demo/summarize_error_messages.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1413fb18-3c0e-4cb7-ba25-42d3eeae09c8",
   "metadata": {},
   "source": [
    "# Summarizing failure reasons\n",
    "This notebook demonstrates how one can dive into summarizing error messages and failure reasones. The `_result.jsonl` files contain a column `result`, which contains a string, that in case of test failure is \"failed: \" and in other cases contains additionally the error message that was observed. These failures and errors can be summarized for each model as shown here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da4ac394-a726-42c6-be64-be200c63bd13",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "45984da5-ba46-4c0c-b7d1-e842bd3e9725",
   "metadata": {},
   "outputs": [],
   "source": [
    "directory = \"../data/\"\n",
    "# if you want to investigate a single model only, add its name here:\n",
    "search_term = \"\"\n",
    "\n",
    "# Enter the terms to search for here\n",
    "common_errors = ['has no attribute', 'invalid syntax', 'is not defined', \"Can't convert object\", 'cannot import', 'out of range', 'unexpected keyword argument']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4166e77-38fa-4954-a9d7-0015efd47f8f",
   "metadata": {},
   "source": [
    "First we collect all results and the corresponding models from the jsonl files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c2abe33e-1d70-4ce4-b003-3923c4ff1fb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "collection = []\n",
    "for filename in os.listdir(directory):\n",
    "    if search_term in filename and filename.endswith(\"_results.jsonl\"):\n",
    "        df = pd.read_json(directory + filename, lines=True)\n",
    "        df['model'] = filename.replace(\"samples_\",\"\").replace(\"_results\",\"\").replace(\".jsonl\",\"\")\n",
    "        collection.append(df)\n",
    "\n",
    "if len(collection) == 1:\n",
    "    df = collection[0]\n",
    "else:\n",
    "    df = pd.concat(collection)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77f92b8d-5eb2-4d64-984d-bf703e78fed7",
   "metadata": {},
   "source": [
    "We then focus on the tests which failed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c688498a-6c20-454f-8c38-3168fe25bae5",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>task_id</th>\n",
       "      <th>completion</th>\n",
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       "      <th>passed</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>../test_cases/apply_otsu_threshold_and_count_p...</td>\n",
       "      <td>\\n# Plan:\\n# 1. Import required libraries (cv2...</td>\n",
       "      <td>Here's the completed code with a step-by-step ...</td>\n",
       "      <td>failed: OpenCV(4.9.0) D:\\a\\opencv-python\\openc...</td>\n",
       "      <td>False</td>\n",
       "      <td>claude-3-5-sonnet-20240620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>../test_cases/binary_skeleton.ipynb</td>\n",
       "      <td>\\n# Plan:\\n# 1. Import necessary libraries (sk...</td>\n",
       "      <td>Here's the completed code with a step-by-step ...</td>\n",
       "      <td>failed: Input must be a 2D numpy array of bool...</td>\n",
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       "      <td>claude-3-5-sonnet-20240620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>../test_cases/convolve_images.ipynb</td>\n",
       "      <td>\\n# Plan:\\n# 1. Check if the input images are ...</td>\n",
       "      <td>Here's the complete code with a plan, necessar...</td>\n",
       "      <td>failed: Input image and kernel image must have...</td>\n",
       "      <td>False</td>\n",
       "      <td>claude-3-5-sonnet-20240620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>../test_cases/deconvolve_image.ipynb</td>\n",
       "      <td>\\n# Plan:\\n# 1. Import required libraries (num...</td>\n",
       "      <td>Here's the completed code with a plan, necessa...</td>\n",
       "      <td>failed: fft2() got an unexpected keyword argum...</td>\n",
       "      <td>False</td>\n",
       "      <td>claude-3-5-sonnet-20240620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>../test_cases/detect_edges.ipynb</td>\n",
       "      <td>\\n# Plan:\\n# 1. Convert the image to grayscale...</td>\n",
       "      <td>Here's the completed code with a plan, necessa...</td>\n",
       "      <td>failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...</td>\n",
       "      <td>False</td>\n",
       "      <td>claude-3-5-sonnet-20240620</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>../test_cases/workflow_batch_process_folder_me...</td>\n",
       "      <td>\\nimport os\\nimport cv2  # for handling tif im...</td>\n",
       "      <td># Plan to solve the problem step by step:\\n1. ...</td>\n",
       "      <td>failed: 'label'</td>\n",
       "      <td>False</td>\n",
       "      <td>phi3-3.8b-mini-instruct-4k-fp16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>../test_cases/workflow_segmentation_counting.i...</td>\n",
       "      <td># Step-by-step plan:\\n1. Load the given `image...</td>\n",
       "      <td># Step-by-step plan:\\n1. Load the given `image...</td>\n",
       "      <td>failed: unterminated string literal (detected ...</td>\n",
       "      <td>False</td>\n",
       "      <td>phi3-3.8b-mini-instruct-4k-fp16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>../test_cases/workflow_segmentation_measuremen...</td>\n",
       "      <td>\\n# Step-by-step plan to solve the problem:\\n\\...</td>\n",
       "      <td>```python\\n# Step-by-step plan to solve the pr...</td>\n",
       "      <td>failed: OpenCV(4.9.0) D:\\a\\opencv-python\\openc...</td>\n",
       "      <td>False</td>\n",
       "      <td>phi3-3.8b-mini-instruct-4k-fp16</td>\n",
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       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>../test_cases/workflow_segment_measure_umap.ipynb</td>\n",
       "      <td>\\nfrom skimage import io, measure, morphology\\...</td>\n",
       "      <td>```python\\nfrom skimage import io, measure, mo...</td>\n",
       "      <td>failed: \"None of [Index(['Area', 'Perimeter', ...</td>\n",
       "      <td>False</td>\n",
       "      <td>phi3-3.8b-mini-instruct-4k-fp16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>../test_cases/workflow_watershed_segmentation_...</td>\n",
       "      <td>\\n# Libraries import\\nimport cv2\\nimport numpy...</td>\n",
       "      <td># Plan:\\n1. Import necessary libraries for ima...</td>\n",
       "      <td>failed: OpenCV(4.9.0) D:/a/opencv-python/openc...</td>\n",
       "      <td>False</td>\n",
       "      <td>phi3-3.8b-mini-instruct-4k-fp16</td>\n",
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       "<p>9700 rows × 6 columns</p>\n",
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      "text/plain": [
       "                                               task_id  \\\n",
       "0    ../test_cases/apply_otsu_threshold_and_count_p...   \n",
       "2                  ../test_cases/binary_skeleton.ipynb   \n",
       "6                  ../test_cases/convolve_images.ipynb   \n",
       "12                ../test_cases/deconvolve_image.ipynb   \n",
       "13                    ../test_cases/detect_edges.ipynb   \n",
       "..                                                 ...   \n",
       "565  ../test_cases/workflow_batch_process_folder_me...   \n",
       "566  ../test_cases/workflow_segmentation_counting.i...   \n",
       "567  ../test_cases/workflow_segmentation_measuremen...   \n",
       "568  ../test_cases/workflow_segment_measure_umap.ipynb   \n",
       "569  ../test_cases/workflow_watershed_segmentation_...   \n",
       "\n",
       "                                            completion  \\\n",
       "0    \\n# Plan:\\n# 1. Import required libraries (cv2...   \n",
       "2    \\n# Plan:\\n# 1. Import necessary libraries (sk...   \n",
       "6    \\n# Plan:\\n# 1. Check if the input images are ...   \n",
       "12   \\n# Plan:\\n# 1. Import required libraries (num...   \n",
       "13   \\n# Plan:\\n# 1. Convert the image to grayscale...   \n",
       "..                                                 ...   \n",
       "565  \\nimport os\\nimport cv2  # for handling tif im...   \n",
       "566  # Step-by-step plan:\\n1. Load the given `image...   \n",
       "567  \\n# Step-by-step plan to solve the problem:\\n\\...   \n",
       "568  \\nfrom skimage import io, measure, morphology\\...   \n",
       "569  \\n# Libraries import\\nimport cv2\\nimport numpy...   \n",
       "\n",
       "                                         full_response  \\\n",
