demo/summarize_by_passk.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c217f162-d86d-489f-b6a8-6bd82a98181a",
   "metadata": {},
   "source": [
    "# pass@k analysis\n",
    "This notebook demonstrates how to visualize which model managed to achieve which pass@k value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d7c80449-8b7b-41a7-a92c-f7edfd443e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c7abf5c6-62ac-4597-8aec-b5b9ab9503b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples_claude-3-5-sonnet-20240620_passk_results.csv\n",
      "samples_claude-3-5-sonnet-20241022_passk_results.csv\n",
      "samples_claude-3-opus-20240229_passk_results.csv\n",
      "samples_codegemma-7b-instruct-fp16_passk_results.csv\n",
      "samples_codellama-70b-instruct-q4_0_passk_results.csv\n",
      "samples_codellama_passk_results.csv\n",
      "samples_command-r-plus-104b-q4_0_passk_results.csv\n",
      "samples_deepseek-coder-v2_passk_results.csv\n",
      "samples_gemini-1.5-flash-001_passk_results.csv\n",
      "samples_gemini-1.5-pro-001_passk_results.csv\n",
      "samples_gemini-pro_passk_results.csv\n",
      "samples_gpt-3.5-turbo-1106_passk_results.csv\n",
      "samples_gpt-4-1106-preview_passk_results.csv\n",
      "samples_gpt-4-turbo-2024-04-09_passk_results.csv\n",
      "samples_gpt-4o-2024-05-13_passk_results.csv\n",
      "samples_gpt-4o-2024-08-06_passk_results.csv\n",
      "samples_gpt-4o-mini-2024-07-18_passk_results.csv\n",
      "samples_llama3-70b-instruct-q4_0_passk_results.csv\n",
      "samples_llama3-70b-instruct-q8_0_passk_results.csv\n",
      "samples_llama3-8b-instruct-fp16_passk_results.csv\n",
      "samples_meta-llama-3.1-405b-instruct_passk_results.csv\n",
      "samples_mistral-nemo_passk_results.csv\n",
      "samples_mixtral-8x22b-instruct-v0.1-q4_0_passk_results.csv\n",
      "samples_mixtral-8x7b-instruct-v0.1-q5_0_passk_results.csv\n",
      "samples_o1-mini-2024-09-12_passk_results.csv\n",
      "samples_phi3-3.8b-mini-instruct-4k-fp16_passk_results.csv\n",
      "samples_reference_passk_results.csv\n"
     ]
    }
   ],
   "source": [
    "directory = \"../data/\"\n",
    "\n",
    "collection = []\n",
    "for filename in os.listdir(directory):\n",
    "    if filename.endswith(\".csv\"):\n",
    "        print(filename)\n",
    "        df = pd.read_csv(directory + filename)\n",
    "        df['model'] = filename.replace(\"_passk_results.csv\", \"\").replace(\"samples_\", \"\")\n",
    "        collection.append(df)\n",
    "\n",
    "df = pd.concat(collection)"
   ]
  },
  {
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   "execution_count": 3,
   "id": "a2cceb48-c75c-4b76-a984-322f3a055062",
   "metadata": {},
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       "   Unnamed: 0    pass@1    pass@5   pass@10                             model\n",
       "0           0  0.580702  0.747911  0.789474        claude-3-5-sonnet-20240620\n",
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       "0           0  0.470175  0.654135  0.684211            claude-3-opus-20240229\n",
       "0           0  0.249123  0.494291  0.578947        codegemma-7b-instruct-fp16\n",
       "0           0  0.177193  0.497633  0.631579       codellama-70b-instruct-q4_0\n",
       "0           0  0.087719  0.300543  0.456140                         codellama\n",
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       "0           0  0.294737  0.448761  0.543860              gemini-1.5-flash-001\n",
       "0           0  0.459649  0.603801  0.649123                gemini-1.5-pro-001\n",
       "0           0  0.173684  0.353523  0.438596                        gemini-pro\n",
       "0           0  0.333333  0.553676  0.701754                gpt-3.5-turbo-1106\n",
       "0           0  0.464912  0.678850  0.719298                gpt-4-1106-preview\n",
       "0           0  0.473684  0.686090  0.736842            gpt-4-turbo-2024-04-09\n",
       "0           0  0.508772  0.694932  0.754386                 gpt-4o-2024-05-13\n",
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       "0           0  0.424561  0.625731  0.684211            gpt-4o-mini-2024-07-18\n",
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       "0           0  0.359649  0.612016  0.719298          llama3-70b-instruct-q8_0\n",
