visualize_performance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import plotly.express as px\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>image_id</th>\n",
       "      <th>dataset</th>\n",
       "      <th>tumor</th>\n",
       "      <th>dataset_tumor</th>\n",
       "      <th>iou</th>\n",
       "      <th>dice</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>area_label</th>\n",
       "      <th>area_pred</th>\n",
       "      <th>training</th>\n",
       "      <th>testing</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>BUSIS</td>\n",
       "      <td>benign</td>\n",
       "      <td>BUSIS_benign</td>\n",
       "      <td>0.913688</td>\n",
       "      <td>0.954897</td>\n",
       "      <td>0.926265</td>\n",
       "      <td>0.985356</td>\n",
       "      <td>38310.0</td>\n",
       "      <td>40754.0</td>\n",
       "      <td>baseline</td>\n",
       "      <td>default</td>\n",
       "      <td>mit-b3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>BUSIS</td>\n",
       "      <td>malignant</td>\n",
       "      <td>BUSIS_malignant</td>\n",
       "      <td>0.874532</td>\n",
       "      <td>0.933067</td>\n",
       "      <td>0.883836</td>\n",
       "      <td>0.988106</td>\n",
       "      <td>60954.0</td>\n",
       "      <td>68145.0</td>\n",
       "      <td>baseline</td>\n",
       "      <td>default</td>\n",
       "      <td>mit-b3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>BUSIS</td>\n",
       "      <td>malignant</td>\n",
       "      <td>BUSIS_malignant</td>\n",
       "      <td>0.809384</td>\n",
       "      <td>0.894651</td>\n",
       "      <td>0.906286</td>\n",
       "      <td>0.883312</td>\n",
       "      <td>17791.0</td>\n",
       "      <td>17340.0</td>\n",
       "      <td>baseline</td>\n",
       "      <td>default</td>\n",
       "      <td>mit-b3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>BUSIS</td>\n",
       "      <td>benign</td>\n",
       "      <td>BUSIS_benign</td>\n",
       "      <td>0.880153</td>\n",
       "      <td>0.936257</td>\n",
       "      <td>0.987369</td>\n",
       "      <td>0.890175</td>\n",
       "      <td>20988.0</td>\n",
       "      <td>18922.0</td>\n",
       "      <td>baseline</td>\n",
       "      <td>default</td>\n",
       "      <td>mit-b3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>BUSIS</td>\n",
       "      <td>malignant</td>\n",
       "      <td>BUSIS_malignant</td>\n",
       "      <td>0.930291</td>\n",
       "      <td>0.963887</td>\n",
       "      <td>0.969557</td>\n",
       "      <td>0.958283</td>\n",
       "      <td>62780.0</td>\n",
       "      <td>62050.0</td>\n",
       "      <td>baseline</td>\n",
       "      <td>default</td>\n",
       "      <td>mit-b3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16141</th>\n",
       "      <td>1789</td>\n",
       "      <td>Mayo</td>\n",
       "      <td>benign</td>\n",
       "      <td>Mayo_benign</td>\n",
       "      <td>0.884401</td>\n",
       "      <td>0.938655</td>\n",
       "      <td>0.933130</td>\n",
       "      <td>0.944245</td>\n",
       "      <td>9739.0</td>\n",
       "      <td>9855.0</td>\n",
       "      <td>clicktrained</td>\n",
       "      <td>with_clicks</td>\n",
       "      <td>mit-b5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16142</th>\n",
       "      <td>1790</td>\n",
       "      <td>Mayo</td>\n",
       "      <td>malignant</td>\n",
       "      <td>Mayo_malignant</td>\n",
       "      <td>0.710409</td>\n",
       "      <td>0.830689</td>\n",
       "      <td>0.995738</td>\n",
       "      <td>0.712576</td>\n",
       "      <td>4262.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>clicktrained</td>\n",
       "      <td>with_clicks</td>\n",
       "      <td>mit-b5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16143</th>\n",
       "      <td>1791</td>\n",
       "      <td>Mayo</td>\n",