       "0    Here's the completed code with a step-by-step ...   \n",
       "2    Here's the completed code with a step-by-step ...   \n",
       "6    Here's the complete code with a plan, necessar...   \n",
       "12   Here's the completed code with a plan, necessa...   \n",
       "13   Here's the completed code with a plan, necessa...   \n",
       "..                                                 ...   \n",
       "565  # Plan to solve the problem step by step:\\n1. ...   \n",
       "566  # Step-by-step plan:\\n1. Load the given `image...   \n",
       "567  ```python\\n# Step-by-step plan to solve the pr...   \n",
       "568  ```python\\nfrom skimage import io, measure, mo...   \n",
       "569  # Plan:\\n1. Import necessary libraries for ima...   \n",
       "\n",
       "                                                result  passed  \\\n",
       "0    failed: OpenCV(4.9.0) D:\\a\\opencv-python\\openc...   False   \n",
       "2    failed: Input must be a 2D numpy array of bool...   False   \n",
       "6    failed: Input image and kernel image must have...   False   \n",
       "12   failed: fft2() got an unexpected keyword argum...   False   \n",
       "13   failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...   False   \n",
       "..                                                 ...     ...   \n",
       "565                                    failed: 'label'   False   \n",
       "566  failed: unterminated string literal (detected ...   False   \n",
       "567  failed: OpenCV(4.9.0) D:\\a\\opencv-python\\openc...   False   \n",
       "568  failed: \"None of [Index(['Area', 'Perimeter', ...   False   \n",
       "569  failed: OpenCV(4.9.0) D:/a/opencv-python/openc...   False   \n",
       "\n",
       "                               model  \n",
       "0         claude-3-5-sonnet-20240620  \n",
       "2         claude-3-5-sonnet-20240620  \n",
       "6         claude-3-5-sonnet-20240620  \n",
       "12        claude-3-5-sonnet-20240620  \n",
       "13        claude-3-5-sonnet-20240620  \n",
       "..                               ...  \n",
       "565  phi3-3.8b-mini-instruct-4k-fp16  \n",
       "566  phi3-3.8b-mini-instruct-4k-fp16  \n",
       "567  phi3-3.8b-mini-instruct-4k-fp16  \n",
       "568  phi3-3.8b-mini-instruct-4k-fp16  \n",
       "569  phi3-3.8b-mini-instruct-4k-fp16  \n",
       "\n",
       "[9700 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['passed'] == False]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "20c4c64f-6612-425c-86af-e2a52f109ff9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"failed: OpenCV(4.9.0) D:\\\\a\\\\opencv-python\\\\opencv-python\\\\opencv\\\\modules\\\\imgproc\\\\src\\\\thresh.cpp:1555: error: (-2:Unspecified error) in function 'double __cdecl cv::threshold(const class cv::_InputArray &,const class cv::_OutputArray &,double,double,int)'\\n> THRESH_OTSU mode:\\n>     'src_type == CV_8UC1 || src_type == CV_16UC1'\\n> where\\n>     'src_type' is 4 (CV_32SC1)\\n\",\n",
       " 'failed: Input must be a 2D numpy array of boolean type',\n",
       " 'failed: Input image and kernel image must have the same dimensions',\n",
       " \"failed: fft2() got an unexpected keyword argument 's'\",\n",
       " \"failed: OpenCV(4.9.0) d:\\\\a\\\\opencv-python\\\\opencv-python\\\\opencv\\\\modules\\\\imgproc\\\\src\\\\color.simd_helpers.hpp:92: error: (-2:Unspecified error) in function '__cdecl cv::impl::`anonymous-namespace'::CvtHelper<struct cv::impl::`anonymous namespace'::Set<3,4,-1>,struct cv::impl::A0x59191d0d::Set<1,-1,-1>,struct cv::impl::A0x59191d0d::Set<0,2,5>,4>::CvtHelper(const class cv::_InputArray &,const class cv::_OutputArray &,int)'\\n> Invalid number of channels in input image:\\n>     'VScn::contains(scn)'\\n> where\\n>     'scn' is 1\\n\",\n",
       " 'failed: ',\n",
       " 'failed: ',\n",
       " 'failed: Input must be a numpy array of boolean type',\n",
       " 'failed: ',\n",
       " 'failed: ']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just some examples to get an idea\n",
    "df.head(10)['result'].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d134c4f6-0277-426a-ab80-81362aa08c5c",
   "metadata": {},
   "source": [
    "## Searching for common terms\n",
    "First, we search the error messages for common errors as specified above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a840bf73-6480-4b95-9ace-4097361fe0d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rober\\AppData\\Local\\Temp\\ipykernel_25076\\3576577103.py:7: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  error_counts = df.groupby('model').apply(count_errors, error_list=common_errors)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>model</th>\n",
       "      <th>claude-3-5-sonnet-20240620</th>\n",
       "      <th>claude-3-5-sonnet-20241022</th>\n",
       "      <th>claude-3-opus-20240229</th>\n",
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       "      <th>gemini-1.5-pro-001</th>\n",
       "      <th>...</th>\n",
       "      <th>gpt-4o-mini-2024-07-18</th>\n",
       "      <th>llama3-70b-instruct-q4_0</th>\n",
       "      <th>llama3-70b-instruct-q8_0</th>\n",
       "      <th>llama3-8b-instruct-fp16</th>\n",
       "      <th>meta-llama-3.1-405b-instruct</th>\n",
       "      <th>mistral-nemo</th>\n",
       "      <th>mixtral-8x22b-instruct-v0.1-q4_0</th>\n",
       "      <th>mixtral-8x7b-instruct-v0.1-q5_0</th>\n",
       "      <th>o1-mini-2024-09-12</th>\n",
       "      <th>phi3-3.8b-mini-instruct-4k-fp16</th>\n",
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       "      <td>86</td>\n",
       "      <td>32</td>\n",
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       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
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       "      <td>28</td>\n",
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       "      <td>27</td>\n",
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       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>cannot import</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>out of range</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unexpected keyword argument</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "model                        claude-3-5-sonnet-20240620  \\\n",
       "has no attribute                                     28   \n",
       "invalid syntax                                        0   \n",
       "is not defined                                        0   \n",
       "Can't convert object                                  0   \n",
       "cannot import                                         0   \n",
       "out of range                                          0   \n",
       "unexpected keyword argument                           8   \n",
       "\n",
       "model                        claude-3-5-sonnet-20241022  \\\n",
       "has no attribute                                     25   \n",
       "invalid syntax                                        0   \n",
       "is not defined                                        1   \n",
       "Can't convert object                                  0   \n",
       "cannot import                                         0   \n",
       "out of range                                          0   \n",
       "unexpected keyword argument                           8   \n",
       "\n",
       "model                        claude-3-opus-20240229  \\\n",
       "has no attribute                                 45   \n",
       "invalid syntax                                    0   \n",
       "is not defined                                    5   \n",
       "Can't convert object                              1   \n",
       "cannot import                                     5   \n",
       "out of range                                      3   \n",
       "unexpected keyword argument                       5   \n",
       "\n",
       "model                        codegemma-7b-instruct-fp16  codellama  \\\n",
       "has no attribute                                     49         59   \n",
       "invalid syntax                                        1         58   \n",
       "is not defined                                       86         32   \n",
       "Can't convert object                                  1         13   \n",
       "cannot import                                        15         17   \n",
       "out of range                                          5          4   \n",
       "unexpected keyword argument                           8          4   \n",
       "\n",
       "model                        codellama-70b-instruct-q4_0  \\\n",
