Classes_proportion.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GianmarcoLattaruolo/Vision_Project/blob/main/MappaGeografica.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "FPhvnGpkjnDL"
      },
      "source": [
        "# **Classes proportion**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sK8GFLzUQfQm",
        "outputId": "c826f791-b45f-4d61-b814-88004d50720e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "are we in Colab?: False\n"
          ]
        }
      ],
      "source": [
        "# with this line we can check if we are in colab or not\n",
        "import os\n",
        "import sys\n",
        "from pathlib import Path\n",
        "in_colab = 'google.colab' in sys.modules\n",
        "print(\"are we in Colab?:\",in_colab)\n",
        "\n",
        "cwd = Path(os.getcwd())\n",
        "if in_colab:\n",
        "    from google.colab import drive\n",
        "    drive.mount('/gdrive')\n",
        "    !pip install -q condacolab\n",
        "    import condacolab\n",
        "    condacolab.install()\n",
        "    os.chdir('/gdrive/MyDrive/GeoEstimation')\n",
        "    \n",
        "else:\n",
        "    #our defult wd in local should be Vision_Project\n",
        "    if str(cwd)[-14:] == 'Vision_Project':\n",
        "        os.chdir(cwd / 'GeoEstimation')\n",
        "    sys.path.append(cwd / 'GeoEstimation')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.4015869517302182 0.1692748512232753 0.392109323341415\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "#the default working directory is geoestimation\n",
        "r = pd.read_csv(r'\\resources\\yfcc4k.csv')\n",
        "n = r.shape[0]\n",
        "a,b,c = sum(r.LOC==0.0)/n,sum(r.LOC==1.0)/n,sum(r.LOC==2.0)/n\n",
        "print(a,b,c)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "g0NiaQs4kXKr"
      },
      "source": [
        "## Produce the csv with the proportions\n",
        "We want a .csv file with the proportions of images labelled as class 0, 1 or 2 in the wrong classified (wrong geolocated) and right classified images, these for all the cell partitionings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {},
      "outputs": [
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              "                                       img_id      p_key  pred_class  \\\n",
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              "                                        IMG_ID  LOC\n",
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              "                                    img_id   p_key  pred_class   pred_lat  \\\n",
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              "\n",
              "     pred_lng  \n",
              "0 -117.214523  \n",
              "1 -117.214523  \n",
              "2  151.215057  \n",
              "3  128.915405  \n",
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              "                                    IMG_ID  coarse_pred  middle_pred  \\\n",
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              "\n",
              "   fine_pred  \n",
              "0       6600  \n",
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              "                                    IMG_ID  LOC  coarse  middle   fine\n",
              "0  1000269685_e60e9cdfb4_1125_78841376@N00  0.0    2323    3970   5249\n",
              "1  1000304467_1a75a200b1_1296_78841376@N00  1.0    2323    3970   5249\n",
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              "4  1008804117_ce4e6fef8a_1349_97522422@N00  0.0    1374    1815   2002"
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              "                                    IMG_ID  coarse_pred  middle_pred  \\\n",
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              "4  1008804117_ce4e6fef8a_1349_97522422@N00          473          514   \n",
              "\n",
              "   fine_pred  LOC  coarse  middle   fine  \n",
              "0       6600  0.0    2323    3970   5249  \n",
              "1       5249  1.0    2323    3970   5249  \n",
              "2       4481  0.0    2323    3970   5249  \n",
              "3        326  2.0    3104    6469  10864  \n",
              "4        527  0.0    1374    1815   2002  "
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              "                                     IMG_ID  coarse_pred  middle_pred  \\\n",
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              "40     104419192_8551684362_41_64825499@N00           14           13   \n",
              "\n",
              "    fine_pred  LOC  coarse  middle  fine  \n",
              "1        5249  1.0    2323    3970  5249  \n",
              "24       5316  2.0    1080    4010  5316  \n",
              "38       2822  1.0    1023    1231  2822  \n",
              "39       2137  1.0    1439    1929  2137  \n",
              "40       5455  2.0      14      13  5455  "
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              "                                     IMG_ID  coarse_pred  middle_pred  \\\n",