       "0           0  0.142105  0.336745  0.403509           llama3-8b-instruct-fp16\n",
       "0           0  0.454386  0.687552  0.771930      meta-llama-3.1-405b-instruct\n",
       "0           0  0.152632  0.359649  0.508772                      mistral-nemo\n",
       "0           0  0.243860  0.514690  0.666667  mixtral-8x22b-instruct-v0.1-q4_0\n",
       "0           0  0.207018  0.485171  0.578947   mixtral-8x7b-instruct-v0.1-q5_0\n",
       "0           0  0.531579  0.697090  0.754386                o1-mini-2024-09-12\n",
       "0           0  0.180702  0.421540  0.508772   phi3-3.8b-mini-instruct-4k-fp16\n",
       "0           0  1.000000  1.000000  1.000000                         reference"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df"
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  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e5544292-bf62-495d-8be7-4ab493c71aed",
   "metadata": {},
   "outputs": [
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       "      <td>0.612016</td>\n",
       "      <td>0.719298</td>\n",
       "      <td>llama3-70b-instruct-q8_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.350877</td>\n",
       "      <td>0.615915</td>\n",
       "      <td>0.719298</td>\n",
       "      <td>llama3-70b-instruct-q4_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.553676</td>\n",
       "      <td>0.701754</td>\n",
       "      <td>gpt-3.5-turbo-1106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.294737</td>\n",
       "      <td>0.448761</td>\n",
       "      <td>0.543860</td>\n",
       "      <td>gemini-1.5-flash-001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.249123</td>\n",
       "      <td>0.494291</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>codegemma-7b-instruct-fp16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.243860</td>\n",
       "      <td>0.514690</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>mixtral-8x22b-instruct-v0.1-q4_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.207018</td>\n",
       "      <td>0.485171</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>mixtral-8x7b-instruct-v0.1-q5_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.180702</td>\n",
       "      <td>0.421540</td>\n",
       "      <td>0.508772</td>\n",
       "      <td>phi3-3.8b-mini-instruct-4k-fp16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.177193</td>\n",
       "      <td>0.497633</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>codellama-70b-instruct-q4_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.173684</td>\n",
       "      <td>0.353523</td>\n",
       "      <td>0.438596</td>\n",
       "      <td>gemini-pro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.152632</td>\n",
       "      <td>0.359649</td>\n",
       "      <td>0.508772</td>\n",
       "      <td>mistral-nemo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.142105</td>\n",
       "      <td>0.336745</td>\n",
       "      <td>0.403509</td>\n",
       "      <td>llama3-8b-instruct-fp16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.122807</td>\n",
       "      <td>0.384781</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>command-r-plus-104b-q4_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.087719</td>\n",
       "      <td>0.300543</td>\n",
       "      <td>0.456140</td>\n",
       "      <td>codellama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pass@1    pass@5   pass@10                             model\n",
       "0  1.000000  1.000000  1.000000                         reference\n",
       "0  0.636842  0.724937  0.754386        claude-3-5-sonnet-20241022\n",
       "0  0.580702  0.747911  0.789474        claude-3-5-sonnet-20240620\n",
       "0  0.533333  0.690337  0.736842                 gpt-4o-2024-08-06\n",
       "0  0.531579  0.697090  0.754386                o1-mini-2024-09-12\n",
       "0  0.508772  0.694932  0.754386                 gpt-4o-2024-05-13\n",
       "0  0.473684  0.686090  0.736842            gpt-4-turbo-2024-04-09\n",
       "0  0.470175  0.654135  0.684211            claude-3-opus-20240229\n",
       "0  0.464912  0.678850  0.719298                gpt-4-1106-preview\n",