       "has no attribute                                      41   \n",
       "invalid syntax                                        56   \n",
       "is not defined                                        36   \n",
       "Can't convert object                                  13   \n",
       "cannot import                                          7   \n",
       "out of range                                           4   \n",
       "unexpected keyword argument                            8   \n",
       "\n",
       "model                        command-r-plus-104b-q4_0  deepseek-coder-v2  \\\n",
       "has no attribute                                   25                 31   \n",
       "invalid syntax                                    167                  4   \n",
       "is not defined                                     45                  2   \n",
       "Can't convert object                                0                  0   \n",
       "cannot import                                       6                  7   \n",
       "out of range                                        1                  0   \n",
       "unexpected keyword argument                         0                 11   \n",
       "\n",
       "model                        gemini-1.5-flash-001  gemini-1.5-pro-001  ...  \\\n",
       "has no attribute                               28                  35  ...   \n",
       "invalid syntax                                  1                   0  ...   \n",
       "is not defined                                 35                  10  ...   \n",
       "Can't convert object                            0                   0  ...   \n",
       "cannot import                                  13                  11  ...   \n",
       "out of range                                   20                  11  ...   \n",
       "unexpected keyword argument                     2                   9  ...   \n",
       "\n",
       "model                        gpt-4o-mini-2024-07-18  llama3-70b-instruct-q4_0  \\\n",
       "has no attribute                                 43                        42   \n",
       "invalid syntax                                    2                         6   \n",
       "is not defined                                    4                         3   \n",
       "Can't convert object                              0                         0   \n",
       "cannot import                                     0                         2   \n",
       "out of range                                      0                         0   \n",
       "unexpected keyword argument                       4                         5   \n",
       "\n",
       "model                        llama3-70b-instruct-q8_0  \\\n",
       "has no attribute                                   38   \n",
       "invalid syntax                                      5   \n",
       "is not defined                                      1   \n",
       "Can't convert object                                0   \n",
       "cannot import                                      12   \n",
       "out of range                                        1   \n",
       "unexpected keyword argument                        10   \n",
       "\n",
       "model                        llama3-8b-instruct-fp16  \\\n",
       "has no attribute                                  60   \n",
       "invalid syntax                                    10   \n",
       "is not defined                                    12   \n",
       "Can't convert object                               0   \n",
       "cannot import                                     12   \n",
       "out of range                                       3   \n",
       "unexpected keyword argument                        4   \n",
       "\n",
       "model                        meta-llama-3.1-405b-instruct  mistral-nemo  \\\n",
       "has no attribute                                       46            59   \n",
       "invalid syntax                                          0            24   \n",
       "is not defined                                          3            28   \n",
       "Can't convert object                                    0             0   \n",
       "cannot import                                           4            19   \n",
       "out of range                                            3             4   \n",
       "unexpected keyword argument                             4             9   \n",
       "\n",
       "model                        mixtral-8x22b-instruct-v0.1-q4_0  \\\n",
       "has no attribute                                           58   \n",
       "invalid syntax                                              3   \n",
       "is not defined                                              5   \n",
       "Can't convert object                                        6   \n",
       "cannot import                                               4   \n",
       "out of range                                                1   \n",
       "unexpected keyword argument                                 3   \n",
       "\n",
       "model                        mixtral-8x7b-instruct-v0.1-q5_0  \\\n",
       "has no attribute                                          54   \n",
       "invalid syntax                                             7   \n",
       "is not defined                                            27   \n",
       "Can't convert object                                       3   \n",
       "cannot import                                              7   \n",
       "out of range                                               9   \n",
       "unexpected keyword argument                               13   \n",
       "\n",
       "model                        o1-mini-2024-09-12  \\\n",
       "has no attribute                             30   \n",
       "invalid syntax                                0   \n",
       "is not defined                                0   \n",
       "Can't convert object                          0   \n",
       "cannot import                                 2   \n",
       "out of range                                  0   \n",
       "unexpected keyword argument                   7   \n",
       "\n",
       "model                        phi3-3.8b-mini-instruct-4k-fp16  \n",
       "has no attribute                                          44  \n",
       "invalid syntax                                            30  \n",
       "is not defined                                            26  \n",
       "Can't convert object                                      12  \n",
       "cannot import                                              6  \n",
       "out of range                                               4  \n",
       "unexpected keyword argument                                8  \n",
       "\n",
       "[7 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the function to count errors\n",
    "def count_errors(group, error_list):\n",
    "    counts = {error: group['result'].str.contains(error, regex=False).sum() for error in error_list}\n",
    "    return pd.Series(counts)\n",
    "\n",
    "# Apply the function to each model group\n",
    "error_counts = df.groupby('model').apply(count_errors, error_list=common_errors)\n",
    "\n",
    "# Transpose the result for the desired format: models as columns, errors as rows\n",
    "error_counts = error_counts.T\n",
    "error_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3744e74f-c498-4685-b27c-1cb0bd76e989",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['claude-3-5-sonnet-20241022',\n",
       " 'claude-3-5-sonnet-20240620',\n",
       " 'gpt-4o-2024-08-06',\n",
       " 'o1-mini-2024-09-12',\n",
       " 'gpt-4o-2024-05-13',\n",
       " 'gpt-4-turbo-2024-04-09',\n",
       " 'claude-3-opus-20240229',\n",
       " 'gpt-4-1106-preview',\n",
       " 'gemini-1.5-pro-001',\n",
       " 'meta-llama-3.1-405b-instruct',\n",
       " 'gpt-4o-mini-2024-07-18',\n",
       " 'deepseek-coder-v2',\n",
       " 'llama3-70b-instruct-q8_0',\n",
       " 'llama3-70b-instruct-q4_0',\n",
       " 'gpt-3.5-turbo-1106',\n",
       " 'gemini-1.5-flash-001',\n",
       " 'codegemma-7b-instruct-fp16',\n",
       " 'mixtral-8x22b-instruct-v0.1-q4_0',\n",
       " 'mixtral-8x7b-instruct-v0.1-q5_0',\n",
       " 'phi3-3.8b-mini-instruct-4k-fp16',\n",
       " 'codellama-70b-instruct-q4_0',\n",
       " 'gemini-pro',\n",
       " 'mistral-nemo',\n",
       " 'llama3-8b-instruct-fp16',\n",
       " 'command-r-plus-104b-q4_0',\n",
       " 'codellama']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reorder columns to be similar to evaluation result plots\n",
