{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "058ab624-607b-4c6d-9608-554583bd05b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from aggme.aggregation import IntervalAggregation\n",
    "from aggme.utils import AnnotationData, Visualizer\n",
    "from aggme.utils.processing import abc_intervals_to_template"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5727a247-f0b3-4ebf-b00f-19dc609a219e",
   "metadata": {},
   "source": [
    "# Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b1beece-4df4-4818-8f5f-2d2e792f2d7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = AnnotationData(markup_type='interval')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1479acd-bf4c-4810-bf74-15f8bf0660e1",
   "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>INPUT:video</th>\n",
       "      <th>OUTPUT:video</th>\n",
       "      <th>ASSIGNMENT:user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>case_000_ov3_cl1</td>\n",
       "      <td>[{'begin':0.73027,'end':2.17368,'label':'inter...</td>\n",
       "      <td>user_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>case_000_ov3_cl1</td>\n",
       "      <td>[{'begin':0.85214,'end':2.12798,'label':'inter...</td>\n",
       "      <td>user_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>case_000_ov3_cl1</td>\n",
       "      <td>[{'begin':0.75699,'end':2.33033,'label':'inter...</td>\n",
       "      <td>user_3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        INPUT:video                                       OUTPUT:video  \\\n",
       "0  case_000_ov3_cl1  [{'begin':0.73027,'end':2.17368,'label':'inter...   \n",
       "1  case_000_ov3_cl1  [{'begin':0.85214,'end':2.12798,'label':'inter...   \n",
       "2  case_000_ov3_cl1  [{'begin':0.75699,'end':2.33033,'label':'inter...   \n",
       "\n",
       "  ASSIGNMENT:user_id  \n",
       "0             user_1  \n",
       "1             user_2  \n",
       "2             user_3  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./input_files/intervals_data.csv\")\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd900776-2a70-4624-ab4d-e5871fb791ea",
   "metadata": {},
   "source": [
    "# Check and visualize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "033ae649-24c4-478e-ae44-fa05d4c1b460",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MarkupGroup object\n",
       "name: case_000_ov3_cl1\n",
       "data_len: 3\n",
       "annotators_len: 3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.load_markups(df, abc_intervals_to_template)\n",
    "group = data.groups[0]\n",
    "group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a9713edd-3a0d-44d3-8010-498f3832f9cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis_group = Visualizer(group)\n",
    "vis_group.draw(group)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c74db3c-6667-429f-885f-85b31e67fa30",
   "metadata": {},
   "source": [
    "# Aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "20aba9c6-2a54-4a62-bdaa-c2cab3102a58",
   "metadata": {},
   "outputs": [],
   "source": [
    "aggregator = IntervalAggregation()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f23306d-4a79-40fe-8f91-384ef679aa5b",
   "metadata": {},
   "source": [
    "Total results of aggregation:\n",
    "- Every failed step sends markup group to next method in this order `['hard', 'drop', 'soft']`,\n",
    "- To make drop logic work, you shold have more than 3 markups on image and possibly a lower threshold/confidence value,\n",
    "- Every markup, that failed all the methods goes to `['fail']`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9571e1e4-0d8c-4f36-9699-ba3c1d4c43ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 14/14 [00:00<00:00, 61.58it/s]\n"
     ]
    }
   ],
   "source": [
    "data.load_markups(df, abc_intervals_to_template)\n",
    "results = aggregator.get_aggregation_results(data, threshold=0.7, confidence=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f6fd8104-7a62-4a80-8e81-4573fceebda7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['hard', 'drop', 'soft', 'fail'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2b55b455-fff5-4070-83b5-0d2752ba8a28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 4, 2, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(results['hard']), len(results['drop']), len(results['soft']), len(results['fail'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "67b7a447-538d-46f8-a7b8-cd8c2fefca04",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'hard': [(MarkupGroup object\n",