       "0  0.459649  0.603801  0.649123                gemini-1.5-pro-001\n",
       "0  0.454386  0.687552  0.771930      meta-llama-3.1-405b-instruct\n",
       "0  0.424561  0.625731  0.684211            gpt-4o-mini-2024-07-18\n",
       "0  0.368421  0.642161  0.719298                 deepseek-coder-v2\n",
       "0  0.359649  0.612016  0.719298          llama3-70b-instruct-q8_0\n",
       "0  0.350877  0.615915  0.719298          llama3-70b-instruct-q4_0\n",
       "0  0.333333  0.553676  0.701754                gpt-3.5-turbo-1106\n",
       "0  0.294737  0.448761  0.543860              gemini-1.5-flash-001\n",
       "0  0.249123  0.494291  0.578947        codegemma-7b-instruct-fp16\n",
       "0  0.243860  0.514690  0.666667  mixtral-8x22b-instruct-v0.1-q4_0\n",
       "0  0.207018  0.485171  0.578947   mixtral-8x7b-instruct-v0.1-q5_0\n",
       "0  0.180702  0.421540  0.508772   phi3-3.8b-mini-instruct-4k-fp16\n",
       "0  0.177193  0.497633  0.631579       codellama-70b-instruct-q4_0\n",
       "0  0.173684  0.353523  0.438596                        gemini-pro\n",
       "0  0.152632  0.359649  0.508772                      mistral-nemo\n",
       "0  0.142105  0.336745  0.403509           llama3-8b-instruct-fp16\n",
       "0  0.122807  0.384781  0.526316          command-r-plus-104b-q4_0\n",
       "0  0.087719  0.300543  0.456140                         codellama"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the \"Unnamed: 0\" column from the dataframe\n",
    "df = df.drop(\"Unnamed: 0\", axis=1)\n",
    "df = df.sort_values(\"pass@1\", ascending=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e09d98ea-7fc8-4004-9382-f9a02765b052",
   "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>model</th>\n",
       "      <th>reference</th>\n",
       "      <th>claude-3-5-sonnet-20241022</th>\n",
       "      <th>claude-3-5-sonnet-20240620</th>\n",
       "      <th>gpt-4o-2024-08-06</th>\n",
       "      <th>o1-mini-2024-09-12</th>\n",
       "      <th>gpt-4o-2024-05-13</th>\n",
       "      <th>gpt-4-turbo-2024-04-09</th>\n",
       "      <th>claude-3-opus-20240229</th>\n",
       "      <th>gpt-4-1106-preview</th>\n",
       "      <th>gemini-1.5-pro-001</th>\n",
       "      <th>...</th>\n",
       "      <th>mixtral-8x22b-instruct-v0.1-q4_0</th>\n",
       "      <th>mixtral-8x7b-instruct-v0.1-q5_0</th>\n",
       "      <th>phi3-3.8b-mini-instruct-4k-fp16</th>\n",
       "      <th>codellama-70b-instruct-q4_0</th>\n",
       "      <th>gemini-pro</th>\n",
       "      <th>mistral-nemo</th>\n",
       "      <th>llama3-8b-instruct-fp16</th>\n",
       "      <th>command-r-plus-104b-q4_0</th>\n",
       "      <th>codellama</th>\n",
       "      <th>k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pass@1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.636842</td>\n",
       "      <td>0.580702</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.531579</td>\n",
       "      <td>0.508772</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.470175</td>\n",
       "      <td>0.464912</td>\n",
       "      <td>0.459649</td>\n",
       "      <td>...</td>\n",
       "      <td>0.243860</td>\n",
       "      <td>0.207018</td>\n",
       "      <td>0.180702</td>\n",
       "      <td>0.177193</td>\n",
       "      <td>0.173684</td>\n",
       "      <td>0.152632</td>\n",
       "      <td>0.142105</td>\n",
       "      <td>0.122807</td>\n",
       "      <td>0.087719</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pass@5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.724937</td>\n",
       "      <td>0.747911</td>\n",
       "      <td>0.690337</td>\n",
       "      <td>0.697090</td>\n",
       "      <td>0.694932</td>\n",
       "      <td>0.686090</td>\n",
       "      <td>0.654135</td>\n",
       "      <td>0.678850</td>\n",
       "      <td>0.603801</td>\n",
       "      <td>...</td>\n",
       "      <td>0.514690</td>\n",
       "      <td>0.485171</td>\n",
       "      <td>0.421540</td>\n",
       "      <td>0.497633</td>\n",
       "      <td>0.353523</td>\n",
       "      <td>0.359649</td>\n",
       "      <td>0.336745</td>\n",
       "      <td>0.384781</td>\n",