    "with open(\"model_order.txt\", 'r') as f:\n",
    "    columns_in_order = f.readlines()\n",
    "    columns_in_order = [c.strip() for c in columns_in_order if \"reference\" not in c]\n",
    "columns_in_order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2cef3f79-8f60-435d-9b6d-59b9f546426a",
   "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>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>meta-llama-3.1-405b-instruct</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>has no attribute</th>\n",
       "      <td>25</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>45</td>\n",
       "      <td>48</td>\n",
       "      <td>35</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>49</td>\n",
       "      <td>58</td>\n",
       "      <td>54</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>43</td>\n",
       "      <td>59</td>\n",
       "      <td>60</td>\n",
       "      <td>25</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invalid syntax</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>167</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is not defined</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>86</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>36</td>\n",
       "      <td>203</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>45</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Can't convert object</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cannot import</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>out of range</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unexpected keyword argument</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "model                        claude-3-5-sonnet-20241022  \\\n",
       "has no attribute                                     25   \n",
       "invalid syntax                                        0   \n",
       "is not defined                                        1   \n",
       "Can't convert object                                  0   \n",
       "cannot import                                         0   \n",
       "out of range                                          0   \n",
       "unexpected keyword argument                           8   \n",
       "\n",
       "model                        claude-3-5-sonnet-20240620  gpt-4o-2024-08-06  \\\n",
       "has no attribute                                     28                 32   \n",
       "invalid syntax                                        0                  0   \n",
       "is not defined                                        0                  0   \n",
       "Can't convert object                                  0                  0   \n",
       "cannot import                                         0                  3   \n",
       "out of range                                          0                  0   \n",
       "unexpected keyword argument                           8                  8   \n",
       "\n",
       "model                        o1-mini-2024-09-12  gpt-4o-2024-05-13  \\\n",
       "has no attribute                             30                 33   \n",
       "invalid syntax                                0                  0   \n",
       "is not defined                                0                  0   \n",
       "Can't convert object                          0                  0   \n",
       "cannot import                                 2                  2   \n",
       "out of range                                  0                  1   \n",
       "unexpected keyword argument                   7                  5   \n",
       "\n",
       "model                        gpt-4-turbo-2024-04-09  claude-3-opus-20240229  \\\n",
       "has no attribute                                 33                      45   \n",
       "invalid syntax                                    0                       0   \n",
       "is not defined                                    4                       5   \n",
       "Can't convert object                              1                       1   \n",
       "cannot import                                     3                       5   \n",
       "out of range                                      0                       3   \n",
       "unexpected keyword argument                      15                       5   \n",
       "\n",
       "model                        gpt-4-1106-preview  gemini-1.5-pro-001  \\\n",
       "has no attribute                             48                  35   \n",
       "invalid syntax                                1                   0   \n",
       "is not defined                                8                  10   \n",
       "Can't convert object                          3                   0   \n",
       "cannot import                                 2                  11   \n",
       "out of range                                  0                  11   \n",
       "unexpected keyword argument                   8                   9   \n",
       "\n",
       "model                        meta-llama-3.1-405b-instruct  ...  \\\n",
       "has no attribute                                       46  ...   \n",
       "invalid syntax                                          0  ...   \n",
       "is not defined                                          3  ...   \n",
       "Can't convert object                                    0  ...   \n",
       "cannot import                                           4  ...   \n",
       "out of range                                            3  ...   \n",
       "unexpected keyword argument                             4  ...   \n",
       "\n",
       "model                        codegemma-7b-instruct-fp16  \\\n",
       "has no attribute                                     49   \n",
       "invalid syntax                                        1   \n",
       "is not defined                                       86   \n",
       "Can't convert object                                  1   \n",
       "cannot import                                        15   \n",
       "out of range                                          5   \n",
       "unexpected keyword argument                           8   \n",
       "\n",
       "model                        mixtral-8x22b-instruct-v0.1-q4_0  \\\n",
       "has no attribute                                           58   \n",
       "invalid syntax                                              3   \n",
       "is not defined                                              5   \n",
       "Can't convert object                                        6   \n",
       "cannot import                                               4   \n",
       "out of range                                                1   \n",
       "unexpected keyword argument                                 3   \n",
       "\n",
       "model                        mixtral-8x7b-instruct-v0.1-q5_0  \\\n",
       "has no attribute                                          54   \n",
       "invalid syntax                                             7   \n",
       "is not defined                                            27   \n",
       "Can't convert object                                       3   \n",
       "cannot import                                              7   \n",
       "out of range                                               9   \n",
       "unexpected keyword argument                               13   \n",
       "\n",
       "model                        phi3-3.8b-mini-instruct-4k-fp16  \\\n",
       "has no attribute                                          44   \n",
       "invalid syntax                                            30   \n",
       "is not defined                                            26   \n",
       "Can't convert object                                      12   \n",
       "cannot import                                              6   \n",
       "out of range                                               4   \n",
       "unexpected keyword argument                                8   \n",
       "\n",
       "model                        codellama-70b-instruct-q4_0  gemini-pro  \\\n",
       "has no attribute                                      41          43   \n",
       "invalid syntax                                        56           0   \n",
       "is not defined                                        36         203   \n",
       "Can't convert object                                  13           3   \n",
       "cannot import                                          7           6   \n",
