qm_project.ipynb

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  "metadata": {
    "colab": {
      "name": "qm_project.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "yB48MgByTguD"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yB48MgByTguD"
      },
      "source": [
        "# **SETUP**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PiqgiwlaTx7U"
      },
      "source": [
        "**Path to .json files**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JIH8z0cGhWq8"
      },
      "source": [
        "ap = 'drive/My Drive/Python Projects/qm_project/anonymized_project.json'  # path to anonymized_project.json\n",
        "ref = 'drive/My Drive/Python Projects/qm_project/references.json'         # path to references.json"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1IsMlJZ2T4pd"
      },
      "source": [
        "**Importation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PpkwmUcJki_Y"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "import requests"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8mzW9W8-UeWE"
      },
      "source": [
        "**Initial functions**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jAJQFxYohnit"
      },
      "source": [
        "def get_result_keys(df):\n",
        "    \"\"\"\n",
        "    Get dataframe and result_keys\n",
        "    :param df: input dataframe\n",
        "    :return: dataframe and result_keys\n",
        "    \"\"\"\n",
        "    ap_results = df.iloc[0][0]['results']\n",
        "    result_keys = list(ap_results.keys())\n",
        "    return ap_results, result_keys\n",
        "\n",
        "\n",
        "def get_img_keys(df):\n",
        "    \"\"\"\n",
        "    Get dataframe and result_keys\n",
        "    :param df: input dataframe\n",
        "    :return: dataframe and result_keys\n",
        "    \"\"\"\n",
        "    ref_keys = df.keys()\n",
        "    return ref_df, ref_keys"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ScNLPb6cM7gM"
      },
      "source": [
        "**Load data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dZP2h7eVEq0"
      },
      "source": [
        "ap_df = pd.read_json(ap)\n",
        "ref_df = pd.read_json(ref)\n",
        "\n",
        "ap_results, result_keys = get_result_keys(ap_df)\n",
        "ref_df, ref_keys = get_img_keys(ref_df)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w-JT6QrnUlOk"
      },
      "source": [
        "# **Task 1**\n",
        "Gather insights about the annotators  \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HAlSKsz6X9y0"
      },
      "source": [
        "**a. How many annotators did contribute to the dataset?**  \n",
        "\n",
        "**Conclusion:**  \n",
        "There are 22 annotators that contribute to the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YI5bAZdhksRD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "outputId": "3957da76-f932-4ba5-cd23-819c500bf397"
      },
      "source": [
        "export_csv = True\n",
        "\n",
        "\n",
        "annotators = []\n",
        "for result_key in result_keys:\n",
        "    result_amount = len(ap_results[result_key]['results'])\n",
        "    vendor_user_ids = [ap_results[result_key]['results'][i]['user']['vendor_user_id'] for i in range(result_amount)]\n",
        "\n",
        "    # - Add data to list\n",
        "    annotators.extend(vendor_user_ids)\n",
        "\n",
        "# Get unique vendor_user_id\n",
        "annotators = sorted(set(annotators))\n",
        "annotators_df = pd.DataFrame({'vendor_user_id': annotators})\n",
        "\n",
        "# Export data to csv file\n",
        "if export_csv:\n",
        "    annotators_df.to_csv(\"annotators.csv\", index=False)\n",
        "\n",
        "annotators_df"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>vendor_user_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>annotator_01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>annotator_02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>annotator_03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>annotator_04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>annotator_05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>annotator_06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>annotator_07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>annotator_08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>annotator_09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>annotator_10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>annotator_11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>annotator_12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>annotator_13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>annotator_14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>annotator_15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>annotator_16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>annotator_17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>annotator_18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>annotator_19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>annotator_20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>annotator_21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>annotator_22</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   vendor_user_id\n",
              "0    annotator_01\n",
              "1    annotator_02\n",
              "2    annotator_03\n",
              "3    annotator_04\n",
              "4    annotator_05\n",
              "5    annotator_06\n",
              "6    annotator_07\n",
              "7    annotator_08\n",
              "8    annotator_09\n",
              "9    annotator_10\n",
              "10   annotator_11\n",
              "11   annotator_12\n",
              "12   annotator_13\n",
              "13   annotator_14\n",
              "14   annotator_15\n",
              "15   annotator_16\n",
              "16   annotator_17\n",
              "17   annotator_18\n",
              "18   annotator_19\n",
              "19   annotator_20\n",
              "20   annotator_21\n",
              "21   annotator_22"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N2cHjal3WVsT"
      },
      "source": [
        "**b. What are the average, min and max annotation times (durations)? Feel free to add visual representations here such as graphs if you like.**\n",
        "\n",
        "**Conclusion:**  \n",
        "There exists a few samples with the value of -99999 ms, which is considered to be error data.  \n",
        "Moreover, samples labeled as corrupted are also excluded from assessment.  \n",
        "\n",
        "Minimum annotation time: 10 ms  \n",
        "Maximum annotation time: 42398 ms  \n",
        "Average annotation time: 1289 ms"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 752
        },
        "id": "5_IkohcZQlYl",
        "outputId": "77052cfd-7b77-42a2-cc5b-d46460442354"
      },
      "source": [
        "export_csv = True\n",
        "remove_data_issue = True\n",
        "fs = 15\n",
        "\n",
        "\n",
        "def get_duration_data(result_key, index):\n",
        "    \"\"\"\n",
        "    Get the corresponding duration data based on result_key and index\n",
        "    :param result_key: key of anonymized_project result\n",
        "    :param index: index of items in result key\n",
        "    :return: a list of data\n",
        "    \"\"\"\n",
        "    vendor_user_id = ap_results[result_key]['results'][index]['user']['vendor_user_id']\n",
        "    duration = ap_results[result_key]['results'][index]['task_output']['duration_ms']\n",
        "    image_url = ap_results[result_key]['results'][0]['task_input']['image_url']\n",
        "    image_name = image_url.split(\"/\")[-1]\n",
        "    image_name = image_name.split(\".\")[0]\n",
        "    return [vendor_user_id, image_name, duration, image_url]\n",
        "\n",
        "\n",
        "durations = []\n",
        "for result_key in result_keys:\n",
        "    result_amount = len(ap_results[result_key]['results'])\n",
        "\n",
        "    # Collect durations which are not labeled as corrupted\n",
        "    rows = [get_duration_data(result_key, i) for i in range(result_amount)\n",
        "                if not ap_results[result_key]['results'][i]['task_output']['corrupt_data']]\n",
        "\n",
        "    # - Add data to list\n",
        "    durations.extend(rows)\n",
        "\n",
        "\n",
        "# Store data in Pandas DataFrame\n",
        "durations_df = pd.DataFrame(data=durations, columns=['vendor_user_id', 'image_name', 'duration', 'image_url'])\n",
        "idxs = np.arange(0, len(durations))\n",
        "durations_df['idx'] = idxs\n",
        "\n",
        "# Remove data issue\n",
        "if remove_data_issue:\n",
        "    durations_df = durations_df.loc[durations_df['duration'] > 0]\n",
        "\n",
        "# Export data to csv file\n",
        "if export_csv:\n",
        "    durations_df.to_csv(\"annotation_time.csv\", index=False)\n",
        "\n",
        "print(durations_df['duration'].describe())\n",
        "\n",
        "\n",
        "# Scatter plot of annotation time\n",
        "ax1 = durations_df.plot.scatter(x='idx', y='duration')\n",
        "ax1.axhline(y=durations_df[\"duration\"].mean(), color='r', linestyle='-')    # draw mean line\n",
        "ax1.set_yticks(list(ax1.get_yticks()) + [durations_df[\"duration\"].mean()])  # add extra tick for mean line\n",
        "ax1.set_title(\"Scatter plot of annotation time\", fontsize=fs)\n",
        "ax1.set_xlabel(\"Samples\", fontsize=fs)\n",
        "ax1.set_ylabel(\"Duration [ms]\", fontsize=fs)\n",
        "ax1.grid(True)\n",
        "\n",
        "# Histogram of annotation time\n",
        "fig2, ax2 = plt.subplots()\n",
        "ax2 = durations_df['duration'].plot.hist(bins=200)\n",
        "ax2.set_title(\"Histogram of annotation time\", fontsize=fs)\n",
        "ax2.set_xlabel(\"Duration [ms]\", fontsize=fs)\n",
        "ax2.grid(True)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "count    90861.000000\n",
            "mean      1289.943661\n",
            "std       1124.010057\n",
            "min         10.000000\n",
            "25%        887.000000\n",
            "50%       1058.000000\n",
            "75%       1328.000000\n",
            "max      42398.000000\n",
            "Name: duration, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qZd8aS1rVY1m"
      },
      "source": [
        "**Annotation time grouped by annotators**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 813
        },
        "id": "ES41DjT8RO6V",
        "outputId": "0401bd15-47f2-4ed2-cec1-a439c4bc585e"
      },