       "      <td>benign</td>\n",
       "      <td>Mayo_benign</td>\n",
       "      <td>0.812118</td>\n",
       "      <td>0.896319</td>\n",
       "      <td>0.863986</td>\n",
       "      <td>0.931166</td>\n",
       "      <td>1569.0</td>\n",
       "      <td>1691.0</td>\n",
       "      <td>clicktrained</td>\n",
       "      <td>with_clicks</td>\n",
       "      <td>mit-b5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16144</th>\n",
       "      <td>1792</td>\n",
       "      <td>Mayo</td>\n",
       "      <td>benign</td>\n",
       "      <td>Mayo_benign</td>\n",
       "      <td>0.778532</td>\n",
       "      <td>0.875477</td>\n",
       "      <td>0.970769</td>\n",
       "      <td>0.797220</td>\n",
       "      <td>3166.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>clicktrained</td>\n",
       "      <td>with_clicks</td>\n",
       "      <td>mit-b5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16145</th>\n",
       "      <td>1793</td>\n",
       "      <td>Mayo</td>\n",
       "      <td>benign</td>\n",
       "      <td>Mayo_benign</td>\n",
       "      <td>0.790461</td>\n",
       "      <td>0.882969</td>\n",
       "      <td>0.976826</td>\n",
       "      <td>0.805567</td>\n",
       "      <td>2407.0</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>clicktrained</td>\n",
       "      <td>with_clicks</td>\n",
       "      <td>mit-b5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16146 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       image_id dataset      tumor    dataset_tumor       iou      dice  \\\n",
       "0             0   BUSIS     benign     BUSIS_benign  0.913688  0.954897   \n",
       "1             1   BUSIS  malignant  BUSIS_malignant  0.874532  0.933067   \n",
       "2             2   BUSIS  malignant  BUSIS_malignant  0.809384  0.894651   \n",
       "3             3   BUSIS     benign     BUSIS_benign  0.880153  0.936257   \n",
       "4             4   BUSIS  malignant  BUSIS_malignant  0.930291  0.963887   \n",
       "...         ...     ...        ...              ...       ...       ...   \n",
       "16141      1789    Mayo     benign      Mayo_benign  0.884401  0.938655   \n",
       "16142      1790    Mayo  malignant   Mayo_malignant  0.710409  0.830689   \n",
       "16143      1791    Mayo     benign      Mayo_benign  0.812118  0.896319   \n",
       "16144      1792    Mayo     benign      Mayo_benign  0.778532  0.875477   \n",
       "16145      1793    Mayo     benign      Mayo_benign  0.790461  0.882969   \n",
       "\n",
       "       precision    recall  area_label  area_pred      training      testing  \\\n",
       "0       0.926265  0.985356     38310.0    40754.0      baseline      default   \n",
       "1       0.883836  0.988106     60954.0    68145.0      baseline      default   \n",
       "2       0.906286  0.883312     17791.0    17340.0      baseline      default   \n",
       "3       0.987369  0.890175     20988.0    18922.0      baseline      default   \n",
       "4       0.969557  0.958283     62780.0    62050.0      baseline      default   \n",
       "...          ...       ...         ...        ...           ...          ...   \n",
       "16141   0.933130  0.944245      9739.0     9855.0  clicktrained  with_clicks   \n",
       "16142   0.995738  0.712576      4262.0     3050.0  clicktrained  with_clicks   \n",
       "16143   0.863986  0.931166      1569.0     1691.0  clicktrained  with_clicks   \n",
       "16144   0.970769  0.797220      3166.0     2600.0  clicktrained  with_clicks   \n",
       "16145   0.976826  0.805567      2407.0     1985.0  clicktrained  with_clicks   \n",
       "\n",
       "        model  \n",
       "0      mit-b3  \n",
       "1      mit-b3  \n",
       "2      mit-b3  \n",
       "3      mit-b3  \n",
       "4      mit-b3  \n",
       "...       ...  \n",
       "16141  mit-b5  \n",
       "16142  mit-b5  \n",
       "16143  mit-b5  \n",
