\n","\n","        best_f1 = perf_df[\"f1\"].idxmax()\n","        ax.annotate(\n","            f\"{perf_df['f1'].iloc[best_f1]*100:.2f}%\",\n","            (perf_df[\"epoch\"].iloc[best_f1], perf_df[\"f1\"].iloc[best_f1]),\n","            ha=\"center\",\n","            va=\"bottom\",\n","            xytext=(0, 0),\n","            textcoords=\"offset points\",\n","            fontsize=10,\n","        )\n","\n","    # Set y-axis limit\n","    y_scales = ax.get_ylim()\n","    ax.set_ylim(y_scales[0], y_scales[1] + y_offset)\n","\n","    # Add title and labels\n","    ax.set_xlabel(\"Epoch (0: base model, 0.2 - 2: fine-tuned models)\")\n","    ax.set_ylabel(\"F1 Score\")\n","\n","    # Set x-axis grid spacing to 0.2\n","    ax.xaxis.set_major_locator(MultipleLocator(x_major_locator))\n","    ax.set_title(\n","        \"Performance Analysis Across Checkpoints for Models\"\n","    )\n","\n","    # Rotate x labels\n","    plt.xticks(rotation=0)\n","    plt.grid(True)\n","    # plt.tight_layout()\n","\n","    # Set legend at the right to avoid overlapping with lines\n","    plt.legend(loc=\"center left\", bbox_to_anchor=(1.0, 0.5))\n","\n","    plt.show()"]},{"cell_type":"code","execution_count":48,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_perf(model_perf_dfs, model_markers)"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"mostRecentlyExecutedCommandWithImplicitDF":{"commandId":-1,"dataframes":["_sqldf"]},"pythonIndentUnit":4},"notebookName":"10_eval-lf-medium-py3.11","widgets":{}},"colab":{"gpuType":"L4","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}