       "      <td>0.300543</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pass@10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.754386</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.754386</td>\n",
       "      <td>0.754386</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.719298</td>\n",
       "      <td>0.649123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.508772</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.438596</td>\n",
       "      <td>0.508772</td>\n",
       "      <td>0.403509</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.456140</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "model    reference  claude-3-5-sonnet-20241022  claude-3-5-sonnet-20240620  \\\n",
       "pass@1         1.0                    0.636842                    0.580702   \n",
       "pass@5         1.0                    0.724937                    0.747911   \n",
       "pass@10        1.0                    0.754386                    0.789474   \n",
       "\n",
       "model    gpt-4o-2024-08-06  o1-mini-2024-09-12  gpt-4o-2024-05-13  \\\n",
       "pass@1            0.533333            0.531579           0.508772   \n",
       "pass@5            0.690337            0.697090           0.694932   \n",
       "pass@10           0.736842            0.754386           0.754386   \n",
       "\n",
       "model    gpt-4-turbo-2024-04-09  claude-3-opus-20240229  gpt-4-1106-preview  \\\n",
       "pass@1                 0.473684                0.470175            0.464912   \n",
       "pass@5                 0.686090                0.654135            0.678850   \n",
       "pass@10                0.736842                0.684211            0.719298   \n",
       "\n",
       "model    gemini-1.5-pro-001  ...  mixtral-8x22b-instruct-v0.1-q4_0  \\\n",
       "pass@1             0.459649  ...                          0.243860   \n",
       "pass@5             0.603801  ...                          0.514690   \n",
       "pass@10            0.649123  ...                          0.666667   \n",
       "\n",
       "model    mixtral-8x7b-instruct-v0.1-q5_0  phi3-3.8b-mini-instruct-4k-fp16  \\\n",
       "pass@1                          0.207018                         0.180702   \n",
       "pass@5                          0.485171                         0.421540   \n",
       "pass@10                         0.578947                         0.508772   \n",
       "\n",
       "model    codellama-70b-instruct-q4_0  gemini-pro  mistral-nemo  \\\n",
       "pass@1                      0.177193    0.173684      0.152632   \n",
       "pass@5                      0.497633    0.353523      0.359649   \n",
       "pass@10                     0.631579    0.438596      0.508772   \n",
       "\n",
       "model    llama3-8b-instruct-fp16  command-r-plus-104b-q4_0  codellama   k  \n",
       "pass@1                  0.142105                  0.122807   0.087719   1  \n",
       "pass@5                  0.336745                  0.384781   0.300543   5  \n",
       "pass@10                 0.403509                  0.526316   0.456140  10  \n",
       "\n",
       "[3 rows x 28 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the \"model\" column as the index and then transpose\n",
    "df_transposed = df.set_index('model').transpose()\n",
    "df_transposed['k'] = [1,5,10]\n",
    "df_transposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14bc43f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aae3266d-e23f-4231-ab82-866dc60d3249",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     pass@1    pass@5   pass@10                             model\n",
      "0  1.000000  1.000000  1.000000                         reference\n",
      "0  0.636842  0.724937  0.754386        claude-3-5-sonnet-20241022\n",
      "0  0.580702  0.747911  0.789474        claude-3-5-sonnet-20240620\n",
      "0  0.533333  0.690337  0.736842                 gpt-4o-2024-08-06\n",
      "0  0.531579  0.697090  0.754386                o1-mini-2024-09-12\n",
      "0  0.508772  0.694932  0.754386                 gpt-4o-2024-05-13\n",
      "0  0.473684  0.686090  0.736842            gpt-4-turbo-2024-04-09\n",
      "0  0.470175  0.654135  0.684211            claude-3-opus-20240229\n",
      "0  0.464912  0.678850  0.719298                gpt-4-1106-preview\n",