       "out of range                                           4           0   \n",
       "unexpected keyword argument                            8           1   \n",
       "\n",
       "model                        mistral-nemo  llama3-8b-instruct-fp16  \\\n",
       "has no attribute                       59                       60   \n",
       "invalid syntax                         24                       10   \n",
       "is not defined                         28                       12   \n",
       "Can't convert object                    0                        0   \n",
       "cannot import                          19                       12   \n",
       "out of range                            4                        3   \n",
       "unexpected keyword argument             9                        4   \n",
       "\n",
       "model                        command-r-plus-104b-q4_0  codellama  \n",
       "has no attribute                                   25         59  \n",
       "invalid syntax                                    167         58  \n",
       "is not defined                                     45         32  \n",
       "Can't convert object                                0         13  \n",
       "cannot import                                       6         17  \n",
       "out of range                                        1          4  \n",
       "unexpected keyword argument                         0          4  \n",
       "\n",
       "[7 rows x 26 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_counts = error_counts[columns_in_order]\n",
    "error_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c3d6ff52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13.2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "print(sns.__version__)\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "\n",
    "# Separate the numerical data for plotting and the string column for y-axis labels\n",
    "numerical_data = error_counts.select_dtypes(include=[np.number])\n",
    "\n",
    "# Plotting\n",
    "fig = plt.figure(figsize=(8,4))\n",
    "#plot square heatmap entries\n",
    "sns.heatmap(numerical_data, annot=True, linewidths=1, fmt=\".0f\", cbar_kws={'shrink': 1, 'aspect': 2*16}, cmap=\"Blues\", robust=True)\n",
    "plt.gca().set_xlabel('')\n",
    "plt.gca().set_ylabel('')\n",
    "\n",
    "plt.savefig(\"../docs/paper/error_counts_heatmap.png\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "22921333-323a-4aed-85bc-ae95286b52bb",
   "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>\\rot{claude-3-5-sonnet-20241022}</th>\n",
       "      <th>\\rot{claude-3-5-sonnet-20240620}</th>\n",
       "      <th>\\rot{gpt-4o-2024-08-06}</th>\n",
       "      <th>\\rot{o1-mini-2024-09-12}</th>\n",
       "      <th>\\rot{gpt-4o-2024-05-13}</th>\n",
       "      <th>\\rot{gpt-4-turbo-2024-04-09}</th>\n",
       "      <th>\\rot{claude-3-opus-20240229}</th>\n",
       "      <th>\\rot{gpt-4-1106-preview}</th>\n",
       "      <th>\\rot{gemini-1.5-pro-001}</th>\n",
       "      <th>\\rot{meta-llama-3.1-405b-instruct}</th>\n",
       "      <th>...</th>\n",
       "      <th>\\rot{codegemma-7b-instruct-fp16}</th>\n",
       "      <th>\\rot{mixtral-8x22b-instruct-v0.1-q4_0}</th>\n",
       "      <th>\\rot{mixtral-8x7b-instruct-v0.1-q5_0}</th>\n",
       "      <th>\\rot{phi3-3.8b-mini-instruct-4k-fp16}</th>\n",
       "      <th>\\rot{codellama-70b-instruct-q4_0}</th>\n",
       "      <th>\\rot{gemini-pro}</th>\n",
       "      <th>\\rot{mistral-nemo}</th>\n",
       "      <th>\\rot{llama3-8b-instruct-fp16}</th>\n",
       "      <th>\\rot{command-r-plus-104b-q4_0}</th>\n",
       "      <th>\\rot{codellama}</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>has no attribute</th>\n",
       "      <td>25</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>45</td>\n",
       "      <td>48</td>\n",
       "      <td>35</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>49</td>\n",
       "      <td>58</td>\n",
       "      <td>54</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>43</td>\n",
       "      <td>59</td>\n",
       "      <td>60</td>\n",
       "      <td>25</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invalid syntax</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>167</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is not defined</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>86</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>36</td>\n",
       "      <td>203</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>45</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Can't convert object</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cannot import</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>out of range</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unexpected keyword argument</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             \\rot{claude-3-5-sonnet-20241022}  \\\n",
       "has no attribute                                           25   \n",
       "invalid syntax                                              0   \n",
       "is not defined                                              1   \n",
       "Can't convert object                                        0   \n",
       "cannot import                                               0   \n",
       "out of range                                                0   \n",
       "unexpected keyword argument                                 8   \n",
       "\n",
       "                             \\rot{claude-3-5-sonnet-20240620}  \\\n",
       "has no attribute                                           28   \n",
       "invalid syntax                                              0   \n",
       "is not defined                                              0   \n",
       "Can't convert object                                        0   \n",
       "cannot import                                               0   \n",
       "out of range                                                0   \n",
       "unexpected keyword argument                                 8   \n",
       "\n",
       "                             \\rot{gpt-4o-2024-08-06}  \\\n",
       "has no attribute                                  32   \n",
       "invalid syntax                                     0   \n",
       "is not defined                                     0   \n",
       "Can't convert object                               0   \n",
       "cannot import                                      3   \n",
       "out of range                                       0   \n",
       "unexpected keyword argument                        8   \n",
       "\n",
       "                             \\rot{o1-mini-2024-09-12}  \\\n",
       "has no attribute                                   30   \n",
       "invalid syntax                                      0   \n",
       "is not defined                                      0   \n",
       "Can't convert object                                0   \n",
       "cannot import                                       2   \n",
       "out of range                                        0   \n",
       "unexpected keyword argument                         7   \n",
       "\n",
       "                             \\rot{gpt-4o-2024-05-13}  \\\n",
       "has no attribute                                  33   \n",
       "invalid syntax                                     0   \n",
       "is not defined                                     0   \n",
       "Can't convert object                               0   \n",
       "cannot import                                      2   \n",
       "out of range                                       1   \n",
       "unexpected keyword argument                        5   \n",
       "\n",
       "                             \\rot{gpt-4-turbo-2024-04-09}  \\\n",
       "has no attribute                                       33   \n",
       "invalid syntax                                          0   \n",
       "is not defined                                          4   \n",
       "Can't convert object                                    1   \n",
       "cannot import                                           3   \n",
       "out of range                                            0   \n",
       "unexpected keyword argument                            15   \n",
       "\n",
       "                             \\rot{claude-3-opus-20240229}  \\\n",
       "has no attribute                                       45   \n",
       "invalid syntax                                          0   \n",