       "   name: case_000_ov3_cl1\n",
       "   data_len: 3\n",
       "   annotators_len: 3,\n",
       "   MarkupGroup object\n",
       "   name: case_000_ov3_cl1\n",
       "   data_len: 1\n",
       "   annotators_len: 1,\n",
       "   {'user_1', 'user_2', 'user_3'},\n",
       "   set()),\n",
       "  (MarkupGroup object\n",
       "   name: case_000_ov3_cl2\n",
       "   data_len: 6\n",
       "   annotators_len: 3,\n",
       "   MarkupGroup object\n",
       "   name: case_000_ov3_cl2\n",
       "   data_len: 2\n",
       "   annotators_len: 1,\n",
       "   {'user_1', 'user_2', 'user_3'},\n",
       "   set()),\n",
       "  (MarkupGroup object\n",
       "   name: case_005_ov4_cl1_rm_interval\n",
       "   data_len: 3\n",
       "   annotators_len: 3,\n",
       "   MarkupGroup object\n",
       "   name: case_005_ov4_cl1_rm_interval\n",
       "   data_len: 1\n",
       "   annotators_len: 1,\n",
       "   {'user_1', 'user_2', 'user_4'},\n",
       "   set()),\n",
       "  (MarkupGroup object\n",
       "   name: case_006_ov3_cl1_other_interval\n",
       "   data_len: 3\n",
       "   annotators_len: 3,\n",
       "   MarkupGroup object\n",
       "   name: case_006_ov3_cl1_other_interval\n",
       "   data_len: 1\n",
       "   annotators_len: 1,\n",
       "   {'user_1', 'user_2', 'user_3'},\n",
       "   set()),\n",
       "  (MarkupGroup object\n",
       "   name: case_006_ov4_cl1_other_interval\n",
       "   data_len: 4\n",
       "   annotators_len: 4,\n",
       "   MarkupGroup object\n",
       "   name: case_006_ov4_cl1_other_interval\n",
       "   data_len: 1\n",
       "   annotators_len: 1,\n",
       "   {'user_1', 'user_2', 'user_3', 'user_4'},\n",
       "   set())],\n",
       " 'drop': [(MarkupGroup object\n",
       "   name: case_001_ov4_cl1_bad_interval\n",
       "   data_len: 4\n",
       "   annotators_len: 4,\n",
       "   MarkupGroup object\n",
       "   name: case_001_ov4_cl1_bad_interval\n",
       "   data_len: 1\n",
       "   annotators_len: 1,\n",
       "   {'user_1', 'user_2', 'user_4'},\n",
       "   {'user_3'}),\n",
       "  (MarkupGroup object\n",
       "   name: case_002_ov4_cl1_bad_intervals\n",
       "   data_len: 5\n",
       "   annotators_len: 4,\n",
       "   MarkupGroup object\n",
       "   name: case_002_ov4_cl1_bad_intervals\n",
       "   data_len: 1\n",
       "   annotators_len: 1,\n",
       "   {'user_1', 'user_2', 'user_4'},\n",
       "   {'user_3'}),\n",
       "  (MarkupGroup object\n",
       "   name: case_003_ov4_cl1_add_interval\n",
       "   data_len: 5\n",
       "   annotators_len: 4,\n",
       "   MarkupGroup object\n",
       "   name: case_003_ov4_cl1_add_interval\n",
       "   data_len: 1\n",
       "   annotators_len: 1,\n",
       "   {'user_1', 'user_2', 'user_4'},\n",
       "   {'user_3'}),\n",
       "  (MarkupGroup object\n",
       "   name: case_004_ov4_cl1_add_small_interval\n",
       "   data_len: 5\n",
       "   annotators_len: 4,\n",
       "   MarkupGroup object\n",
       "   name: case_004_ov4_cl1_add_small_interval\n",
       "   data_len: 1\n",
       "   annotators_len: 1,\n",
       "   {'user_1', 'user_2', 'user_4'},\n",
       "   {'user_3'})],\n",
       " 'soft': [(MarkupGroup object\n",
       "   name: case_003_ov3_cl1_add_interval\n",
       "   data_len: 4\n",
       "   annotators_len: 3,\n",
       "   MarkupGroup object\n",
       "   name: case_003_ov3_cl1_add_interval\n",
       "   data_len: 1\n",
       "   annotators_len: 1,\n",
       "   {'user_1', 'user_2', 'user_3'},\n",
       "   []),\n",
       "  (MarkupGroup object\n",
       "   name: case_004_ov3_cl1_add_small_interval\n",
       "   data_len: 4\n",
       "   annotators_len: 3,\n",
       "   MarkupGroup object\n",
       "   name: case_004_ov3_cl1_add_small_interval\n",
       "   data_len: 1\n",
       "   annotators_len: 1,\n",
       "   {'user_1', 'user_2'},\n",
       "   {'user_3'})],\n",
       " 'fail': [(MarkupGroup object\n",
       "   name: case_001_ov3_cl1_bad_interval\n",
       "   data_len: 3\n",
       "   annotators_len: 3,\n",
       "   None,\n",
       "   None,\n",
       "   None),\n",
       "  (MarkupGroup object\n",
       "   name: case_002_ov3_cl1_bad_intervals\n",
       "   data_len: 4\n",
       "   annotators_len: 3,\n",
       "   None,\n",
       "   None,\n",
       "   None),\n",
       "  (MarkupGroup object\n",
       "   name: case_005_ov3_cl1_rm_interval\n",
       "   data_len: 2\n",
       "   annotators_len: 2,\n",
       "   None,\n",
       "   None,\n",