      "0  0.459649  0.603801  0.649123                gemini-1.5-pro-001\n",
      "0  0.454386  0.687552  0.771930      meta-llama-3.1-405b-instruct\n",
      "0  0.424561  0.625731  0.684211            gpt-4o-mini-2024-07-18\n",
      "0  0.368421  0.642161  0.719298                 deepseek-coder-v2\n",
      "0  0.359649  0.612016  0.719298          llama3-70b-instruct-q8_0\n",
      "0  0.350877  0.615915  0.719298          llama3-70b-instruct-q4_0\n",
      "0  0.333333  0.553676  0.701754                gpt-3.5-turbo-1106\n",
      "0  0.294737  0.448761  0.543860              gemini-1.5-flash-001\n",
      "0  0.249123  0.494291  0.578947        codegemma-7b-instruct-fp16\n",
      "0  0.243860  0.514690  0.666667  mixtral-8x22b-instruct-v0.1-q4_0\n",
      "0  0.207018  0.485171  0.578947   mixtral-8x7b-instruct-v0.1-q5_0\n",
      "0  0.180702  0.421540  0.508772   phi3-3.8b-mini-instruct-4k-fp16\n",
      "0  0.177193  0.497633  0.631579       codellama-70b-instruct-q4_0\n",
      "0  0.173684  0.353523  0.438596                        gemini-pro\n",
      "0  0.152632  0.359649  0.508772                      mistral-nemo\n",
      "0  0.142105  0.336745  0.403509           llama3-8b-instruct-fp16\n",
      "0  0.122807  0.384781  0.526316          command-r-plus-104b-q4_0\n",
      "0  0.087719  0.300543  0.456140                         codellama\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "\n",
    "# Assuming 'k' is an index after transposition, let's reset the index first if not already done\n",
    "if 'k' not in df_transposed.columns:\n",
    "    df_transposed.reset_index(inplace=True)\n",
    "print(df)\n",
    "# Separate 'k' column for x-axis\n",
    "x = df_transposed['k']\n",
    "vals = df_transposed.drop('k', axis=1)\n",
    "fig = plt.figure(figsize=(6,6))\n",
    "g = sns.lineplot(vals, palette=\"Set3\", dashes=False, linewidth=2)\n",
    "sns.despine(offset=10, trim=True);\n",
    "g.set_xticklabels(x)\n",
    "\n",
    "# Adding plot decorations\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Pass rate')\n",
    "plt.title('Pass rates depending on k')\n",
    "plt.legend(title='LLMs', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(\"../docs/paper/pass_k_llms_plot.png\", dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f4d7420a-71f0-41f5-8fa0-a0156e3230ad",
   "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>model</th>\n",
       "      <th>reference</th>\n",
       "      <th>claude-3-5-sonnet-20241022</th>\n",
       "      <th>claude-3-5-sonnet-20240620</th>\n",
       "      <th>gpt-4o-2024-08-06</th>\n",
       "      <th>o1-mini-2024-09-12</th>\n",
       "      <th>gpt-4o-2024-05-13</th>\n",
       "      <th>gpt-4-turbo-2024-04-09</th>\n",
       "      <th>claude-3-opus-20240229</th>\n",
       "      <th>gpt-4-1106-preview</th>\n",
       "      <th>gemini-1.5-pro-001</th>\n",
       "      <th>...</th>\n",
       "      <th>codegemma-7b-instruct-fp16</th>\n",
       "      <th>mixtral-8x22b-instruct-v0.1-q4_0</th>\n",
       "      <th>mixtral-8x7b-instruct-v0.1-q5_0</th>\n",
       "      <th>phi3-3.8b-mini-instruct-4k-fp16</th>\n",
       "      <th>codellama-70b-instruct-q4_0</th>\n",
       "      <th>gemini-pro</th>\n",
       "      <th>mistral-nemo</th>\n",
       "      <th>llama3-8b-instruct-fp16</th>\n",
       "      <th>command-r-plus-104b-q4_0</th>\n",
       "      <th>codellama</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pass@1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.636842</td>\n",
       "      <td>0.580702</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.531579</td>\n",
       "      <td>0.508772</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.470175</td>\n",
       "      <td>0.464912</td>\n",
       "      <td>0.459649</td>\n",
       "      <td>...</td>\n",
       "      <td>0.249123</td>\n",
       "      <td>0.243860</td>\n",
       "      <td>0.207018</td>\n",
       "      <td>0.180702</td>\n",
       "      <td>0.177193</td>\n",
       "      <td>0.173684</td>\n",
       "      <td>0.152632</td>\n",
       "      <td>0.142105</td>\n",
       "      <td>0.122807</td>\n",
       "      <td>0.087719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pass@5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.724937</td>\n",
