diff --git "a/competition/11b_Llama-3_8b_p1_en_analysis.ipynb" "b/competition/11b_Llama-3_8b_p1_en_analysis.ipynb"
new file mode 100644--- /dev/null
+++ "b/competition/11b_Llama-3_8b_p1_en_analysis.ipynb"
@@ -0,0 +1 @@
+{"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>meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-117_torch.bfloat16_lf</th>\n","      <th>meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-234_torch.bfloat16_lf</th>\n","      <th>meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-351_torch.bfloat16_lf</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Was Zhen Zhesuo suicide?</td>\n","      <td>No</td>\n","      <td>The Mystery of the Coast</td>\n","      <td>In the quiet seaside cottage of a neighbor, a ...</td>\n","      <td>Zhen Zhesao was a nature-loving painter who ca...</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Was Zhen Zhesuo sickly?</td>\n","      <td>Yes</td>\n","      <td>The Mystery of the Coast</td>\n","      <td>In the quiet seaside cottage of a neighbor, a ...</td>\n","      <td>Zhen Zhesao was a nature-loving painter who ca...</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>The painting was by Zhen.</td>\n","      <td>Yes</td>\n","      <td>The Mystery of the Coast</td>\n","      <td>In the quiet seaside cottage of a neighbor, a ...</td>\n","      <td>Zhen Zhesao was a nature-loving painter who ca...</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Was Zhen with a heart condition?</td>\n","      <td>Yes</td>\n","      <td>The Mystery of the Coast</td>\n","      <td>In the quiet seaside cottage of a neighbor, a ...</td>\n","      <td>Zhen Zhesao was a nature-loving painter who ca...</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>The wheel was the murderer's weapon.</td>\n","      <td>No</td>\n","      <td>The Mystery of the Coast</td>\n","      <td>In the quiet seaside cottage of a neighbor, a ...</td>\n","      <td>Zhen Zhesao was a nature-loving painter who ca...</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</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","    </tr>\n","    <tr>\n","      <th>2995</th>\n","      <td>Did the weeping person have to make a sacrific...</td>\n","      <td>Yes</td>\n","      <td>Zhen Zhuo's wails</td>\n","      <td>One night, in a quiet village, a weeping sound...</td>\n","      <td>It turned out that the old hat belonged to a l...</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Unimportant</td>\n","    </tr>\n","    <tr>\n","      <th>2996</th>\n","      <td>Was the body in the lake?</td>\n","      <td>No</td>\n","      <td>Zhen Zhuo's wails</td>\n","      <td>One night, in a quiet village, a weeping sound...</td>\n","      <td>It turned out that the old hat belonged to a l...</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>2997</th>\n","      <td>Do mourners have a special relationship with t...</td>\n","      <td>Yes</td>\n","      <td>Zhen Zhuo's wails</td>\n","      <td>One night, in a quiet village, a weeping sound...</td>\n","      <td>It turned out that the old hat belonged to a l...</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>2998</th>\n","      <td>Was the owner of the hat dead?</td>\n","      <td>No</td>\n","      <td>Zhen Zhuo's wails</td>\n","      <td>One night, in a quiet village, a weeping sound...</td>\n","      <td>It turned out that the old hat belonged to a l...</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>2999</th>\n","      <td>Was the dead person wounded?</td>\n","      <td>No</td>\n","      <td>Zhen Zhuo's wails</td>\n","      <td>One night, in a quiet village, a weeping sound...</td>\n","      <td>It turned out that the old hat belonged to a l...</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Unimportant</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>3000 rows × 8 columns</p>\n","</div>"],"text/plain":["                                                   text label  \\\n","0                              Was Zhen Zhesuo suicide?    No   \n","1                               Was Zhen Zhesuo sickly?   Yes   \n","2                             The painting was by Zhen.   Yes   \n","3                      Was Zhen with a heart condition?   Yes   \n","4                  The wheel was the murderer's weapon.    No   \n","...                                                 ...   ...   \n","2995  Did the weeping person have to make a sacrific...   Yes   \n","2996                          Was the body in the lake?    No   \n","2997  Do mourners have a special relationship with t...   Yes   \n","2998                     Was the owner of the hat dead?    No   \n","2999                       Was the dead person wounded?    No   \n","\n","                         title  \\\n","0     The Mystery of the Coast   \n","1     The Mystery of the Coast   \n","2     The Mystery of the Coast   \n","3     The Mystery of the Coast   \n","4     The Mystery of the Coast   \n","...                        ...   \n","2995         Zhen Zhuo's wails   \n","2996         Zhen Zhuo's wails   \n","2997         Zhen Zhuo's wails   \n","2998         Zhen Zhuo's wails   \n","2999         Zhen Zhuo's wails   \n","\n","                                                 puzzle  \\\n","0     In the quiet seaside cottage of a neighbor, a ...   \n","1     In the quiet seaside cottage of a neighbor, a ...   \n","2     In the quiet seaside cottage of a neighbor, a ...   \n","3     In the quiet seaside cottage of a neighbor, a ...   \n","4     In the quiet seaside cottage of a neighbor, a ...   \n","...                                                 ...   \n","2995  One night, in a quiet village, a weeping sound...   \n","2996  One night, in a quiet village, a weeping sound...   \n","2997  One night, in a quiet village, a weeping sound...   \n","2998  One night, in a quiet village, a weeping sound...   \n","2999  One night, in a quiet village, a weeping sound...   \n","\n","                                                  truth  \\\n","0     Zhen Zhesao was a nature-loving painter who ca...   \n","1     Zhen Zhesao was a nature-loving painter who ca...   \n","2     Zhen Zhesao was a nature-loving painter who ca...   \n","3     Zhen Zhesao was a nature-loving painter who ca...   \n","4     Zhen Zhesao was a nature-loving painter who ca...   \n","...                                                 ...   \n","2995  It turned out that the old hat belonged to a l...   \n","2996  It turned out that the old hat belonged to a l...   \n","2997  It turned out that the old hat belonged to a l...   \n","2998  It turned out that the old hat belonged to a l...   \n","2999  It turned out that the old hat belonged to a l...   \n","\n","     meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-117_torch.bfloat16_lf  \\\n","0                                                    No                     \n","1                                                   Yes                     \n","2                                                    No                     \n","3                                                   Yes                     \n","4                                                    No                     \n","...                                                 ...                     \n","2995                                                 No                     \n","2996                                                 No                     \n","2997                                                Yes                     \n","2998                                                 No                     \n","2999                                                 No                     \n","\n","     meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-234_torch.bfloat16_lf  \\\n","0                                                    No                     \n","1                                                   Yes                     \n","2                                                   Yes                     \n","3                                                   Yes                     \n","4                                                    No                     \n","...                                                 ...                     \n","2995                                                 No                     \n","2996                                                 No                     \n","2997                                                Yes                     \n","2998                                                 No                     \n","2999                                                 No                     \n","\n","     meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-351_torch.bfloat16_lf  \n","0                                                    No                    \n","1                                                   Yes                    \n","2                                                   Yes                    \n","3                                                   Yes                    \n","4                                                    No                    \n","...                                                 ...                    \n","2995                                        Unimportant                    \n","2996                                                 No                    \n","2997                                                Yes                    \n","2998                                                 No                    \n","2999                                        Unimportant                    \n","\n","[3000 rows x 8 columns]"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","df = pd.read_csv(\"results/llama3-8b_lora_sft_bf16-p1_en.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"," 'meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-117_torch.bfloat16_lf',\n"," 'meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-234_torch.bfloat16_lf',\n"," 'meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-351_torch.bfloat16_lf']"]},"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":["********** meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-117_torch.bfloat16_lf **********\n","meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-117_torch.bfloat16_lf\n","No                       2033\n","Yes                       840\n","Unimportant                83\n","Incorrect questioning      32\n","Correct answer             12\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":["********** meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-234_torch.bfloat16_lf **********\n","meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-234_torch.bfloat16_lf\n","No                       1224\n","Yes                       929\n","Unimportant               724\n","Incorrect questioning      70\n","Correct answer             53\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":["********** meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-351_torch.bfloat16_lf **********\n","meta-llama/Meta-Llama-3-8B-Instruct/checkpoint-351_torch.bfloat16_lf\n","No                       1603\n","Yes                       871\n","Unimportant               442\n","Incorrect questioning      50\n","Correct answer             34\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","    try:\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","    except Exception as e:\n","        print(e)\n","        accuracy = precision = recall = f1 = np.nan\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_84362/3667033001.py:18: 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>0.333333</td>\n","      <td>meta-llama/Meta-Llama-3-8B-Instruct/checkpoint...</td>\n","      <td>0.648667</td>\n","      <td>0.652593</td>\n","      <td>0.648667</td>\n","      <td>0.631272</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.666667</td>\n","      <td>meta-llama/Meta-Llama-3-8B-Instruct/checkpoint...</td>\n","      <td>0.561000</td>\n","      <td>0.689710</td>\n","      <td>0.561000</td>\n","      <td>0.608339</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.000000</td>\n","      <td>meta-llama/Meta-Llama-3-8B-Instruct/checkpoint...</td>\n","      <td>0.621000</td>\n","      <td>0.686843</td>\n","      <td>0.621000</td>\n","      <td>0.641744</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      epoch                                              model  accuracy  \\\n","0  0.333333  meta-llama/Meta-Llama-3-8B-Instruct/checkpoint...  0.648667   \n","1  0.666667  meta-llama/Meta-Llama-3-8B-Instruct/checkpoint...  0.561000   \n","2  1.000000  meta-llama/Meta-Llama-3-8B-Instruct/checkpoint...  0.621000   \n","\n","   precision    recall        f1  \n","0   0.652593  0.648667  0.631272  \n","1   0.689710  0.561000  0.608339  \n","2   0.686843  0.621000  0.641744  "]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["import pandas as pd\n","\n","perf_df = pd.DataFrame(\n","    columns=[\"epoch\", \"model\", \"accuracy\", \"precision\", \"recall\", \"f1\"]\n",")\n","for i, col in enumerate(df.columns[5:]):\n","    accuracy, precision, recall, f1 = calc_metrics_for_col(df, col)\n","    new_model_metrics = {\n","        \"epoch\": (i + 1) / 3,\n","        \"model\": col,\n","        \"accuracy\": accuracy,\n","        \"precision\": precision,\n","        \"recall\": recall,\n","        \"f1\": f1,\n","    }\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":13,"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():.3f}\",\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\")\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":14,"metadata":{},"outputs":[],"source":["perf_df.to_csv(\"results/mgtv-llama3_p1_en_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}