      "source": [
        "export_csv = True\n",
        "\n",
        "annotation_time_df = pd.read_csv(\"annotation_time.csv\")\n",
        "annotation_time_by_user_df = annotation_time_df.groupby(\"vendor_user_id\")\n",
        "\n",
        "# Stack data into list\n",
        "annotation_time_by_user = []\n",
        "for item in annotation_time_by_user_df:\n",
        "    annotation_time_by_user.append([item[0], item[1]['duration'].mean(), item[1]['duration'].max(), item[1]['duration'].min()])\n",
        "\n",
        "annotation_time_by_user_df = pd.DataFrame(data=annotation_time_by_user, columns=['vendor_user_id', 'mean', 'max', 'min'])\n",
        "\n",
        "# Get types of annotation time\n",
        "max_user_anno_time = annotation_time_by_user_df['max'].max()\n",
        "min_user_anno_time = annotation_time_by_user_df['min'].min()\n",
        "max_user_ave_anno_time = annotation_time_by_user_df['mean'].max()\n",
        "min_user_ave_anno_time = annotation_time_by_user_df['mean'].min()\n",
        "\n",
        "# Get users based on annotation time types\n",
        "user_max_time = annotation_time_by_user_df.loc[annotation_time_by_user_df['max'] == max_user_anno_time]\n",
        "user_min_time = annotation_time_by_user_df.loc[annotation_time_by_user_df['min'] == min_user_anno_time]\n",
        "user_max_ave_time = annotation_time_by_user_df.loc[annotation_time_by_user_df['mean'] == max_user_ave_anno_time]\n",
        "user_min_ave_time = annotation_time_by_user_df.loc[annotation_time_by_user_df['mean'] == min_user_ave_anno_time]\n",
        "\n",
        "# Display\n",
        "print(\"Max annotation time:         %.2f ms - by %s\" % (max_user_anno_time, user_max_time['vendor_user_id'].values))\n",
        "print(\"Min annotation time:         %.2f ms - by %s\" % (min_user_anno_time, user_min_time['vendor_user_id'].values))\n",
        "print(\"Max average annotation time: %.2f ms - by %s\" % (max_user_ave_anno_time, user_max_ave_time['vendor_user_id'].values))\n",
        "print(\"Min average annotation time: %.2f ms - by %s\" % (min_user_ave_anno_time, user_min_ave_time['vendor_user_id'].values))\n",
        "\n",
        "# Export data to csv file\n",
        "if export_csv:\n",
        "    annotation_time_by_user_df.to_csv(\"annotation_time_by_user.csv\", index=False)\n",
        "\n",
        "annotation_time_by_user_df"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Max annotation time:         42398.00 ms - by ['annotator_06']\n",
            "Min annotation time:         10.00 ms - by ['annotator_04']\n",
            "Max average annotation time: 1687.78 ms - by ['annotator_19']\n",
            "Min average annotation time: 879.43 ms - by ['annotator_22']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>vendor_user_id</th>\n",
              "      <th>mean</th>\n",
              "      <th>max</th>\n",
              "      <th>min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>annotator_01</td>\n",
              "      <td>1077.323437</td>\n",
              "      <td>5120</td>\n",
              "      <td>526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>annotator_02</td>\n",
              "      <td>1178.170090</td>\n",
              "      <td>24856</td>\n",
              "      <td>447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>annotator_03</td>\n",
              "      <td>1460.249206</td>\n",
              "      <td>24190</td>\n",
              "      <td>503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>annotator_04</td>\n",
              "      <td>1113.926958</td>\n",
              "      <td>19158</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>annotator_05</td>\n",
              "      <td>1562.089209</td>\n",
              "      <td>23037</td>\n",
              "      <td>263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>annotator_06</td>\n",
              "      <td>1496.940217</td>\n",
              "      <td>42398</td>\n",
              "      <td>615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>annotator_07</td>\n",
              "      <td>1578.362299</td>\n",
              "      <td>27727</td>\n",
              "      <td>525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>annotator_08</td>\n",
              "      <td>1434.702111</td>\n",
              "      <td>33326</td>\n",
              "      <td>166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>annotator_09</td>\n",
              "      <td>1198.814198</td>\n",
              "      <td>37428</td>\n",
              "      <td>534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>annotator_10</td>\n",
              "      <td>1252.974603</td>\n",
              "      <td>37995</td>\n",
              "      <td>472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>annotator_11</td>\n",
              "      <td>1279.910659</td>\n",
              "      <td>41703</td>\n",
              "      <td>302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>annotator_12</td>\n",
              "      <td>1306.305475</td>\n",
              "      <td>31780</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>annotator_13</td>\n",
              "      <td>1155.011161</td>\n",
              "      <td>24000</td>\n",
              "      <td>470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>annotator_14</td>\n",
              "      <td>1594.908986</td>\n",
              "      <td>25488</td>\n",
              "      <td>444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>annotator_15</td>\n",
              "      <td>1365.291721</td>\n",
              "      <td>28760</td>\n",
              "      <td>231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>annotator_16</td>\n",
              "      <td>1269.793124</td>\n",
              "      <td>35880</td>\n",
              "      <td>575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>annotator_17</td>\n",
              "      <td>991.886370</td>\n",
              "      <td>13233</td>\n",
              "      <td>421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>annotator_18</td>\n",
              "      <td>1592.880224</td>\n",
              "      <td>34207</td>\n",
              "      <td>558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>annotator_19</td>\n",
              "      <td>1687.775758</td>\n",
              "      <td>15414</td>\n",
              "      <td>599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>annotator_20</td>\n",
              "      <td>1173.149200</td>\n",
              "      <td>10971</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>annotator_21</td>\n",
              "      <td>1238.922373</td>\n",
              "      <td>13238</td>\n",
              "      <td>279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>annotator_22</td>\n",
              "      <td>879.434957</td>\n",
              "      <td>40866</td>\n",
              "      <td>270</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   vendor_user_id         mean    max  min\n",
              "0    annotator_01  1077.323437   5120  526\n",
              "1    annotator_02  1178.170090  24856  447\n",
              "2    annotator_03  1460.249206  24190  503\n",
              "3    annotator_04  1113.926958  19158   10\n",
              "4    annotator_05  1562.089209  23037  263\n",
              "5    annotator_06  1496.940217  42398  615\n",
              "6    annotator_07  1578.362299  27727  525\n",
              "7    annotator_08  1434.702111  33326  166\n",
              "8    annotator_09  1198.814198  37428  534\n",
              "9    annotator_10  1252.974603  37995  472\n",
              "10   annotator_11  1279.910659  41703  302\n",
              "11   annotator_12  1306.305475  31780  149\n",
              "12   annotator_13  1155.011161  24000  470\n",
              "13   annotator_14  1594.908986  25488  444\n",
              "14   annotator_15  1365.291721  28760  231\n",
              "15   annotator_16  1269.793124  35880  575\n",
              "16   annotator_17   991.886370  13233  421\n",
              "17   annotator_18  1592.880224  34207  558\n",
              "18   annotator_19  1687.775758  15414  599\n",
              "19   annotator_20  1173.149200  10971   61\n",
              "20   annotator_21  1238.922373  13238  279\n",
              "21   annotator_22   879.434957  40866  270"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ISRY3O_4Xh9X"
      },
      "source": [
        "**Annotation time grouped by images**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "QZI_bAyrXhWV",
        "outputId": "4bc9841f-0eea-4fbc-8cb2-e17492b0866f"
      },
      "source": [
        "export_csv = True\n",
        "\n",
        "annotation_time_df = pd.read_csv(\"annotation_time.csv\")\n",
        "annotation_time_by_image_df = annotation_time_df.groupby(\"image_name\")\n",
        "\n",
        "# Stack data into list\n",
        "annotation_time_by_image = []\n",
        "for item in annotation_time_by_image_df:\n",
        "    annotation_time_by_image.append([item[0], item[1]['duration'].mean(), item[1]['duration'].max(), item[1]['duration'].min()])\n",
        "\n",
        "annotation_time_by_image_df = pd.DataFrame(data=annotation_time_by_image, columns=['image_name', 'mean', 'max', 'min'])\n",
        "\n",
        "# Get types of annotation time\n",
        "max_image_anno_time = annotation_time_by_image_df['max'].max()\n",
        "min_image_anno_time = annotation_time_by_image_df['min'].min()\n",
        "max_image_ave_anno_time = annotation_time_by_image_df['mean'].max()\n",
        "min_image_ave_anno_time = annotation_time_by_image_df['mean'].min()\n",
        "\n",
        "# Get images based on annotation time types\n",
        "image_max_time = annotation_time_by_image_df.loc[annotation_time_by_image_df['max'] == max_image_anno_time]\n",
        "image_min_time = annotation_time_by_image_df.loc[annotation_time_by_image_df['min'] == min_image_anno_time]\n",
        "image_max_ave_time = annotation_time_by_image_df.loc[annotation_time_by_image_df['mean'] == max_image_ave_anno_time]\n",
        "image_min_ave_time = annotation_time_by_image_df.loc[annotation_time_by_image_df['mean'] == min_image_ave_anno_time]\n",
        "\n",
        "# Display\n",
        "print(\"Max annotation time:         %.2f ms - of %s\" % (max_image_anno_time, image_max_time['image_name'].values))\n",
        "print(\"Min annotation time:         %.2f ms - of %s\" % (min_image_anno_time, image_min_time['image_name'].values))\n",
        "print(\"Max average annotation time: %.2f ms - of %s\" % (max_image_ave_anno_time, image_max_ave_time['image_name'].values))\n",