       "is not defined                                          5   \n",
       "Can't convert object                                    1   \n",
       "cannot import                                           5   \n",
       "out of range                                            3   \n",
       "unexpected keyword argument                             5   \n",
       "\n",
       "                             \\rot{gpt-4-1106-preview}  \\\n",
       "has no attribute                                   48   \n",
       "invalid syntax                                      1   \n",
       "is not defined                                      8   \n",
       "Can't convert object                                3   \n",
       "cannot import                                       2   \n",
       "out of range                                        0   \n",
       "unexpected keyword argument                         8   \n",
       "\n",
       "                             \\rot{gemini-1.5-pro-001}  \\\n",
       "has no attribute                                   35   \n",
       "invalid syntax                                      0   \n",
       "is not defined                                     10   \n",
       "Can't convert object                                0   \n",
       "cannot import                                      11   \n",
       "out of range                                       11   \n",
       "unexpected keyword argument                         9   \n",
       "\n",
       "                             \\rot{meta-llama-3.1-405b-instruct}  ...  \\\n",
       "has no attribute                                             46  ...   \n",
       "invalid syntax                                                0  ...   \n",
       "is not defined                                                3  ...   \n",
       "Can't convert object                                          0  ...   \n",
       "cannot import                                                 4  ...   \n",
       "out of range                                                  3  ...   \n",
       "unexpected keyword argument                                   4  ...   \n",
       "\n",
       "                             \\rot{codegemma-7b-instruct-fp16}  \\\n",
       "has no attribute                                           49   \n",
       "invalid syntax                                              1   \n",
       "is not defined                                             86   \n",
       "Can't convert object                                        1   \n",
       "cannot import                                              15   \n",
       "out of range                                                5   \n",
       "unexpected keyword argument                                 8   \n",
       "\n",
       "                             \\rot{mixtral-8x22b-instruct-v0.1-q4_0}  \\\n",
       "has no attribute                                                 58   \n",
       "invalid syntax                                                    3   \n",
       "is not defined                                                    5   \n",
       "Can't convert object                                              6   \n",
       "cannot import                                                     4   \n",
       "out of range                                                      1   \n",
       "unexpected keyword argument                                       3   \n",
       "\n",
       "                             \\rot{mixtral-8x7b-instruct-v0.1-q5_0}  \\\n",
       "has no attribute                                                54   \n",
       "invalid syntax                                                   7   \n",
       "is not defined                                                  27   \n",
       "Can't convert object                                             3   \n",
       "cannot import                                                    7   \n",
       "out of range                                                     9   \n",
       "unexpected keyword argument                                     13   \n",
       "\n",
       "                             \\rot{phi3-3.8b-mini-instruct-4k-fp16}  \\\n",
       "has no attribute                                                44   \n",
       "invalid syntax                                                  30   \n",
       "is not defined                                                  26   \n",
       "Can't convert object                                            12   \n",
       "cannot import                                                    6   \n",
       "out of range                                                     4   \n",
       "unexpected keyword argument                                      8   \n",
       "\n",
       "                             \\rot{codellama-70b-instruct-q4_0}  \\\n",
       "has no attribute                                            41   \n",
       "invalid syntax                                              56   \n",
       "is not defined                                              36   \n",
       "Can't convert object                                        13   \n",
       "cannot import                                                7   \n",
       "out of range                                                 4   \n",
       "unexpected keyword argument                                  8   \n",
       "\n",
       "                             \\rot{gemini-pro}  \\rot{mistral-nemo}  \\\n",
       "has no attribute                           43                  59   \n",
       "invalid syntax                              0                  24   \n",
       "is not defined                            203                  28   \n",
       "Can't convert object                        3                   0   \n",
       "cannot import                               6                  19   \n",
       "out of range                                0                   4   \n",
       "unexpected keyword argument                 1                   9   \n",
       "\n",
       "                             \\rot{llama3-8b-instruct-fp16}  \\\n",
       "has no attribute                                        60   \n",
       "invalid syntax                                          10   \n",
       "is not defined                                          12   \n",
       "Can't convert object                                     0   \n",
       "cannot import                                           12   \n",
       "out of range                                             3   \n",
       "unexpected keyword argument                              4   \n",
       "\n",
       "                             \\rot{command-r-plus-104b-q4_0}  \\rot{codellama}  \n",
       "has no attribute                                         25               59  \n",
       "invalid syntax                                          167               58  \n",
       "is not defined                                           45               32  \n",
       "Can't convert object                                      0               13  \n",
       "cannot import                                             6               17  \n",
       "out of range                                              1                4  \n",
       "unexpected keyword argument                               0                4  \n",
       "\n",
       "[7 rows x 26 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_counts.columns = [f\"\\\\rot{{{col}}}\" for col in error_counts.columns]\n",
    "error_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cd68df2-fcab-480e-8b09-dde4dd06b8d0",
   "metadata": {},
   "source": [
    "Next we save the dataframe as tex to disk, so that we can include it in the manuscript."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ca668df5-add1-476b-9dd1-15dd9c2dd24d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert DataFrame to TeX format string\n",
    "tex_str = error_counts.to_latex()\n",
    "\n",
    "# Define filename for the TeX file\n",
    "tex_filename = '../docs/paper/error_counts_table.tex'\n",
    "\n",
    "# Write the TeX format string to a file\n",
    "with open(tex_filename, 'w') as tex_file:\n",
    "    tex_file.write(tex_str)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "766f05eb-711b-442f-b571-0b59146f781f",
   "metadata": {},
   "source": [
    "## Most popular failure reasons\n",