       "16144  mit-b5  \n",
       "16145  mit-b5  \n",
       "\n",
       "[16146 rows x 13 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv(\"train_df.csv\", index_col=0)\n",
    "image_performance = None\n",
    "\n",
    "for training in [\"baseline\", \"clicktrained\"]:\n",
    "    for testing in [\"default\", \"without_clicks\", \"with_clicks\"]:\n",
    "        if training == \"baseline\" and testing != \"default\":\n",
    "            continue\n",
    "        if training == \"clicktrained\" and testing == \"default\":\n",
    "            continue\n",
    "        for model in [\"mit-b3\", \"mit-b4\", \"mit-b5\"]:\n",
    "            model_path = \"model_\" + training + \"_\" + model\n",
    "            if training == \"baseline\":\n",
    "                df = pd.read_csv(model_path + \"/image_perf_df.csv\")\n",
    "                df.rename(columns={df.columns[0]: \"image_id\"}, inplace=True)\n",
    "            else:\n",
    "                # this part was not summarized in training :(\n",
    "                # let's summarize it now\n",
    "                predictions_path = model_path + \"/predictions_clicks_train\"\n",
    "                if testing == \"with_clicks\":\n",
    "                    predictions_path += \"_test\"\n",
    "                performance_metrics_file = (\n",
    "                    predictions_path + \"/performance_metrics.json\"\n",
    "                )\n",
    "                with open(performance_metrics_file, \"r\") as pfile:\n",
    "                    performance_metrics = json.load(pfile)\n",
    "                    performance_metrics = {\n",
    "                        int(k): v for k, v in performance_metrics.items()\n",
    "                    }\n",
    "\n",
    "                df = train_df[[\"dataset\", \"tumor\", \"dataset_tumor\"]].copy()\n",
    "                df[\"iou\"] = [\n",
    "                    performance_metrics[x][\"per_category_niou\"][1]\n",
    "                    for x in range(len(performance_metrics.keys()))\n",
    "                ]\n",
    "                df[\"dice\"] = [\n",
    "                    performance_metrics[x][\"per_category_dice\"][1]\n",
    "                    for x in range(len(performance_metrics.keys()))\n",
    "                ]\n",
    "                df[\"precision\"] = [\n",
    "                    performance_metrics[x][\"per_category_precision\"][1]\n",
    "                    for x in range(len(performance_metrics.keys()))\n",
    "                ]\n",
    "                df[\"recall\"] = [\n",
    "                    performance_metrics[x][\"per_category_recall\"][1]\n",
    "                    for x in range(len(performance_metrics.keys()))\n",
    "                ]\n",
    "                df[\"area_label\"] = [\n",
    "                    performance_metrics[x][\"total_area_label\"][1]\n",
    "                    for x in range(len(performance_metrics.keys()))\n",
    "                ]\n",
    "                df[\"area_pred\"] = [\n",
    "                    performance_metrics[x][\"total_area_pred\"][1]\n",
    "                    for x in range(len(performance_metrics.keys()))\n",
    "                ]\n",
    "                df.reset_index(inplace=True, names=[\"image_id\"])\n",
    "\n",
    "            df[\"training\"] = [training] * df.shape[0]\n",
    "            df[\"testing\"] = [testing] * df.shape[0]\n",
    "            df[\"model\"] = [model] * df.shape[0]\n",
    "\n",
    "            if image_performance is None:\n",
    "                image_performance = df.copy(deep=True)\n",
    "            else:\n",
    "                image_performance = pd.concat(\n",
    "                    [image_performance, df], ignore_index=True, copy=True\n",
    "                )\n",
    "\n",
    "image_performance.to_csv(\"image_performance.csv\", index=True)\n",