       "   None)]}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9584b0b4-2de7-402a-b4a0-6ce57f298598",
   "metadata": {},
   "source": [
    "# Visualise results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31888a2e-f5ad-463a-93d9-984b64c1e0fa",
   "metadata": {},
   "source": [
    "<font color='green'> Green </font> color at the top of image shows that annotators are __correct__. <font color=\"red\">Red</font> color will be showing otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c99f3691-4afa-46c5-b658-e822fff1b261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Example 0.1\n",
    "#    Overlap: 3\n",
    "#    Number of classes: 1\n",
    "#    Method: HARD\n",
    "#    Result: GOOD\n",
    "\n",
    "method, group = aggregator.get_group_by_name(results, 'case_000_ov3_cl1')\n",
    "original_group, result_group, accepted, rejected = group\n",
    "vis = Visualizer(original_group)\n",
    "vis.draw(\n",
    "    data = original_group,\n",
    "    result = result_group,\n",
    "    accepted = accepted,\n",
    "    rejected = rejected\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a7095dfb-41c2-4c6e-9be7-c25e854e10f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Example 0.2\n",
    "#    Overlap: 3\n",
    "#    Number of classes: 2\n",
    "#    Method: HARD\n",
    "#    Result: GOOD\n",
    "\n",
    "method, group = aggregator.get_group_by_name(results, 'case_000_ov3_cl2')\n",
    "original_group, result_group, accepted, rejected = group\n",
    "vis = Visualizer(original_group)\n",
    "vis.draw(\n",
    "    data = original_group,\n",
    "    result = result_group,\n",
    "    accepted = accepted,\n",
    "    rejected = rejected\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d6fcbf30-a386-4dab-8759-e111e0209d2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Example 1\n",
    "#    Overlap: 3\n",
    "#    Number of classes: 1\n",
    "#    Method: HARD\n",
    "#    Result: BAD\n",
    "#\n",
    "#  We had 3 markups on image, but user_3 added bad interval and the hard-method do not work.\n",
    "#  We requested additional markup and hard-method worked.\n",
    "\n",
    "method, group = aggregator.get_group_by_name(results, 'case_001_ov3_cl1_bad_interval')\n",
    "original_group, result_group, accepted, rejected = group\n",
    "vis = Visualizer(original_group)\n",
    "vis.draw(\n",
    "    data = original_group,\n",
    "    result = result_group,\n",
    "    accepted = accepted,\n",
    "    rejected = rejected\n",
    ")\n",
    "method, group = aggregator.get_group_by_name(results, 'case_001_ov4_cl1_bad_interval')\n",
    "original_group, result_group, accepted, rejected = group\n",
    "vis = Visualizer(original_group)\n",
    "vis.draw(\n",
    "    data = original_group,\n",
    "    result = result_group,\n",
    "    accepted = accepted,\n",
    "    rejected = rejected\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "22cf7631-bcab-4c5a-ac1d-b9bb8a4cb25e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Example 2\n",
    "#    Overlap: 3\n",
    "#    Number of classes: 1\n",
    "#    Method: HARD\n",
    "#    Result: BAD\n",
    "#\n",
    "#  We had 3 markups on image, but user_3 added bad intervals and the hard-method do not work.\n",
    "#  We requested additional markup and hard-method worked.\n",
    "\n",
    "method, group = aggregator.get_group_by_name(results, 'case_002_ov3_cl1_bad_intervals')\n",
    "original_group, result_group, accepted, rejected = group\n",
    "vis = Visualizer(original_group)\n",
    "vis.draw(\n",
    "    data = original_group,\n",
    "    result = result_group,\n",
    "    accepted = accepted,\n",
    "    rejected = rejected\n",
    ")\n",
    "method, group = aggregator.get_group_by_name(results, 'case_002_ov4_cl1_bad_intervals')\n",
    "original_group, result_group, accepted, rejected = group\n",
    "vis = Visualizer(original_group)\n",
    "vis.draw(\n",
    "    data = original_group,\n",
    "    result = result_group,\n",
    "    accepted = accepted,\n",
    "    rejected = rejected\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2444c429-a229-4ca2-9fa0-09c6d629b39d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Example 3\n",
    "#    Overlap: 3\n",
    "#    Number of classes: 1\n",
    "#    Method: SOFT\n",
    "#    Result: GOOD\n",
    "#\n",
    "#  We had 3 markups on image, but user_3 added extra interval and the hard-method do not work, but soft-method worked.\n",
    "#  We requested additional markup and hard-method worked after dropping one error markup.\n",