        "print(\"Min average annotation time: %.2f ms - of %s\" % (min_image_ave_anno_time, image_min_ave_time['image_name'].values))\n",
        "\n",
        "# Export data to csv file\n",
        "if export_csv:\n",
        "    annotation_time_by_image_df.to_csv(\"annotation_time_by_image.csv\", index=False)\n",
        "\n",
        "# Visualization of the images\n",
        "fig, ax = plt.subplots(1, 4)\n",
        "fig.tight_layout()\n",
        "images_df = [image_max_time, image_min_time, image_max_ave_time, image_min_ave_time]\n",
        "titles = ['max', 'min', 'max_ave', 'min_ave']\n",
        "\n",
        "for idx, item in enumerate(images_df):\n",
        "    im_url = annotation_time_df.loc[annotation_time_df['image_name'] == item['image_name'].values[0]]\n",
        "    im_url = im_url['image_url'].values[0]\n",
        "    im = Image.open(requests.get(im_url, stream=True).raw)\n",
        "    ax[idx].imshow(im)\n",
        "    ax[idx].set_title(titles[idx])\n",
        "\n",
        "annotation_time_by_image_df.describe()\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Max annotation time:         42398.00 ms - of ['img_5245']\n",
            "Min annotation time:         10.00 ms - of ['img_5100']\n",
            "Max average annotation time: 5577.50 ms - of ['img_1340']\n",
            "Min average annotation time: 738.90 ms - of ['img_8427']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>mean</th>\n",
              "      <th>max</th>\n",
              "      <th>min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9087.000000</td>\n",
              "      <td>9087.000000</td>\n",
              "      <td>9087.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1289.927533</td>\n",
              "      <td>2932.915154</td>\n",
              "      <td>760.809838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>395.941187</td>\n",
              "      <td>2843.046908</td>\n",
              "      <td>129.364879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>738.900000</td>\n",
              "      <td>903.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1049.000000</td>\n",
              "      <td>1583.000000</td>\n",
              "      <td>677.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1193.400000</td>\n",
              "      <td>2101.000000</td>\n",
              "      <td>748.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1406.200000</td>\n",
              "      <td>3133.000000</td>\n",
              "      <td>832.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5577.500000</td>\n",
              "      <td>42398.000000</td>\n",
              "      <td>1421.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              mean           max          min\n",
              "count  9087.000000   9087.000000  9087.000000\n",
              "mean   1289.927533   2932.915154   760.809838\n",
              "std     395.941187   2843.046908   129.364879\n",
              "min     738.900000    903.000000    10.000000\n",
              "25%    1049.000000   1583.000000   677.000000\n",
              "50%    1193.400000   2101.000000   748.000000\n",
              "75%    1406.200000   3133.000000   832.000000\n",
              "max    5577.500000  42398.000000  1421.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QKS1ruk8Wodr"
      },
      "source": [
        "**c. Did all annotators produce the same amount of results, or are there differences?**  \n",
        "\n",
        "**Conclusion:**  \n",
        "There are huge differences between the sample sizes of each annotator. Some annotators have up to approximately 6000 samples, while some others have only less than 500 samples.  \n",
        "This will later be used as one of the attributes to evaluate the performance of the annotator."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "MpvxsAi4mtjc",
        "outputId": "2d0917eb-a8da-471d-810c-9c57b342f6ea"
      },
      "source": [
        "export_csv = True\n",
        "fs = 15\n",
        "\n",
        "\n",
        "annotators_list = []\n",
        "for result_key in result_keys:\n",
        "    result_amount = len(ap_results[result_key]['results'])\n",
        "    vendor_user_id = [ap_results[result_key]['results'][i]['user']['vendor_user_id'] for i in range(result_amount)]\n",
        "\n",
        "    # - Add data to list\n",
        "    annotators_list.extend(vendor_user_id)\n",
        "\n",
        "# Count the occurrences of annotators\n",
        "annotators, annotator_count = np.unique(np.array(annotators_list), return_counts=True)\n",
        "\n",
        "# Store data in Pandas DataFrame\n",
        "annotator_result_count_df = pd.DataFrame({\"vendor_user_id\": annotators, \"result_count\": annotator_count})\n",
        "\n",
        "# Visualization\n",
        "ax = annotator_result_count_df['result_count'].plot(kind='bar')\n",
        "ax.set_xticklabels(annotator_result_count_df['vendor_user_id'])\n",
        "ax.grid(True)\n",
        "ax.set_title(\"Users result counts\", fontsize=fs)\n",
        "ax.set_xlabel(\"Annotators\", fontsize=fs)\n",
        "ax.set_ylabel(\"[count]\", fontsize=fs)\n",
        "\n",
        "ax.axhline(y=annotator_result_count_df[\"result_count\"].mean(), color='r', linestyle='-')      # draw mean line\n",
        "ax.axhline(y=annotator_result_count_df[\"result_count\"].std(), color='purple', linestyle='-')  # draw std line\n",
        "ax.legend([\"mean\", \"standard deviation\", \"count\"], prop={'size': fs})\n",
        "\n",
        "# Add numbers to bars\n",
        "for p, count in zip(ax.patches, annotator_result_count_df['result_count'].values):\n",
        "    ax.annotate(str(count), (p.get_x(), p.get_height() + 20))\n",
        "\n",
        "# Export data to csv file\n",
        "if export_csv:\n",
        "    annotator_result_count_df.to_csv(\"annotator_result_count.csv\", index=False)\n",
        "\n",
        "annotator_result_count_df.describe()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>result_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>22.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4130.454545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2403.233550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>170.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1852.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4960.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6189.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7596.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       result_count\n",
              "count     22.000000\n",
              "mean    4130.454545\n",
              "std     2403.233550\n",
              "min      170.000000\n",
              "25%     1852.500000\n",
              "50%     4960.500000\n",
              "75%     6189.000000\n",
              "max     7596.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40-oQKCAW_b1"
      },
      "source": [
        "**d. Are there questions for which annotators highly disagree?**  \n",
        "\n",
        "**Conclusion:**  \n",
        "Questions with the **yes:no** ratio of **4:6**, **5:5** and **6:4** are considered as highly disagree questions. They take about **2.17%** of the entire dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ERPbGEQkuGwv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 858
        },
        "outputId": "6a64b149-d1b8-47e0-dc18-8ccc70c3d6f0"
      },
      "source": [
        "export_csv = True\n",
        "fs = 15\n",
        "\n",
        "question_answers = []\n",
        "\n",
        "for result_key in result_keys:\n",
        "    result_amount = len(ap_results[result_key]['results'])\n",
        "    answers = [ap_results[result_key]['results'][i]['task_output']['answer'] for i in range(result_amount)]\n",
        "    solves = [ap_results[result_key]['results'][i]['task_output']['cant_solve'] for i in range(result_amount)]\n",
        "    corrupts = [ap_results[result_key]['results'][i]['task_output']['corrupt_data'] for i in range(result_amount)]\n",
        "    image_url = ap_results[result_key]['results'][0]['task_input']['image_url']\n",
        "    image_name = image_url.split(\"/\")[-1]\n",
        "    image_name = image_name.split(\".\")[0]\n",
        "\n",
        "    # Count the occurrence of each case \n",
        "    cant_solve = solves.count(True)\n",
        "    corrupt_amount = corrupts.count(True)\n",
        "    ans_yes = answers.count('yes')\n",
        "    ans_no = answers.count('no')\n",
        "\n",
        "    # - Add data to list\n",
        "    question_answers.append([result_key, ans_yes, ans_no, cant_solve, corrupt_amount, image_name, image_url])\n",
        "\n",
        "\n",
        "question_answers_df = pd.DataFrame(data=question_answers,\n",
        "                                   columns=[\"result_key\", \"yes\", \"no\", \"cant_solve\", \"corrupt_data\", \"image_name\", \"image_url\"])\n",
        "\n",
        "# Specify the amount of 'yes' counts\n",
        "yes_group = ['4', '5', '6']\n",
        "highly_disagree_group_df = question_answers_df[question_answers_df['yes'].isin(yes_group)]\n",
        "\n",
        "\n",
        "# Visualization\n",
        "cs = plt.get_cmap('tab20')\n",
        "\n",
        "# Group questions based on difference\n",
        "question_answers_grouped = question_answers_df.groupby('yes')\n",
        "total_answer = len(question_answers)\n",
        "\n",
        "question_groups = [[int(item[0]), len(item[1])] for item in question_answers_grouped]\n",
        "question_groups = [[\"{}:{}\".format(item[0], 10 - item[0]), item[1], round(item[1] / total_answer * 100, 2)]\n",
        "                   for item in sorted(question_groups)]\n",
        "question_groups_df = pd.DataFrame(data=question_groups, columns=['yes:no', 'count', 'percent'])\n",
        "\n",
        "question_groups_df = question_groups_df.astype({'count': int, 'percent': float})\n",
        "explode = (0, 0, 0, 0, 0.1, 0.1, 0.1, 0, 0, 0, 0)\n",
        "ax = question_groups_df.plot.pie(y='percent', autopct='%.2f%%', colormap=cs, labels=None, explode=explode)\n",
        "ax.set_title(\"Questions grouped based on yes:no ratio percentage\", fontsize=fs)\n",
        "ax.legend(question_groups_df['yes:no'], prop={'size': 12})\n",
        "\n",
        "\n",
        "# Export data to csv file\n",
        "if export_csv:\n",
        "    question_answers_df.to_csv(\"question_answers.csv\", index=False)\n",
        "    question_groups_df.to_csv(\"question_groups.csv\", index=False)\n",
        "    highly_disagree_group_df.to_csv(\"highly_disagree_group.csv\", index=False)\n",
        "\n",
        "\n",
        "# Display 3 random samples\n",