    "image_performance\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Global Averages, All Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>iou</th>\n",
       "      <th>dice</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>training</th>\n",
       "      <th>testing</th>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">baseline</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">default</th>\n",
       "      <th>mit-b3</th>\n",
       "      <td>0.763476</td>\n",
       "      <td>0.832078</td>\n",
       "      <td>0.872395</td>\n",
       "      <td>0.821735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mit-b4</th>\n",
       "      <td>0.761666</td>\n",
       "      <td>0.831011</td>\n",
       "      <td>0.871696</td>\n",
       "      <td>0.823835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mit-b5</th>\n",
       "      <td>0.762018</td>\n",
       "      <td>0.830151</td>\n",
       "      <td>0.869729</td>\n",
       "      <td>0.825216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">clicktrained</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">with_clicks</th>\n",
       "      <th>mit-b3</th>\n",
       "      <td>0.883638</td>\n",
       "      <td>0.934701</td>\n",
       "      <td>0.939418</td>\n",
       "      <td>0.934463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mit-b4</th>\n",
       "      <td>0.881285</td>\n",
       "      <td>0.933635</td>\n",
       "      <td>0.942244</td>\n",
       "      <td>0.929536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mit-b5</th>\n",
       "      <td>0.885357</td>\n",
       "      <td>0.936301</td>\n",
       "      <td>0.940771</td>\n",
       "      <td>0.935978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">without_clicks</th>\n",
       "      <th>mit-b3</th>\n",
       "      <td>0.754106</td>\n",
       "      <td>0.817600</td>\n",
       "      <td>0.861910</td>\n",
       "      <td>0.804926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mit-b4</th>\n",
       "      <td>0.723493</td>\n",
       "      <td>0.787543</td>\n",
       "      <td>0.850613</td>\n",
       "      <td>0.770373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mit-b5</th>\n",
       "      <td>0.760948</td>\n",
       "      <td>0.824531</td>\n",
       "      <td>0.861840</td>\n",
       "      <td>0.818604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         iou      dice  precision    recall\n",
       "training     testing        model                                          \n",
       "baseline     default        mit-b3  0.763476  0.832078   0.872395  0.821735\n",
       "                            mit-b4  0.761666  0.831011   0.871696  0.823835\n",
       "                            mit-b5  0.762018  0.830151   0.869729  0.825216\n",
       "clicktrained with_clicks    mit-b3  0.883638  0.934701   0.939418  0.934463\n",
       "                            mit-b4  0.881285  0.933635   0.942244  0.929536\n",
       "                            mit-b5  0.885357  0.936301   0.940771  0.935978\n",
       "             without_clicks mit-b3  0.754106  0.817600   0.861910  0.804926\n",
       "                            mit-b4  0.723493  0.787543   0.850613  0.770373\n",
       "                            mit-b5  0.760948  0.824531   0.861840  0.818604"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_performance[\n",
    "    [\"training\", \"testing\", \"model\", \"iou\", \"dice\", \"precision\", \"recall\"]\n",
    "].groupby(by=[\"training\", \"testing\", \"model\"]).agg(\"mean\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance Details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = px.box(\n",
    "    data_frame=image_performance.query(\"model == 'mit-b3' and training == 'baseline'\")[\n",
    "        [\"dataset\", \"iou\"]\n",
    "    ],\n",
    "    x=\"dataset\",\n",
    "    color=\"dataset\",\n",
    "    y=\"iou\",\n",
    "    width=1000,\n",
    "    height=500,\n",
    ").update_layout(\n",