       "      <td>0.747911</td>\n",
       "      <td>0.690337</td>\n",
       "      <td>0.697090</td>\n",
       "      <td>0.694932</td>\n",
       "      <td>0.686090</td>\n",
       "      <td>0.654135</td>\n",
       "      <td>0.678850</td>\n",
       "      <td>0.603801</td>\n",
       "      <td>...</td>\n",
       "      <td>0.494291</td>\n",
       "      <td>0.514690</td>\n",
       "      <td>0.485171</td>\n",
       "      <td>0.421540</td>\n",
       "      <td>0.497633</td>\n",
       "      <td>0.353523</td>\n",
       "      <td>0.359649</td>\n",
       "      <td>0.336745</td>\n",
       "      <td>0.384781</td>\n",
       "      <td>0.300543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pass@10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.754386</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.754386</td>\n",
       "      <td>0.754386</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.719298</td>\n",
       "      <td>0.649123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.508772</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.438596</td>\n",
       "      <td>0.508772</td>\n",
       "      <td>0.403509</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.456140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "model    reference  claude-3-5-sonnet-20241022  claude-3-5-sonnet-20240620  \\\n",
       "pass@1         1.0                    0.636842                    0.580702   \n",
       "pass@5         1.0                    0.724937                    0.747911   \n",
       "pass@10        1.0                    0.754386                    0.789474   \n",
       "\n",
       "model    gpt-4o-2024-08-06  o1-mini-2024-09-12  gpt-4o-2024-05-13  \\\n",
       "pass@1            0.533333            0.531579           0.508772   \n",
       "pass@5            0.690337            0.697090           0.694932   \n",
       "pass@10           0.736842            0.754386           0.754386   \n",
       "\n",
       "model    gpt-4-turbo-2024-04-09  claude-3-opus-20240229  gpt-4-1106-preview  \\\n",
       "pass@1                 0.473684                0.470175            0.464912   \n",
       "pass@5                 0.686090                0.654135            0.678850   \n",
       "pass@10                0.736842                0.684211            0.719298   \n",
       "\n",
       "model    gemini-1.5-pro-001  ...  codegemma-7b-instruct-fp16  \\\n",
       "pass@1             0.459649  ...                    0.249123   \n",
       "pass@5             0.603801  ...                    0.494291   \n",
       "pass@10            0.649123  ...                    0.578947   \n",
       "\n",
       "model    mixtral-8x22b-instruct-v0.1-q4_0  mixtral-8x7b-instruct-v0.1-q5_0  \\\n",
       "pass@1                           0.243860                         0.207018   \n",
       "pass@5                           0.514690                         0.485171   \n",
       "pass@10                          0.666667                         0.578947   \n",
       "\n",
       "model    phi3-3.8b-mini-instruct-4k-fp16  codellama-70b-instruct-q4_0  \\\n",
       "pass@1                          0.180702                     0.177193   \n",
       "pass@5                          0.421540                     0.497633   \n",
       "pass@10                         0.508772                     0.631579   \n",
       "\n",
       "model    gemini-pro  mistral-nemo  llama3-8b-instruct-fp16  \\\n",
       "pass@1     0.173684      0.152632                 0.142105   \n",
       "pass@5     0.353523      0.359649                 0.336745   \n",
       "pass@10    0.438596      0.508772                 0.403509   \n",
       "\n",
       "model    command-r-plus-104b-q4_0  codellama  \n",
       "pass@1                   0.122807   0.087719  \n",
       "pass@5                   0.384781   0.300543  \n",
       "pass@10                  0.526316   0.456140  \n",
       "\n",
       "[3 rows x 27 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transposed.drop('k', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f3592f5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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