        "highly_disagree_sample_df = highly_disagree_group_df.sample(n=3)\n",
        "highly_disagree_sample_df = highly_disagree_sample_df['image_url']\n",
        "urls = [item for item in highly_disagree_sample_df]\n",
        "fig2, ax2 = plt.subplots(1, 3)\n",
        "fig2.tight_layout()\n",
        "for i in range(len(urls)):\n",
        "    im = Image.open(requests.get(urls[i], stream=True).raw)\n",
        "    ax2[i].imshow(im)\n",
        "\n",
        "question_groups_df"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>yes:no</th>\n",
              "      <th>count</th>\n",
              "      <th>percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0:10</td>\n",
              "      <td>3524</td>\n",
              "      <td>38.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1:9</td>\n",
              "      <td>638</td>\n",
              "      <td>7.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2:8</td>\n",
              "      <td>139</td>\n",
              "      <td>1.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3:7</td>\n",
              "      <td>104</td>\n",
              "      <td>1.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4:6</td>\n",
              "      <td>75</td>\n",
              "      <td>0.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5:5</td>\n",
              "      <td>68</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6:4</td>\n",
              "      <td>54</td>\n",
              "      <td>0.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7:3</td>\n",
              "      <td>83</td>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8:2</td>\n",
              "      <td>112</td>\n",
              "      <td>1.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9:1</td>\n",
              "      <td>437</td>\n",
              "      <td>4.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10:0</td>\n",
              "      <td>3853</td>\n",
              "      <td>42.40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   yes:no  count  percent\n",
              "0    0:10   3524    38.78\n",
              "1     1:9    638     7.02\n",
              "2     2:8    139     1.53\n",
              "3     3:7    104     1.14\n",
              "4     4:6     75     0.83\n",
              "5     5:5     68     0.75\n",
              "6     6:4     54     0.59\n",
              "7     7:3     83     0.91\n",
              "8     8:2    112     1.23\n",
              "9     9:1    437     4.81\n",
              "10   10:0   3853    42.40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DwFXWIVSXFbx"
      },
      "source": [
        "# **Task 2**\n",
        "Besides picking yes or no the annotators had the chance to tell if the data were corrupted or if they for any reason were not able to solve the task. These are fields 'cant_solve' and 'corrupt_data' given in the task_output."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bs0A6QV4YEV3"
      },
      "source": [
        "**a. How often does each occur in the project and do you see a trend within the annotators that made use of these options?**  \n",
        "\n",
        "**Conclusion:**  \n",
        "The percentages of 'cant_solve' and 'corrupt_data' to happen are 0.5035% and 0.0727% respectively.\n",
        "*   'annotator_04' and 'annotator_22', each marked 4 samples as 'cant_solve'.\n",
        "*   'annotator_18' is the one made use of the option 'corrupt_data' most (2 samples).\n",
        "\n",
        "There is no repetition of any sample among those marked with 'cant_solve' and 'corrupt_data'. Each sample in this region is unique."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "iON_A-I8AMWI",
        "outputId": "16dad6cb-ac0c-4748-d6bd-4dbea9e4f71e"
      },
      "source": [
        "annotator_result_count_df = pd.read_csv(\"annotator_result_count.csv\")\n",
        "export_csv = True\n",
        "fs = 15\n",
        "\n",
        "\n",
        "def unsolved_check(result_key, index):\n",
        "    \"\"\"\n",
        "    :param result_key: key of anonymized_project result\n",
        "    :param index: index of items in result key\n",
        "    :return: boolean value based on whether the answer is cant_solve or the data is corrupted\n",
        "    \"\"\"\n",
        "    cant_solve_cond = ap_results[result_key]['results'][index]['task_output']['cant_solve']\n",
        "    corrupt_data_cond = ap_results[result_key]['results'][index]['task_output']['corrupt_data']\n",
        "    return cant_solve_cond or corrupt_data_cond\n",
        "\n",
        "\n",
        "def get_data(result_key, index):\n",
        "    \"\"\"\n",
        "    Get the corresponding data based on result_key and index\n",
        "    :param result_key: key of anonymized_project result\n",
        "    :param index: index of items in result key\n",
        "    :return: a list of data\n",
        "    \"\"\"\n",
        "    vendor_user_id = ap_results[result_key]['results'][index]['user']['vendor_user_id']\n",
        "    cant_solve = ap_results[result_key]['results'][index]['task_output']['cant_solve']\n",
        "    corrupt_data = ap_results[result_key]['results'][index]['task_output']['corrupt_data']\n",
        "    return [vendor_user_id, int(cant_solve), int(corrupt_data)]\n",
        "\n",
        "\n",
        "def get_user_result_count(user_id):\n",
        "    \"\"\"\n",
        "    Get user's result count based on annotator id id\n",
        "    :param user_id: annotator id\n",
        "    :return: user's result count\n",
        "    \"\"\"\n",
        "    annotator_result_count_dict = annotator_result_count_df.set_index('vendor_user_id').to_dict()\n",
        "    count = annotator_result_count_dict['result_count'][user_id]\n",
        "    return int(count)\n",
        "\n",
        "\n",
        "# Gather rows of data with users with 'cant_solve' and 'corrupt_data'\n",
        "unsolved_data = []\n",
        "\n",
        "for result_key in result_keys:\n",
        "    result_amount = len(ap_results[result_key]['results'])\n",
        "    unsolved = [get_data(result_key, i) for i in range(result_amount) if unsolved_check(result_key, i)]\n",
        "\n",
        "    # - Add data to list\n",
        "    unsolved_data.extend(unsolved)\n",
        "\n",
        "\n",
        "# Get unique user ids\n",
        "unsolved_user = sorted(set(np.array(unsolved_data)[:, 0]))\n",
        "\n",
        "\n",
        "# Get the count of 'cant_solve' and 'corrupt_data' for each user and store them in a list\n",
        "grouped_unsolved_data = []\n",
        "\n",
        "for vendor_user_id in unsolved_user:\n",
        "    # - Retrieve the answers of a specific user\n",
        "    user_result = np.array([item for item in unsolved_data if item[0] == vendor_user_id])\n",
        "\n",
        "    # - Count the 'cant_solve' and 'corrupt_data' occurrences\n",
        "    cant_solve_count = np.sum(user_result[:, 1] == '1')\n",
        "    corrupt_data_count = np.sum(user_result[:, 2] == '1')\n",
        "\n",
        "    cant_solve_percent = round(cant_solve_count / get_user_result_count(vendor_user_id) * 100, 4)\n",
        "    corrupt_data_percent = round(corrupt_data_count / get_user_result_count(vendor_user_id) * 100, 4)\n",
        "\n",
        "    # - Add data to list\n",
        "    grouped_unsolved_data.append([vendor_user_id, cant_solve_count, corrupt_data_count, cant_solve_percent, corrupt_data_percent])\n",
        "\n",
        "\n",
        "# Store data in Pandas DataFrame\n",
        "grouped_unsolved_data_df = pd.DataFrame(data=grouped_unsolved_data,\n",
        "                                        columns=['vendor_user_id', 'cant_solve', 'corrupt_data', 'cant_solve_percent', 'corrupt_data_percent'])\n",
        "\n",
        "# Export data to csv file\n",
        "if export_csv:\n",
        "    grouped_unsolved_data_df.to_csv(\"grouped_unsolved_data.csv\", index=False)\n",
        "\n",
        "# Visualization\n",
        "display_df = grouped_unsolved_data_df[['cant_solve', 'corrupt_data']].astype(float)\n",
        "xtick_label = [item.split(\"_\")[1] for item in grouped_unsolved_data_df['vendor_user_id']]\n",
        "\n",
        "ax = display_df.plot(kind='bar', stacked=True)\n",
        "ax.set_xticklabels(xtick_label, fontsize=11)\n",
        "ax.set_title(\"'cant_solve' and 'corrupt_data' occurrences\", fontsize=fs)\n",
        "ax.set_xlabel(\"Annotators\")\n",
        "ax.set_ylabel(\"[count]\", fontsize=fs)\n",
        "ax.grid(True)\n",
        "\n",
        "\n",
        "print(\"Total percentage of 'cant_solve'  : {}%\".format(grouped_unsolved_data_df['cant_solve_percent'].sum()))\n",
        "print(\"Total percentage of 'corrupt_data': {}%\".format(grouped_unsolved_data_df['corrupt_data_percent'].sum()))\n",
        "\n",
        "grouped_unsolved_data_df[['vendor_user_id', 'cant_solve_percent', 'corrupt_data_percent']]"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total percentage of 'cant_solve'  : 0.5035%\n",
            "Total percentage of 'corrupt_data': 0.0727%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>vendor_user_id</th>\n",
              "      <th>cant_solve_percent</th>\n",
              "      <th>corrupt_data_percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>annotator_02</td>\n",
              "      <td>0.0263</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>annotator_04</td>\n",
              "      <td>0.0623</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>annotator_06</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>annotator_07</td>\n",
              "      <td>0.0460</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>annotator_08</td>\n",
              "      <td>0.0306</td>\n",
              "      <td>0.0153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>annotator_11</td>\n",
              "      <td>0.0155</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>annotator_14</td>\n",
              "      <td>0.0580</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>annotator_18</td>\n",
              "      <td>0.0193</td>\n",
              "      <td>0.0387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>annotator_20</td>\n",
              "      <td>0.0163</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>annotator_22</td>\n",
              "      <td>0.2292</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  vendor_user_id  cant_solve_percent  corrupt_data_percent\n",
              "0   annotator_02              0.0263                0.0000\n",
              "1   annotator_04              0.0623                0.0000\n",
              "2   annotator_06              0.0000                0.0187\n",