    "Furthermore, we search for the three most observed reasons for failure. These might be either error messages, or in case the result is only `failed: ` this indicated that the tests were not passed, presumably because the tested function did not return the right result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "27b3e76c-1c26-45c9-9285-409e885b643c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Top1 Result</th>\n",
       "      <th>Top1 Count</th>\n",
       "      <th>Top2 Result</th>\n",
       "      <th>Top2 Count</th>\n",
       "      <th>Top3 Result</th>\n",
       "      <th>Top3 Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>claude-3-5-sonnet-20240620</td>\n",
       "      <td>failed:</td>\n",
       "      <td>149</td>\n",
       "      <td>failed: 'list' object has no attribute 'shape'</td>\n",
       "      <td>20</td>\n",
       "      <td>failed: OpenCV(4.9.0) D:\\a\\opencv-python\\openc...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>claude-3-5-sonnet-20241022</td>\n",
       "      <td>failed:</td>\n",
       "      <td>131</td>\n",
       "      <td>failed: 'list' object has no attribute 'shape'</td>\n",
       "      <td>18</td>\n",
       "      <td>failed: OpenCV(4.10.0) D:\\a\\opencv-python\\open...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>claude-3-opus-20240229</td>\n",
       "      <td>failed:</td>\n",
       "      <td>164</td>\n",
       "      <td>failed: 'list' object has no attribute 'shape'</td>\n",
       "      <td>23</td>\n",
       "      <td>failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>codegemma-7b-instruct-fp16</td>\n",
       "      <td>failed:</td>\n",
       "      <td>137</td>\n",
       "      <td>failed: name 'np' is not defined</td>\n",
       "      <td>61</td>\n",
       "      <td>failed: 'list' object has no attribute 'shape'</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>codellama</td>\n",
       "      <td>failed:</td>\n",
       "      <td>98</td>\n",
       "      <td>failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...</td>\n",
       "      <td>26</td>\n",
       "      <td>failed: invalid syntax (&lt;string&gt;, line 4)</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>codellama-70b-instruct-q4_0</td>\n",
       "      <td>failed:</td>\n",
       "      <td>109</td>\n",
       "      <td>failed: invalid syntax (&lt;string&gt;, line 4)</td>\n",
       "      <td>26</td>\n",
       "      <td>failed: invalid syntax (&lt;string&gt;, line 5)</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>command-r-plus-104b-q4_0</td>\n",
       "      <td>failed:</td>\n",
       "      <td>96</td>\n",
       "      <td>failed: invalid syntax (&lt;string&gt;, line 4)</td>\n",
       "      <td>73</td>\n",
       "      <td>failed: invalid syntax (&lt;string&gt;, line 5)</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>deepseek-coder-v2</td>\n",
       "      <td>failed:</td>\n",
       "      <td>166</td>\n",
       "      <td>failed: OpenCV(4.10.0) d:\\a\\opencv-python\\open...</td>\n",
       "      <td>16</td>\n",
       "      <td>failed: OpenCV(4.10.0) D:/a/opencv-python/open...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gemini-1.5-flash-001</td>\n",
       "      <td>failed:</td>\n",
       "      <td>166</td>\n",
       "      <td>failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...</td>\n",
       "      <td>37</td>\n",
       "      <td>failed: name 'np' is not defined</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gemini-1.5-pro-001</td>\n",
       "      <td>failed:</td>\n",
       "      <td>151</td>\n",
       "      <td>failed: 'list' object has no attribute 'shape'</td>\n",
       "      <td>26</td>\n",
       "      <td>failed: OpenCV(4.10.0) D:\\a\\opencv-python\\open...</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>gemini-pro</td>\n",
       "      <td>failed: name 'np' is not defined</td>\n",
       "      <td>152</td>\n",
       "      <td>failed:</td>\n",
       "      <td>96</td>\n",
       "      <td>failed: 'list' object has no attribute 'shape'</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gpt-3.5-turbo-1106</td>\n",
       "      <td>failed:</td>\n",
       "      <td>152</td>\n",
       "      <td>failed: OpenCV(4.9.0) D:\\a\\opencv-python\\openc...</td>\n",
       "      <td>19</td>\n",
       "      <td>failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gpt-4-1106-preview</td>\n",
       "      <td>failed:</td>\n",
       "      <td>141</td>\n",
       "      <td>failed: 'list' object has no attribute 'shape'</td>\n",
       "      <td>16</td>\n",
       "      <td>failed: 'numpy.ndarray' object has no attribut...</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>gpt-4-turbo-2024-04-09</td>\n",
       "      <td>failed:</td>\n",
       "      <td>153</td>\n",
       "      <td>failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...</td>\n",
       "      <td>15</td>\n",
       "      <td>failed: 'list' object has no attribute 'shape'</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>gpt-4o-2024-05-13</td>\n",
       "      <td>failed:</td>\n",
       "      <td>146</td>\n",
       "      <td>failed: 'list' object has no attribute 'shape'</td>\n",
       "      <td>21</td>\n",
       "      <td>failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>gpt-4o-2024-08-06</td>\n",
       "      <td>failed:</td>\n",
       "      <td>145</td>\n",
       "      <td>failed: 'list' object has no attribute 'shape'</td>\n",
       "      <td>26</td>\n",
       "      <td>failed: OpenCV(4.10.0) d:\\a\\opencv-python\\open...</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>gpt-4o-mini-2024-07-18</td>\n",
       "      <td>failed:</td>\n",
       "      <td>149</td>\n",
       "      <td>failed: 'list' object has no attribute 'shape'</td>\n",
       "      <td>21</td>\n",
       "      <td>failed: OpenCV(4.10.0) D:\\a\\opencv-python\\open...</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>llama3-70b-instruct-q4_0</td>\n",
       "      <td>failed:</td>\n",
       "      <td>162</td>\n",
       "      <td>failed: OpenCV(4.10.0) d:\\a\\opencv-python\\open...</td>\n",
       "      <td>22</td>\n",
       "      <td>failed: 'numpy.ndarray' object has no attribut...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>llama3-70b-instruct-q8_0</td>\n",
       "      <td>failed:</td>\n",
       "      <td>152</td>\n",
       "      <td>failed: OpenCV(4.10.0) d:\\a\\opencv-python\\open...</td>\n",
       "      <td>31</td>\n",
       "      <td>failed: OpenCV(4.10.0) :-1: error: (-5:Bad arg...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>llama3-8b-instruct-fp16</td>\n",
       "      <td>failed:</td>\n",
       "      <td>147</td>\n",
       "      <td>failed: OpenCV(4.10.0) d:\\a\\opencv-python\\open...</td>\n",
       "      <td>47</td>\n",
       "      <td>failed: OpenCV(4.10.0) :-1: error: (-5:Bad arg...</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>meta-llama-3.1-405b-instruct</td>\n",
       "      <td>failed:</td>\n",
       "      <td>166</td>\n",
       "      <td>failed: 'list' object has no attribute 'shape'</td>\n",
       "      <td>20</td>\n",
       "      <td>failed: 'numpy.ndarray' object has no attribut...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>mistral-nemo</td>\n",
       "      <td>failed:</td>\n",
       "      <td>127</td>\n",
       "      <td>failed: OpenCV(4.10.0) d:\\a\\opencv-python\\open...</td>\n",
       "      <td>23</td>\n",
       "      <td>failed: 'list' object has no attribute 'shape'</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>mixtral-8x22b-instruct-v0.1-q4_0</td>\n",
       "      <td>failed:</td>\n",
       "      <td>195</td>\n",
       "      <td>failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...</td>\n",
       "      <td>18</td>\n",
       "      <td>failed: ufunc 'isfinite' not supported for the...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>mixtral-8x7b-instruct-v0.1-q5_0</td>\n",
       "      <td>failed:</td>\n",
       "      <td>145</td>\n",
       "      <td>failed: name 'np' is not defined</td>\n",
       "      <td>22</td>\n",
       "      <td>failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>o1-mini-2024-09-12</td>\n",
       "      <td>failed:</td>\n",
       "      <td>169</td>\n",
       "      <td>failed: 'list' object has no attribute 'shape'</td>\n",
       "      <td>15</td>\n",
       "      <td>failed: OpenCV(4.10.0) d:\\a\\opencv-python\\open...</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>phi3-3.8b-mini-instruct-4k-fp16</td>\n",
       "      <td>failed:</td>\n",
       "      <td>128</td>\n",
       "      <td>failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...</td>\n",
       "      <td>23</td>\n",
       "      <td>failed: invalid syntax (&lt;string&gt;, line 5)</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Model                       Top1 Result  \\\n",