    "\n",
    "method, group = aggregator.get_group_by_name(results, 'case_003_ov3_cl1_add_interval')\n",
    "original_group, result_group, accepted, rejected = group\n",
    "vis = Visualizer(original_group)\n",
    "vis.draw(\n",
    "    data = original_group,\n",
    "    result = result_group,\n",
    "    accepted = accepted,\n",
    "    rejected = rejected\n",
    ")\n",
    "method, group = aggregator.get_group_by_name(results, 'case_003_ov4_cl1_add_interval')\n",
    "original_group, result_group, accepted, rejected = group\n",
    "vis = Visualizer(original_group)\n",
    "vis.draw(\n",
    "    data = original_group,\n",
    "    result = result_group,\n",
    "    accepted = accepted,\n",
    "    rejected = rejected\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0ac62c9b-dfcb-45f6-823a-8a5ee7ad816b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Example 4\n",
    "#    Overlap: 3\n",
    "#    Number of classes: 1\n",
    "#    Method: SOFT\n",
    "#    Result: GOOD\n",
    "#\n",
    "#  We had 3 markups on image, but user_3 added extra small interval and the hard-method do not work, but soft-method worked.\n",
    "#  We requested additional markup and hard-method worked after dropping one error markup.\n",
    "\n",
    "method, group = aggregator.get_group_by_name(results, 'case_004_ov3_cl1_add_small_interval')\n",
    "original_group, result_group, accepted, rejected = group\n",
    "vis = Visualizer(original_group)\n",
    "vis.draw(\n",
    "    data = original_group,\n",
    "    result = result_group,\n",
    "    accepted = accepted,\n",
    "    rejected = rejected\n",
    ")\n",
    "method, group = aggregator.get_group_by_name(results, 'case_004_ov4_cl1_add_small_interval')\n",
    "original_group, result_group, accepted, rejected = group\n",
    "vis = Visualizer(original_group)\n",
    "vis.draw(\n",
    "    data = original_group,\n",
    "    result = result_group,\n",
    "    accepted = accepted,\n",
    "    rejected = rejected\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "42c4d5e1-c315-4065-be60-717d7a884582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Example 5\n",
    "#    Overlap: 3\n",
    "#    Number of classes: 1\n",
    "#    Method: HARD\n",
    "#    Result: BAD\n",
    "#\n",
    "#  We had 3 markups on image, but user_3 added empty markup and the hard-method do not work.\n",
    "#  We requested additional markup and hard-method worked.\n",
    "\n",
    "method, group = aggregator.get_group_by_name(results, 'case_005_ov3_cl1_rm_interval')\n",
    "original_group, result_group, accepted, rejected = group\n",
    "vis = Visualizer(original_group)\n",
    "vis.draw(\n",
    "    data = original_group,\n",
    "    result = result_group,\n",
    "    accepted = accepted,\n",
    "    rejected = rejected\n",
    ")\n",
    "method, group = aggregator.get_group_by_name(results, 'case_005_ov4_cl1_rm_interval')\n",
    "original_group, result_group, accepted, rejected = group\n",
    "vis = Visualizer(original_group)\n",
    "vis.draw(\n",
    "    data = original_group,\n",
    "    result = result_group,\n",
    "    accepted = accepted,\n",
    "    rejected = rejected\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c20563e9-a9ba-4fb7-afd5-a08be4177348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Example 6\n",
    "#    Overlap: 3\n",
    "#    Number of classes: 1\n",
    "#    Method: HARD\n",
    "#    Result: GOOD\n",
    "#\n",
    "#  We had 3 markups on image, but user_3 added other interval and the hard-method worked.\n",
    "#  We requested additional markup and hard-method worked again.\n",
    "\n",
    "method, group = aggregator.get_group_by_name(results, 'case_006_ov3_cl1_other_interval')\n",
    "original_group, result_group, accepted, rejected = group\n",
    "vis = Visualizer(original_group)\n",
    "vis.draw(\n",
    "    data = original_group,\n",
    "    result = result_group,\n",
    "    accepted = accepted,\n",
    "    rejected = rejected\n",
    ")\n",
    "method, group = aggregator.get_group_by_name(results, 'case_006_ov4_cl1_other_interval')\n",
    "original_group, result_group, accepted, rejected = group\n",
    "vis = Visualizer(original_group)\n",
    "vis.draw(\n",
    "    data = original_group,\n",
    "    result = result_group,\n",
    "    accepted = accepted,\n",
    "    rejected = rejected\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "182315b1-1963-4610-bf05-1c382c825526",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}