              "3   annotator_07              0.0460                0.0000\n",
              "4   annotator_08              0.0306                0.0153\n",
              "5   annotator_11              0.0155                0.0000\n",
              "6   annotator_14              0.0580                0.0000\n",
              "7   annotator_18              0.0193                0.0387\n",
              "8   annotator_20              0.0163                0.0000\n",
              "9   annotator_22              0.2292                0.0000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VC27egcbXwmz"
      },
      "source": [
        "# **Task 3**\n",
        "Is the reference set balanced? Please demonstrate via numbers and visualizations.  \n",
        "\n",
        "**Conclusion:**  \n",
        "The reference set is balanced with the percentage of True and False labels are 50.47% and 49.53%, respectively."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "32pdEm83OP2g",
        "outputId": "a15c991e-c409-45ce-af29-3ff3366b439a"
      },
      "source": [
        "fs = 15\n",
        "\n",
        "\n",
        "# Get data\n",
        "ref_ans = [[ref_key, ref_df[ref_key]['is_bicycle']] for ref_key in ref_keys]\n",
        "ref_ans = np.array(ref_ans)\n",
        "\n",
        "# Store the collected data in Pandas DataFrame\n",
        "ref_ans_df = pd.DataFrame({'key': ref_ans[:, 0], 'is_bicycle': ref_ans[:, 1]})\n",
        "ref_ans_df = ref_ans_df.groupby('is_bicycle').nunique()\n",
        "ref_ans_df = ref_ans_df.rename(columns={\"key\": \"count\"})\n",
        "ax = ref_ans_df.plot.pie(y='count', autopct='%.2f%%', labels=None)\n",
        "ax.set_title(\"Reference set distribution percentage\", fontsize=fs)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Reference set distribution percentage')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e43b5iowYZPw"
      },
      "source": [
        "# **Task 4**\n",
        "Using the reference set, can you identify good and bad annotators? Please use statistics and visualizations. Feel free to get creative."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WAG5HCpFYeSD"
      },
      "source": [
        "**Create data**\n",
        "\n",
        "**Important note:**\n",
        "This cell is used to collect data by doing cross-checking all answers with the reference set. The execution takes approximately 45 minutes to complete and it only needs to execute once to create the annotators_quality_assessment.csv file.\n",
        "\n",
        "***There is no need to run this cell for evaluation if the annotators_quality_assessment.csv exits. Simply upload the file and ignore this cell.***\n",
        "\n",
        "The 'null' column is used to indicates the amount of samples with 'cant_solve' or 'corrupt_data' labeled as True."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mk_MdtchYi3o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8d1ccab9-cda1-47a6-ff52-c72c29fbbf6f"
      },
      "source": [
        "export_csv = True\n",
        "\n",
        "\n",
        "def percent_cal(value, sum):\n",
        "    \"\"\"\n",
        "    Calculate percent value\n",
        "    \"\"\"\n",
        "    return value / sum * 100\n",
        "\n",
        "\n",
        "# Get annotators\n",
        "annotators = []\n",
        "for result_key in result_keys:\n",
        "    result_amount = len(ap_results[result_key]['results'])\n",
        "    vendor_user_ids = [ap_results[result_key]['results'][i]['user']['vendor_user_id'] for i in range(result_amount)]\n",
        "    annotators.extend(vendor_user_ids)\n",
        "\n",
        "annotators = sorted(set(annotators))\n",
        "\n",
        "\n",
        "# Get reference set\n",
        "ref_ans = [[key, ref_df[key]['is_bicycle']] for key in ref_keys]\n",
        "\n",
        "\n",
        "# Get annotators' answers\n",
        "annotators_ans = []\n",
        "for result_key in result_keys:\n",
        "    result_amount = len(ap_results[result_key]['results'])\n",
        "\n",
        "    # annotators\n",
        "    vendor_user_ids = [ap_results[result_key]['results'][i]['user']['vendor_user_id'] for i in range(result_amount)]\n",
        "\n",
        "    # annotators' answers\n",
        "    answers = [ap_results[result_key]['results'][i]['task_output']['answer'] for i in range(result_amount)]\n",
        "\n",
        "    # image name\n",
        "    image_url = ap_results[result_key]['results'][0]['task_input']['image_url']\n",
        "    image_name = image_url.split(\"/\")[-1]\n",
        "    image_name = image_name.split(\".\")[0]\n",
        "\n",
        "    for idx, ans in zip(vendor_user_ids, answers):\n",
        "        annotators_ans.append([idx, image_name, ans])\n",
        "\n",
        "\n",
        "# Group the annotators' answer by annotators\n",
        "annotators_packages = []\n",
        "for an_id in annotators:\n",
        "    temp = [[idx, im_name, ans] for idx, im_name, ans in annotators_ans if an_id == idx]\n",
        "    annotators_packages.append(temp)\n",
        "\n",
        "\n",
        "# Count the correct, incorrect and null answers\n",
        "annotators_quality = []\n",
        "for annotator_package in annotators_packages:\n",
        "    # - Initialization of counters\n",
        "    correct, incorrect, null = 0, 0, 0\n",
        "\n",
        "    # - Current annotator\n",
        "    vendor_user_id = annotator_package[0][0]\n",
        "\n",
        "    # - Looping through each package and count the correct, incorrect and null answer\n",
        "    for user_id, im_name, user_an in annotator_package:\n",
        "        # -- Get row index of reference answer\n",
        "        idx = [i for i, ref in enumerate(ref_ans) if im_name in ref][0]\n",
        "\n",
        "        # -- Conditions\n",
        "        correct_cond1 = user_an == 'yes' and ref_ans[idx][1]\n",
        "        correct_cond2 = user_an == 'no' and not ref_ans[idx][1]\n",
        "        null_cond = user_an == \"\"\n",
        "\n",
        "        # -- Check condition and count\n",
        "        if correct_cond1 or correct_cond2:\n",
        "            correct += 1\n",
        "        elif null_cond:\n",
        "            null += 1\n",
        "        else:\n",
        "            incorrect += 1\n",
        "\n",
        "    total = correct + incorrect + null\n",
        "\n",
        "    # - Add data to list\n",
        "    annotators_quality.append([vendor_user_id, correct, incorrect, null, total,\n",
        "                               percent_cal(correct, total), percent_cal(incorrect, total), percent_cal(null, total)])\n",
        "\n",
        "    print(vendor_user_id, correct, incorrect, null, total,\n",
        "                               percent_cal(correct, total), percent_cal(incorrect, total), percent_cal(null, total))\n",
        "\n",
        "\n",
        "# Store data in Pandas DataFrame\n",
        "annotators_quality_df = pd.DataFrame(data=annotators_quality,\n",
        "                                     columns=['vendor_user_id', 'correct', 'incorrect', 'null', 'total',\n",
        "                                              'correct_p', 'incorrect_p', 'null_p'])\n",
        "\n",
        "# Export data to csv file\n",
        "if export_csv:\n",
        "    annotators_quality_df.to_csv(\"annotators_quality_assessment.csv\", index=False)\n",
        "\n",
        "\n",
        "annotators_quality_df"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "annotator_01 1214 66 0 1280 94.84375 5.15625 0.0\n",
            "annotator_02 7101 493 2 7596 93.48341232227489 6.490258030542391 0.02632964718272775\n",
            "annotator_03 585 45 0 630 92.85714285714286 7.142857142857142 0.0\n",
            "annotator_04 5960 457 4 6421 92.82043295436848 7.117271453044697 0.06229559258682448\n",
            "annotator_05 3256 219 0 3475 93.6978417266187 6.302158273381295 0.0\n",
            "annotator_06 4945 391 1 5337 92.6550496533633 7.3262132284054715 0.01873711823121604\n",
            "annotator_07 1990 184 1 2175 91.49425287356323 8.459770114942529 0.04597701149425287\n",
            "annotator_08 5878 656 3 6537 89.9189230533884 10.035184335322013 0.04589261128958238\n",
            "annotator_09 4536 324 0 4860 93.33333333333333 6.666666666666667 0.0\n",
            "annotator_10 280 35 0 315 88.88888888888889 11.11111111111111 0.0\n",
            "annotator_11 6026 409 1 6436 93.62958359229336 6.354878806712244 0.015537600994406462\n",
            "annotator_12 5774 436 0 6210 92.97906602254429 7.020933977455717 0.0\n",
            "annotator_13 6702 376 0 7078 94.68776490534049 5.312235094659509 0.0\n",
            "annotator_14 1633 91 1 1725 94.66666666666667 5.27536231884058 0.057971014492753624\n",
            "annotator_15 5774 314 0 6088 94.84231274638634 5.157687253613666 0.0\n",
            "annotator_16 4769 292 0 5061 94.23038925113613 5.769610748863861 0.0\n",
            "annotator_17 3283 202 0 3485 94.20373027259684 5.796269727403156 0.0\n",
            "annotator_18 4841 326 3 5170 93.63636363636364 6.3056092843326885 0.058027079303675046\n",
            "annotator_19 161 9 0 170 94.70588235294117 5.294117647058823 0.0\n",
            "annotator_20 5809 316 1 6126 94.82533463924257 5.158341495266079 0.01632386549134835\n",
            "annotator_21 2773 177 0 2950 94.0 6.0 0.0\n",
            "annotator_22 1644 97 4 1745 94.21203438395416 5.558739255014326 0.2292263610315186\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>vendor_user_id</th>\n",
              "      <th>correct</th>\n",
              "      <th>incorrect</th>\n",
              "      <th>null</th>\n",
              "      <th>total</th>\n",
              "      <th>correct_p</th>\n",
              "      <th>incorrect_p</th>\n",
              "      <th>null_p</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>annotator_01</td>\n",
              "      <td>1214</td>\n",
              "      <td>66</td>\n",
              "      <td>0</td>\n",
              "      <td>1280</td>\n",
              "      <td>94.843750</td>\n",
              "      <td>5.156250</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>annotator_02</td>\n",
              "      <td>7101</td>\n",
              "      <td>493</td>\n",
              "      <td>2</td>\n",
              "      <td>7596</td>\n",
              "      <td>93.483412</td>\n",
              "      <td>6.490258</td>\n",
              "      <td>0.026330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>annotator_03</td>\n",
              "      <td>585</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>630</td>\n",