       "0         claude-3-5-sonnet-20240620                          failed:    \n",
       "1         claude-3-5-sonnet-20241022                          failed:    \n",
       "2             claude-3-opus-20240229                          failed:    \n",
       "3         codegemma-7b-instruct-fp16                          failed:    \n",
       "4                          codellama                          failed:    \n",
       "5        codellama-70b-instruct-q4_0                          failed:    \n",
       "6           command-r-plus-104b-q4_0                          failed:    \n",
       "7                  deepseek-coder-v2                          failed:    \n",
       "8               gemini-1.5-flash-001                          failed:    \n",
       "9                 gemini-1.5-pro-001                          failed:    \n",
       "10                        gemini-pro  failed: name 'np' is not defined   \n",
       "11                gpt-3.5-turbo-1106                          failed:    \n",
       "12                gpt-4-1106-preview                          failed:    \n",
       "13            gpt-4-turbo-2024-04-09                          failed:    \n",
       "14                 gpt-4o-2024-05-13                          failed:    \n",
       "15                 gpt-4o-2024-08-06                          failed:    \n",
       "16            gpt-4o-mini-2024-07-18                          failed:    \n",
       "17          llama3-70b-instruct-q4_0                          failed:    \n",
       "18          llama3-70b-instruct-q8_0                          failed:    \n",
       "19           llama3-8b-instruct-fp16                          failed:    \n",
       "20      meta-llama-3.1-405b-instruct                          failed:    \n",
       "21                      mistral-nemo                          failed:    \n",
       "22  mixtral-8x22b-instruct-v0.1-q4_0                          failed:    \n",
       "23   mixtral-8x7b-instruct-v0.1-q5_0                          failed:    \n",
       "24                o1-mini-2024-09-12                          failed:    \n",
       "25   phi3-3.8b-mini-instruct-4k-fp16                          failed:    \n",
       "\n",
       "    Top1 Count                                        Top2 Result  Top2 Count  \\\n",
       "0          149     failed: 'list' object has no attribute 'shape'          20   \n",
       "1          131     failed: 'list' object has no attribute 'shape'          18   \n",
       "2          164     failed: 'list' object has no attribute 'shape'          23   \n",
       "3          137                   failed: name 'np' is not defined          61   \n",
       "4           98  failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...          26   \n",
       "5          109          failed: invalid syntax (<string>, line 4)          26   \n",
       "6           96          failed: invalid syntax (<string>, line 4)          73   \n",
       "7          166  failed: OpenCV(4.10.0) d:\\a\\opencv-python\\open...          16   \n",
       "8          166  failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...          37   \n",
       "9          151     failed: 'list' object has no attribute 'shape'          26   \n",
       "10         152                                           failed:           96   \n",
       "11         152  failed: OpenCV(4.9.0) D:\\a\\opencv-python\\openc...          19   \n",
       "12         141     failed: 'list' object has no attribute 'shape'          16   \n",
       "13         153  failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...          15   \n",
       "14         146     failed: 'list' object has no attribute 'shape'          21   \n",
       "15         145     failed: 'list' object has no attribute 'shape'          26   \n",
       "16         149     failed: 'list' object has no attribute 'shape'          21   \n",
       "17         162  failed: OpenCV(4.10.0) d:\\a\\opencv-python\\open...          22   \n",
       "18         152  failed: OpenCV(4.10.0) d:\\a\\opencv-python\\open...          31   \n",
       "19         147  failed: OpenCV(4.10.0) d:\\a\\opencv-python\\open...          47   \n",
       "20         166     failed: 'list' object has no attribute 'shape'          20   \n",
       "21         127  failed: OpenCV(4.10.0) d:\\a\\opencv-python\\open...          23   \n",
       "22         195  failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...          18   \n",
       "23         145                   failed: name 'np' is not defined          22   \n",
       "24         169     failed: 'list' object has no attribute 'shape'          15   \n",
       "25         128  failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...          23   \n",
       "\n",
       "                                          Top3 Result  Top3 Count  \n",
       "0   failed: OpenCV(4.9.0) D:\\a\\opencv-python\\openc...          10  \n",
       "1   failed: OpenCV(4.10.0) D:\\a\\opencv-python\\open...          10  \n",
       "2   failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...          11  \n",
       "3      failed: 'list' object has no attribute 'shape'          13  \n",
       "4           failed: invalid syntax (<string>, line 4)          22  \n",
       "5           failed: invalid syntax (<string>, line 5)          15  \n",
       "6           failed: invalid syntax (<string>, line 5)          38  \n",
       "7   failed: OpenCV(4.10.0) D:/a/opencv-python/open...          11  \n",
       "8                    failed: name 'np' is not defined          29  \n",
       "9   failed: OpenCV(4.10.0) D:\\a\\opencv-python\\open...          13  \n",
       "10     failed: 'list' object has no attribute 'shape'          17  \n",
       "11  failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...          16  \n",
       "12  failed: 'numpy.ndarray' object has no attribut...          12  \n",
       "13     failed: 'list' object has no attribute 'shape'          14  \n",
       "14  failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...          12  \n",
       "15  failed: OpenCV(4.10.0) d:\\a\\opencv-python\\open...          12  \n",
       "16  failed: OpenCV(4.10.0) D:\\a\\opencv-python\\open...          18  \n",
       "17  failed: 'numpy.ndarray' object has no attribut...          16  \n",
       "18  failed: OpenCV(4.10.0) :-1: error: (-5:Bad arg...          14  \n",
       "19  failed: OpenCV(4.10.0) :-1: error: (-5:Bad arg...          32  \n",
       "20  failed: 'numpy.ndarray' object has no attribut...          14  \n",
       "21     failed: 'list' object has no attribute 'shape'          13  \n",
       "22  failed: ufunc 'isfinite' not supported for the...          14  \n",
       "23  failed: OpenCV(4.9.0) d:\\a\\opencv-python\\openc...          20  \n",
       "24  failed: OpenCV(4.10.0) d:\\a\\opencv-python\\open...          12  \n",
       "25          failed: invalid syntax (<string>, line 5)          15  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1: Group the DataFrame by 'model' and get the value counts of 'result'\n",
    "model_result_count = df.groupby('model')['result'].value_counts()\n",
    "\n",
    "# Step 2: Create an empty DataFrame to store the results\n",
    "model_top_results = []\n",
    "\n",
    "# Step 3: Loop through each group to get the three most common results per model\n",
    "for model, counts in model_result_count.groupby(level=0):\n",
    "    # Get the top three results (note: nlargest returns the results)\n",
    "    top_three = counts.nlargest(3)\n",
    "    # Prepare data to append to the DataFrame\n",
    "    data = {\n",
    "        'Model': model,\n",
    "        'Top1 Result': top_three.index.get_level_values(1)[0],\n",
    "        'Top1 Count': top_three.iloc[0],\n",
    "        'Top2 Result': top_three.index.get_level_values(1)[1] if len(top_three) > 1 else None,\n",
    "        'Top2 Count': top_three.iloc[1] if len(top_three) > 1 else None,\n",
    "        'Top3 Result': top_three.index.get_level_values(1)[2] if len(top_three) > 2 else None,\n",
    "        'Top3 Count': top_three.iloc[2] if len(top_three) > 2 else None\n",
    "    }\n",
    "    # Append data\n",
    "    model_top_results.append(data)\n",
    "\n",
    "# Display the resulting DataFrame\n",
    "most_common_errors = pd.DataFrame(model_top_results)\n",
    "most_common_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3efa529b-aa82-459a-a91d-32ab52652e6b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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