    "    showlegend=False,\n",
    "    title=dict(\n",
    "        text=\"<b>MiT-b3 baseline model, IoU distribution, by dataset</b>\",\n",
    "        font=dict(size=18),\n",
    "    ),\n",
    "    template=\"plotly_white\",\n",
    "    xaxis_title=\"<b>dataset</b>\",\n",
    "    yaxis_title=\"<b>IoU</b>\",\n",
    ")\n",
    "_.write_image(\"iou_mit-b3_datasets.png\")\n",
    "_.show(\"png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = px.box(\n",
    "    data_frame=image_performance.query(\"model == 'mit-b3' and training == 'baseline'\")[\n",
    "        [\"dataset\", \"dice\"]\n",
    "    ],\n",
    "    x=\"dataset\",\n",
    "    color=\"dataset\",\n",
    "    y=\"dice\",\n",
    "    width=1000,\n",
    "    height=500,\n",
    ").update_layout(\n",
    "    showlegend=False,\n",
    "    title=dict(\n",
    "        text=\"<b>MiT-b3 baseline model, Dice distribution, by dataset</b>\",\n",
    "        font=dict(size=18),\n",
    "    ),\n",
    "    template=\"plotly_white\",\n",
    "    xaxis_title=\"<b>dataset</b>\",\n",
    "    yaxis_title=\"<b>Dice</b>\",\n",
    ")\n",
    "_.write_image(\"dice_mit-b3_datasets.png\")\n",
    "_.show(\"png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = px.box(\n",
    "    data_frame=image_performance.query(\n",
    "        \"model == 'mit-b3' and training == 'baseline' and tumor != 'normal'\"\n",
    "    )[[\"tumor\", \"iou\"]],\n",
    "    x=\"tumor\",\n",
    "    color=\"tumor\",\n",
    "    y=\"iou\",\n",
    "    width=1000,\n",
    "    height=500,\n",
    ").update_layout(\n",
    "    showlegend=False,\n",
    "    title=dict(\n",
    "        text=\"<b>MiT-b3 baseline model: IoU distribution, by lesion type</b>\",\n",
    "        font=dict(size=18),\n",
    "    ),\n",
    "    template=\"plotly_white\",\n",
    "    xaxis_title=\"<b>lesion type</b>\",\n",
    "    yaxis_title=\"<b>IoU</b>\",\n",
    ")\n",
    "_.show(\"png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = px.box(\n",
    "    data_frame=image_performance.query(\"model == 'mit-b3' and training == 'baseline'\")[\n",
    "        [\"dataset_tumor\", \"iou\"]\n",
    "    ],\n",
    "    x=\"dataset_tumor\",\n",
    "    color=\"dataset_tumor\",\n",
    "    y=\"iou\",\n",
    "    width=1000,\n",
    "    height=500,\n",
    ").update_layout(\n",
    "    showlegend=False,\n",
    "    title=dict(\n",
    "        text=\"<b>MiT-b3 baseline model: IoU distribution, by dataset and lesion type</b>\",\n",
    "        font=dict(size=18),\n",
    "    ),\n",
    "    template=\"plotly_white\",\n",
    "    xaxis_title=\"<b>dataset and lesion type</b>\",\n",
    "    yaxis_title=\"<b>IoU</b>\",\n",
    ")\n",
    "_.show(\"png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = (\n",
    "    px.box(\n",
    "        data_frame=image_performance.query(\n",
    "            \"model == 'mit-b3' and testing != 'without_clicks'\"\n",
    "        ),\n",
    "        x=\"training\",\n",
    "        color=\"training\",\n",
    "        y=\"iou\",\n",
    "        width=1000,\n",
    "        height=500,\n",
    "    )\n",
    "    .update_layout(\n",
    "        showlegend=False,\n",
    "        title=dict(\n",
    "            text=\"<b>MiT-b3 model: IoU distribution, baseline model vs. click-trained and tested with clicks</b>\",\n",
    "            font=dict(size=18),\n",
    "        ),\n",
    "        template=\"plotly_white\",\n",
    "        xaxis_title=\"<b>training type</b>\",\n",
    "        yaxis_title=\"<b>IoU</b>\",\n",
    "    )\n",
    "    .add_annotation(\n",
    "        text=\"Note: testing with clicks gains extra guidance from users\",\n",
    "        showarrow=False,\n",
    "        x=0,\n",
    "        y=-0.15,\n",
    "        xref=\"paper\",\n",
    "        yref=\"paper\",\n",
    "    )\n",
    ")\n",
    "_.write_image(\"iou_mit-b3_training.png\")\n",
    "_.show(\"png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}