              "      <td>92.857143</td>\n",
              "      <td>7.142857</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>annotator_04</td>\n",
              "      <td>5960</td>\n",
              "      <td>457</td>\n",
              "      <td>4</td>\n",
              "      <td>6421</td>\n",
              "      <td>92.820433</td>\n",
              "      <td>7.117271</td>\n",
              "      <td>0.062296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>annotator_05</td>\n",
              "      <td>3256</td>\n",
              "      <td>219</td>\n",
              "      <td>0</td>\n",
              "      <td>3475</td>\n",
              "      <td>93.697842</td>\n",
              "      <td>6.302158</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>annotator_06</td>\n",
              "      <td>4945</td>\n",
              "      <td>391</td>\n",
              "      <td>1</td>\n",
              "      <td>5337</td>\n",
              "      <td>92.655050</td>\n",
              "      <td>7.326213</td>\n",
              "      <td>0.018737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>annotator_07</td>\n",
              "      <td>1990</td>\n",
              "      <td>184</td>\n",
              "      <td>1</td>\n",
              "      <td>2175</td>\n",
              "      <td>91.494253</td>\n",
              "      <td>8.459770</td>\n",
              "      <td>0.045977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>annotator_08</td>\n",
              "      <td>5878</td>\n",
              "      <td>656</td>\n",
              "      <td>3</td>\n",
              "      <td>6537</td>\n",
              "      <td>89.918923</td>\n",
              "      <td>10.035184</td>\n",
              "      <td>0.045893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>annotator_09</td>\n",
              "      <td>4536</td>\n",
              "      <td>324</td>\n",
              "      <td>0</td>\n",
              "      <td>4860</td>\n",
              "      <td>93.333333</td>\n",
              "      <td>6.666667</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>annotator_10</td>\n",
              "      <td>280</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>315</td>\n",
              "      <td>88.888889</td>\n",
              "      <td>11.111111</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>annotator_11</td>\n",
              "      <td>6026</td>\n",
              "      <td>409</td>\n",
              "      <td>1</td>\n",
              "      <td>6436</td>\n",
              "      <td>93.629584</td>\n",
              "      <td>6.354879</td>\n",
              "      <td>0.015538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>annotator_12</td>\n",
              "      <td>5774</td>\n",
              "      <td>436</td>\n",
              "      <td>0</td>\n",
              "      <td>6210</td>\n",
              "      <td>92.979066</td>\n",
              "      <td>7.020934</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>annotator_13</td>\n",
              "      <td>6702</td>\n",
              "      <td>376</td>\n",
              "      <td>0</td>\n",
              "      <td>7078</td>\n",
              "      <td>94.687765</td>\n",
              "      <td>5.312235</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>annotator_14</td>\n",
              "      <td>1633</td>\n",
              "      <td>91</td>\n",
              "      <td>1</td>\n",
              "      <td>1725</td>\n",
              "      <td>94.666667</td>\n",
              "      <td>5.275362</td>\n",
              "      <td>0.057971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>annotator_15</td>\n",
              "      <td>5774</td>\n",
              "      <td>314</td>\n",
              "      <td>0</td>\n",
              "      <td>6088</td>\n",
              "      <td>94.842313</td>\n",
              "      <td>5.157687</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>annotator_16</td>\n",
              "      <td>4769</td>\n",
              "      <td>292</td>\n",
              "      <td>0</td>\n",
              "      <td>5061</td>\n",
              "      <td>94.230389</td>\n",
              "      <td>5.769611</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>annotator_17</td>\n",
              "      <td>3283</td>\n",
              "      <td>202</td>\n",
              "      <td>0</td>\n",
              "      <td>3485</td>\n",
              "      <td>94.203730</td>\n",
              "      <td>5.796270</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>annotator_18</td>\n",
              "      <td>4841</td>\n",
              "      <td>326</td>\n",
              "      <td>3</td>\n",
              "      <td>5170</td>\n",
              "      <td>93.636364</td>\n",
              "      <td>6.305609</td>\n",
              "      <td>0.058027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>annotator_19</td>\n",
              "      <td>161</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>170</td>\n",
              "      <td>94.705882</td>\n",
              "      <td>5.294118</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>annotator_20</td>\n",
              "      <td>5809</td>\n",
              "      <td>316</td>\n",
              "      <td>1</td>\n",
              "      <td>6126</td>\n",
              "      <td>94.825335</td>\n",
              "      <td>5.158341</td>\n",
              "      <td>0.016324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>annotator_21</td>\n",
              "      <td>2773</td>\n",
              "      <td>177</td>\n",
              "      <td>0</td>\n",
              "      <td>2950</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>annotator_22</td>\n",
              "      <td>1644</td>\n",
              "      <td>97</td>\n",
              "      <td>4</td>\n",
              "      <td>1745</td>\n",
              "      <td>94.212034</td>\n",
              "      <td>5.558739</td>\n",
              "      <td>0.229226</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   vendor_user_id  correct  incorrect  ...  correct_p  incorrect_p    null_p\n",
              "0    annotator_01     1214         66  ...  94.843750     5.156250  0.000000\n",
              "1    annotator_02     7101        493  ...  93.483412     6.490258  0.026330\n",
              "2    annotator_03      585         45  ...  92.857143     7.142857  0.000000\n",
              "3    annotator_04     5960        457  ...  92.820433     7.117271  0.062296\n",
              "4    annotator_05     3256        219  ...  93.697842     6.302158  0.000000\n",
              "5    annotator_06     4945        391  ...  92.655050     7.326213  0.018737\n",
              "6    annotator_07     1990        184  ...  91.494253     8.459770  0.045977\n",
              "7    annotator_08     5878        656  ...  89.918923    10.035184  0.045893\n",
              "8    annotator_09     4536        324  ...  93.333333     6.666667  0.000000\n",
              "9    annotator_10      280         35  ...  88.888889    11.111111  0.000000\n",
              "10   annotator_11     6026        409  ...  93.629584     6.354879  0.015538\n",
              "11   annotator_12     5774        436  ...  92.979066     7.020934  0.000000\n",
              "12   annotator_13     6702        376  ...  94.687765     5.312235  0.000000\n",
              "13   annotator_14     1633         91  ...  94.666667     5.275362  0.057971\n",
              "14   annotator_15     5774        314  ...  94.842313     5.157687  0.000000\n",
              "15   annotator_16     4769        292  ...  94.230389     5.769611  0.000000\n",
              "16   annotator_17     3283        202  ...  94.203730     5.796270  0.000000\n",
              "17   annotator_18     4841        326  ...  93.636364     6.305609  0.058027\n",
              "18   annotator_19      161          9  ...  94.705882     5.294118  0.000000\n",
              "19   annotator_20     5809        316  ...  94.825335     5.158341  0.016324\n",
              "20   annotator_21     2773        177  ...  94.000000     6.000000  0.000000\n",
              "21   annotator_22     1644         97  ...  94.212034     5.558739  0.229226\n",
              "\n",
              "[22 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6XeH5GwWY6Lf"
      },
      "source": [
        "**Visualization**\n",
        "\n",
        "**Conclusion:**  \n",
        "Initially, all annotators are taken in to evaluation. From the percentage graph, the annotators are sorted into 2 groups, good and bad annotators, based on their individual correct percentages comparing with the average correct percentage (93.39%).\n",
        "\n",
        "*Good annotators:*\n",
        "*   annotator_01\n",
        "*   annotator_13\n",
        "*   annotator_14\n",
        "*   annotator_15\n",
        "*   annotator_16\n",
        "*   annotator_17\n",
        "*   annotator_19\n",
        "*   annotator_20\n",
        "\n",
        "*Bad annotators:*\n",
        "*   annotator_07\n",
        "*   annotator_18\n",
        "*   annotator_10\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Vkp9l5JNY5c5",
        "outputId": "f322948c-fa4a-48f3-e039-3c68a6c139ea"
      },
      "source": [
        "width = 0.8\n",
        "fs = 15\n",
        "\n",
        "\n",
        "# Load data\n",
        "annotator_result_count_df = pd.read_csv(\"annotator_result_count.csv\")\n",
        "annotators_quality_df = pd.read_csv(\"annotators_quality_assessment.csv\")\n",
        "annotators_quality = annotators_quality_df.values.tolist()\n",
        "\n",
        "\n",
        "# Amount display\n",
        "labels = annotators_quality_df['vendor_user_id']\n",
        "display_df = annotators_quality_df[['correct', 'incorrect', 'null']]\n",
        "ax1 = display_df.plot(kind='bar', stacked=True, use_index=False)\n",
        "ax1.set_xticklabels(labels)\n",
        "ax1.grid(True)\n",
        "ax1.set_title(\"Annotators assessment\", fontsize=fs)\n",
        "ax1.set_xlabel(\"Annotators\", fontsize=fs)\n",
        "ax1.set_ylabel(\"[count]\", fontsize=fs)\n",
        "ax1.legend(prop={'size': fs})\n",
        "ax1.axhline(y=annotator_result_count_df.mean().values, color='r', linestyle='-')\n",
        "ax1.axhline(y=annotator_result_count_df.std().values, color='purple', linestyle='-')\n",
        "ax1.legend(['mean', 'standard_deviation', 'correct', 'incorrect', 'null'])\n",
        "\n",
        "# - Add numbers to bars\n",
        "for p, correct_count, incorrect_count in zip(ax1.patches, annotators_quality_df['correct'].values, annotators_quality_df['incorrect'].values):\n",
        "    ax1.annotate(str(correct_count), (p.get_x(), p.get_height() + 10), fontsize=12)\n",
        "    ax1.annotate(str(incorrect_count), (p.get_x(), p.get_height() + incorrect_count + 150), fontsize=12)\n",
        "\n",
        "\n",
        "# Percentage display\n",
        "ax2 = annotators_quality_df[['correct_p', 'incorrect_p', 'null_p']].plot(kind='bar', stacked=True, use_index=False)\n",
        "ax2.set_ylim([85, 102])\n",
        "ax2.set_xticklabels(labels)\n",
        "ax2.grid(True)\n",
        "ax2.set_title(\"Annotators assessment in percentage\", fontsize=fs)\n",
        "ax2.set_xlabel(\"Annotators\", fontsize=fs)\n",
        "ax2.set_ylabel(\"[%]\", fontsize=fs)\n",
        "ax2.axhline(y=annotators_quality_df['correct_p'].mean(), color='r', linestyle='-')   # draw mean line\n",
        "ax2.legend(['mean', 'correct', 'incorrect', 'null'], prop={'size': 12})\n",
        "\n",
        "# - Add numbers to bars\n",
        "for p, correct_p, incorrect_p in zip(ax2.patches, annotators_quality_df['correct_p'].values, annotators_quality_df['incorrect_p'].values):\n",
        "    ax2.annotate(str(round(correct_p, 2)), (p.get_x(), 88))\n",
        "    ax2.annotate(str(round(incorrect_p, 2)), (p.get_x(), 97))\n",
        "\n",
        "\n",
        "# Pie chart of average correct, incorrect and null samples\n",
        "pie_labels = 'correct', 'incorrect', 'null'\n",
        "pie_values = [annotators_quality_df['correct_p'].mean(),\n",
        "              annotators_quality_df['incorrect_p'].mean(),\n",
        "              annotators_quality_df['null_p'].mean()]\n",
        "\n",
        "explode = (0, 0, 0.1)\n",
        "\n",
        "fig3, ax3 = plt.subplots()\n",
        "ax3.pie(pie_values, explode=explode, autopct='%1.3f%%', startangle=90, textprops={'fontsize': 14})\n",
        "ax3.axis('equal')\n",
        "ax3.set_title(\"Overall performance of all annotators\", fontsize=fs)\n",
        "ax3.legend(pie_labels, prop={'size': fs})\n",
        "\n",
        "\n",
        "annotators_quality_df['correct_p'].describe()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    22.000000\n",
              "mean     93.391462\n",
              "std       1.561272\n",
              "min      88.888889\n",
              "25%      92.887624\n",
              "50%      93.667103\n",
              "75%      94.557597\n",
              "max      94.843750\n",
              "Name: correct_p, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WMGq8kySi9-B"
      },
      "source": [
        "**Annotators selection based on standard deviation**  \n",
        "However, the evaluation is not fair some annotators’ sample sizes are far below the average.  \n",
        "*E.g.*: annotator_01 is assessed as good while his/her total sample is only ~1200 comparing to annotator_02 with more than 7000 samples.\n",
        "\n",
        "In order to evaluate correctly, only the annotators whose sample sizes are greater than the standard deviation value **$\\sigma$** = 2403.23 are selected. \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZhlheniPhv04"
      },
      "source": [
        "The following annotators are excluded from the evaluation:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "-RBzSPwYhwwh",
        "outputId": "b43bb5f7-a11e-4a77-f00d-3ea026c023f5"
      },
      "source": [
        "# Load data\n",
        "annotator_result_count_df = pd.read_csv(\"annotator_result_count.csv\")\n",
        "annotators_quality_df = pd.read_csv(\"annotators_quality_assessment.csv\")\n",
        "\n",
        "# Remove annotators whose total samples are less than the standard deviation level\n",
        "annotators_low_quality_df = annotators_quality_df.loc[annotators_quality_df['total'] < annotators_quality_df['total'].std()]\n",
        "annotators_low_quality_df"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>vendor_user_id</th>\n",
              "      <th>correct</th>\n",
              "      <th>incorrect</th>\n",
              "      <th>null</th>\n",
              "      <th>total</th>\n",
              "      <th>correct_p</th>\n",
              "      <th>incorrect_p</th>\n",
              "      <th>null_p</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>annotator_01</td>\n",
              "      <td>1214</td>\n",
              "      <td>66</td>\n",
              "      <td>0</td>\n",
              "      <td>1280</td>\n",
              "      <td>94.843750</td>\n",
              "      <td>5.156250</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>annotator_03</td>\n",
              "      <td>585</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>630</td>\n",
              "      <td>92.857143</td>\n",
              "      <td>7.142857</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>annotator_07</td>\n",
              "      <td>1990</td>\n",
              "      <td>184</td>\n",
              "      <td>1</td>\n",
              "      <td>2175</td>\n",
              "      <td>91.494253</td>\n",
              "      <td>8.459770</td>\n",
              "      <td>0.045977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>annotator_10</td>\n",
              "      <td>280</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>315</td>\n",
              "      <td>88.888889</td>\n",
              "      <td>11.111111</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>annotator_14</td>\n",
              "      <td>1633</td>\n",
              "      <td>91</td>\n",
              "      <td>1</td>\n",
              "      <td>1725</td>\n",
              "      <td>94.666667</td>\n",
              "      <td>5.275362</td>\n",
              "      <td>0.057971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>annotator_19</td>\n",
              "      <td>161</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>170</td>\n",
              "      <td>94.705882</td>\n",
              "      <td>5.294118</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>annotator_22</td>\n",
              "      <td>1644</td>\n",
              "      <td>97</td>\n",
              "      <td>4</td>\n",
              "      <td>1745</td>\n",
              "      <td>94.212034</td>\n",
              "      <td>5.558739</td>\n",
              "      <td>0.229226</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   vendor_user_id  correct  incorrect  ...  correct_p  incorrect_p    null_p\n",
              "0    annotator_01     1214         66  ...  94.843750     5.156250  0.000000\n",
              "2    annotator_03      585         45  ...  92.857143     7.142857  0.000000\n",
              "6    annotator_07     1990        184  ...  91.494253     8.459770  0.045977\n",
              "9    annotator_10      280         35  ...  88.888889    11.111111  0.000000\n",
              "13   annotator_14     1633         91  ...  94.666667     5.275362  0.057971\n",
              "18   annotator_19      161          9  ...  94.705882     5.294118  0.000000\n",
              "21   annotator_22     1644         97  ...  94.212034     5.558739  0.229226\n",
              "\n",
              "[7 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-RJrf0MAu-Kd"
      },
      "source": [
        "With this change, the good and bad annotators are reconsidered.  \n",
        "\n",
        "In particular, despite the high correct percentages, *annotator_01* and *annotator_14* are removed from the good annotators group as their sample sizes are too small. *Annotator_21* is taken into consideration.\n",
        "\n",
        "The bad annotator group removed *annotator_07* and *annotator_10* with the same reason and add 3 new annotators, *annotator_04*, *annotator_06* and *annotator_12*."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENnExzXoVQIb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2e68df00-18ba-43b8-c091-fd24600669a2"
      },
      "source": [
        "width = 0.8\n",
        "fs = 15\n",
        "\n",
        "\n",
        "# Load data\n",
        "annotator_result_count_df = pd.read_csv(\"annotator_result_count.csv\")\n",
        "annotators_quality_df = pd.read_csv(\"annotators_quality_assessment.csv\")\n",
        "\n",
        "\n",
        "# Remove annotators whose total samples are less than the standard deviation level\n",
        "annotators_quality_df = annotators_quality_df.loc[annotators_quality_df['total'] > annotators_quality_df['total'].std()]\n",
        "annotators_quality = annotators_quality_df.values.tolist()\n",
        "\n",
        "\n",
        "# Amount display\n",
        "labels = annotators_quality_df['vendor_user_id']\n",
        "display_df = annotators_quality_df[['correct', 'incorrect', 'null']]\n",
        "ax1 = display_df.plot(kind='bar', stacked=True, use_index=False)\n",
        "ax1.set_xticklabels(labels)\n",
        "ax1.grid(True)\n",
        "ax1.set_title(\"Annotators assessment\", fontsize=fs)\n",
        "ax1.set_xlabel(\"Annotators\", fontsize=fs)\n",
        "ax1.set_ylabel(\"[count]\", fontsize=fs)\n",
        "ax1.legend(prop={'size': fs})\n",
        "ax1.axhline(y=annotator_result_count_df.mean().values, color='r', linestyle='-')\n",
        "ax1.axhline(y=annotator_result_count_df.std().values, color='purple', linestyle='-')\n",
        "ax1.legend(['mean', 'standard_deviation', 'correct', 'incorrect', 'null'])\n",
        "\n",
        "# - Add numbers to bars\n",
        "for p, correct_count, incorrect_count in zip(ax1.patches, annotators_quality_df['correct'].values, annotators_quality_df['incorrect'].values):\n",
        "    ax1.annotate(str(correct_count), (p.get_x(), p.get_height() + 10), fontsize=12)\n",
        "    ax1.annotate(str(incorrect_count), (p.get_x(), p.get_height() + incorrect_count + 150), fontsize=12)\n",
        "\n",
        "\n",
        "# Percentage display\n",
        "ax2 = annotators_quality_df[['correct_p', 'incorrect_p', 'null_p']].plot(kind='bar', stacked=True, use_index=False)\n",
        "ax2.set_ylim([85, 102])\n",
        "ax2.set_xticklabels(labels)\n",
        "ax2.grid(True)\n",
        "ax2.set_title(\"Annotators assessment in percentage\", fontsize=fs)\n",
        "ax2.set_xlabel(\"Annotators\", fontsize=fs)\n",
        "ax2.set_ylabel(\"[%]\", fontsize=fs)\n",
        "ax2.axhline(y=annotators_quality_df['correct_p'].mean(), color='r', linestyle='-')   # draw mean line\n",
        "ax2.legend(['mean', 'correct', 'incorrect', 'null'], prop={'size': 12})\n",
        "\n",
        "# Add numbers to bars\n",
        "for p, correct_p, incorrect_p in zip(ax2.patches, annotators_quality_df['correct_p'].values, annotators_quality_df['incorrect_p'].values):\n",
        "    ax2.annotate(str(round(correct_p, 2)), (p.get_x(), 88))\n",
        "    ax2.annotate(str(round(incorrect_p, 2)), (p.get_x(), 97))\n",
        "\n",
        "\n",
        "# Pie chart of average correct, incorrect and null samples\n",
        "pie_labels = 'correct', 'incorrect', 'null'\n",
        "pie_values = [annotators_quality_df['correct_p'].mean(),\n",
        "              annotators_quality_df['incorrect_p'].mean(),\n",
        "              annotators_quality_df['null_p'].mean()]\n",
        "\n",
        "explode = (0, 0, 0.1)\n",
        "\n",
        "fig3, ax3 = plt.subplots()\n",
        "ax3.pie(pie_values, explode=explode, labels=pie_labels, autopct='%1.2f%%', shadow=True, startangle=90)\n",
        "ax3.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "\n",
        "annotators_quality_df['correct_p'].describe()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    15.000000\n",
              "mean     93.529569\n",
              "std       1.213275\n",
              "min      89.918923\n",
              "25%      93.156200\n",
              "50%      93.636364\n",
              "75%      94.217060\n",
              "max      94.842313\n",
              "Name: correct_p, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3Ogx0xkAcmu"
      },
      "source": [
        "**Good and bad annotators assessment**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ditQWR0AhRj"
      },
      "source": [
        "width = 0.8\n",
        "fs = 15\n",
        "\n",
        "\n",
        "def get_duration_data(result_key, index):\n",
        "    \"\"\"\n",
        "    Get the corresponding duration data based on result_key and index\n",
        "    :param result_key: key of anonymized_project result\n",
        "    :param index: index of items in result key\n",
        "    :return: a list of data\n",
        "    \"\"\"\n",
        "    vendor_user_id = ap_results[result_key]['results'][index]['user']['vendor_user_id']\n",
        "    duration = ap_results[result_key]['results'][index]['task_output']['duration_ms']\n",
        "    return [vendor_user_id, duration]\n",
        "\n",
        "\n",
        "def check_duration_condition(result_key, index, group):\n",
        "    \"\"\"\n",
        "    :param result_key: key of anonymized_project result\n",
        "    :param index: index of items in result key\n",
        "    :return: boolean value based on whether the answer data is corrupted and if user is is good annotators list\n",
        "    \"\"\"\n",
        "    cond_1 = ap_results[result_key]['results'][index]['task_output']['corrupt_data']\n",
        "    cond_2 = ap_results[result_key]['results'][index]['user']['vendor_user_id'] in group\n",
        "    return not cond_1 and cond_2\n",
        "\n",
        "\n",
        "# Load data\n",
        "annotator_result_count_df = pd.read_csv(\"annotator_result_count.csv\")\n",
        "annotators_quality_df = pd.read_csv(\"annotators_quality_assessment.csv\")\n",
        "\n",
        "\n",
        "def get_group_data(group, file_name=None):\n",
        "    \"\"\"\n",
        "    Function to retrieve data from a group of selected users\n",
        "    \"\"\"\n",
        "    # Get mean durations\n",
        "    # - Get durations by user\n",
        "    annotator_durations = []\n",
        "\n",
        "    for result_key in result_keys:\n",
        "        result_amount = len(ap_results[result_key]['results'])\n",
        "\n",
        "        # Collect durations which are not labeled as corrupted\n",
        "        row = [get_duration_data(result_key, i) for i in range(result_amount) if check_duration_condition(result_key, i, group)]\n",
        "\n",
        "        # - Add data to list\n",
        "        annotator_durations.extend(row)\n",
        "\n",
        "    annotator_durations_df = pd.DataFrame(data=annotator_durations, columns=['vendor_user_id', 'duration_ms'])\n",
        "\n",
        "\n",
        "    # - Get mean durations\n",
        "    selected_annotators_durations = []\n",
        "    for item in annotator_durations_df.groupby('vendor_user_id'):\n",
        "        name = item[0]\n",
        "        temp_df = item[1]['duration_ms']\n",
        "        temp_df = temp_df.to_frame()\n",
        "        selected_annotators_durations.append([name, np.asarray(temp_df['duration_ms'], dtype=np.float).mean()])\n",
        "\n",
        "    selected_annotators_durations_df = pd.DataFrame(selected_annotators_durations, columns=['vendor_user_id', 'mean_duration'])\n",
        "\n",
        "\n",
        "    # Add sample size column\n",
        "    result_count_df = annotator_result_count_df.loc[annotator_result_count_df['vendor_user_id'].isin(group)]\n",
        "    result_count = result_count_df['result_count'].to_list()\n",
        "    selected_annotators_durations_df['result_count'] = result_count\n",
        "\n",
        "\n",
        "    # Add correct_percent column\n",
        "    annotators_quality_percent_df = annotators_quality_df[['vendor_user_id', 'correct_p', 'incorrect_p', 'null_p']]\n",
        "\n",
        "    correct_percent_df = annotators_quality_percent_df.loc[annotators_quality_percent_df['vendor_user_id'].isin(group)]\n",
        "    correct_percent = correct_percent_df['correct_p'].to_list()\n",
        "    selected_annotators_durations_df['correct_p'] = correct_percent\n",
        "\n",
        "    if file_name is not None:\n",
        "        selected_annotators_durations_df.to_csv(file_name, index=False)\n",
        "\n",
        "    return selected_annotators_durations_df"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Az7L84lMxMph"
      },
      "source": [
        "From inspection, ***annotator_13*** and ***annotator_20*** are considered as **good annotators** as their sample sizes are far above the average level of 4130 samples. Their correct percentages are more than 94% (nearly 95%), higher than the average of 93.52%. Their annotation times are also below the average, which is 1289.9 ms.  \n",
        "\n",
        "Despite the lowest average annotation time and high accuracy of 94%, the low sample size make it difficult to make a conclusion of the performance of annotator_17."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "hG3hV9AsGCad",
        "outputId": "c19f14e7-77e5-44d7-99a8-d85747b24fdd"
      },
      "source": [
        "# Specifying good annotators group\n",
        "good_annotators = [\"annotator_13\", \"annotator_15\", \"annotator_16\", \"annotator_17\", \"annotator_20\", \"annotator_21\"]\n",
        "\n",
        "get_group_data(good_annotators)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>vendor_user_id</th>\n",
              "      <th>mean_duration</th>\n",
              "      <th>result_count</th>\n",
              "      <th>correct_p</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>annotator_13</td>\n",
              "      <td>1155.011161</td>\n",
              "      <td>7078</td>\n",
              "      <td>94.687765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>annotator_15</td>\n",
              "      <td>1365.291721</td>\n",
              "      <td>6088</td>\n",
              "      <td>94.842313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>annotator_16</td>\n",
              "      <td>1269.793124</td>\n",
              "      <td>5061</td>\n",
              "      <td>94.230389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>annotator_17</td>\n",
              "      <td>991.886370</td>\n",
              "      <td>3485</td>\n",
              "      <td>94.203730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>annotator_20</td>\n",
              "      <td>1173.149200</td>\n",
              "      <td>6126</td>\n",
              "      <td>94.825335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>annotator_21</td>\n",
              "      <td>1238.922373</td>\n",
              "      <td>2950</td>\n",
              "      <td>94.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  vendor_user_id  mean_duration  result_count  correct_p\n",
              "0   annotator_13    1155.011161          7078  94.687765\n",
              "1   annotator_15    1365.291721          6088  94.842313\n",
              "2   annotator_16    1269.793124          5061  94.230389\n",
              "3   annotator_17     991.886370          3485  94.203730\n",
              "4   annotator_20    1173.149200          6126  94.825335\n",
              "5   annotator_21    1238.922373          2950  94.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D3gX9VLdzMp0"
      },
      "source": [
        "All four annotators have high sample size with the accuracy below the average of 93.52%. However, beside *annotator_08*, the others three have the accuracy of 92%, which is close to the average line.\n",
        "\n",
        "Only *annotator_08* has the lowest accuracy percentage of 89.9% and high average annotation time of 1434 ms. This cause *annotator_08* to be considered as low performance annotator."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "oNKEasGMGEz8",
        "outputId": "27ce80e8-3060-465e-df00-18a9df6f5fd2"
      },
      "source": [
        "# Specifying bad annotators group\n",
        "bad_annotators = [\"annotator_04\", \"annotator_06\", \"annotator_08\", \"annotator_12\"]\n",
        "get_group_data(bad_annotators)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>vendor_user_id</th>\n",
              "      <th>mean_duration</th>\n",
              "      <th>result_count</th>\n",
              "      <th>correct_p</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>annotator_04</td>\n",
              "      <td>1113.926958</td>\n",
              "      <td>6421</td>\n",
              "      <td>92.820433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>annotator_06</td>\n",
              "      <td>1496.940217</td>\n",
              "      <td>5337</td>\n",
              "      <td>92.655050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>annotator_08</td>\n",
              "      <td>1434.702111</td>\n",
              "      <td>6537</td>\n",
              "      <td>89.918923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>annotator_12</td>\n",
              "      <td>1306.305475</td>\n",
              "      <td>6210</td>\n",
              "      <td>92.979066</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  vendor_user_id  mean_duration  result_count  correct_p\n",
              "0   annotator_04    1113.926958          6421  92.820433\n",
              "1   annotator_06    1496.940217          5337  92.655050\n",
              "2   annotator_08    1434.702111          6537  89.918923\n",
              "3   annotator_12    1306.305475          6210  92.979066"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    }
  ]
}