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              "24     103577701_b68e15492e_32_73293249@N00         1080         4010   \n",
              "\n",
              "    fine_pred  LOC  coarse  middle  fine  \n",
              "1        5249  1.0    2323    3970  5249  \n",
              "9        8412  0.0    1660    2355  6257  \n",
              "22         79  2.0      79      80  1838  \n",
              "23         79  2.0      79      80  1838  \n",
              "24       5316  2.0    1080    4010  5316  "
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              "                                    IMG_ID  coarse_pred  middle_pred  \\\n",
              "9   1021054395_ca7cfe55b3_1061_7132415@N03         1660         2355   \n",
              "11  1029739337_ff06001d12_1361_7132415@N03         1660          835   \n",
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              "24    103577701_b68e15492e_32_73293249@N00         1080         4010   \n",
              "\n",
              "    fine_pred  LOC  coarse  middle  fine  \n",
              "9        8412  0.0    1660    2355  6257  \n",
              "11        923  0.0    1660    2355  9259  \n",
              "22         79  2.0      79      80  1838  \n",
              "23         79  2.0      79      80  1838  \n",
              "24       5316  2.0    1080    4010  5316  "
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              "      <td>278.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wrong_fine</th>\n",
              "      <td>758.0</td>\n",
              "      <td>595.0</td>\n",
              "      <td>1232.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 c0     c1      c2\n",
              "original      791.0  663.0  1510.0\n",
              "right_coarse   58.0   94.0   404.0\n",
              "wrong_coarse  733.0  569.0  1106.0\n",
              "right_middle   45.0   77.0   341.0\n",
              "wrong_middle  746.0  586.0  1169.0\n",
              "right_fine     33.0   68.0   278.0\n",
              "wrong_fine    758.0  595.0  1232.0"
            ]
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        },
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          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>c0</th>\n",
              "      <th>c1</th>\n",
              "      <th>c2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>original</th>\n",
              "      <td>0.266869</td>\n",
              "      <td>0.223684</td>\n",
              "      <td>0.509447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>right_coarse</th>\n",
              "      <td>0.104317</td>\n",
              "      <td>0.169065</td>\n",
              "      <td>0.726619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wrong_coarse</th>\n",
              "      <td>0.304402</td>\n",
              "      <td>0.236296</td>\n",
              "      <td>0.459302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>right_middle</th>\n",
              "      <td>0.097192</td>\n",
              "      <td>0.166307</td>\n",
              "      <td>0.736501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wrong_middle</th>\n",
              "      <td>0.298281</td>\n",
              "      <td>0.234306</td>\n",
              "      <td>0.467413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>right_fine</th>\n",
              "      <td>0.087071</td>\n",
              "      <td>0.179420</td>\n",
              "      <td>0.733509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wrong_fine</th>\n",
              "      <td>0.293230</td>\n",
              "      <td>0.230174</td>\n",
              "      <td>0.476596</td>\n",
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            "text/plain": [
              "                    c0        c1        c2\n",
              "original      0.266869  0.223684  0.509447\n",
              "right_coarse  0.104317  0.169065  0.726619\n",
              "wrong_coarse  0.304402  0.236296  0.459302\n",
              "right_middle  0.097192  0.166307  0.736501\n",
              "wrong_middle  0.298281  0.234306  0.467413\n",
              "right_fine    0.087071  0.179420  0.733509\n",
              "wrong_fine    0.293230  0.230174  0.476596"
            ]
          },
          "metadata": {},
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      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import json\n",
        "  \n",
        "# Opening JSON file\n",
        "#f_original = open('/gdrive/MyDrive/GeoEstimation/resources/im2gps_places365_mapping_h3.json')\n",
        "f_original = open(r'\\resources\\im2gps3k_places365_mapping_h3.json')\n",
        "df_predicted = pd.read_csv(r'\\resources\\predicted_coordinates3k.csv')\n",
        "localizable = pd.read_csv(r'\\resources\\im2gps3k_places365.csv')\n",
        "\n",
        "display(df_predicted.tail())\n",
        "\n",
        "original_test = json.load(f_original)\n",
        "original_test_df = pd.DataFrame.from_dict(original_test, orient='index')\n",
        "original_test_df.reset_index(inplace=True)\n",
        "original_test_df = original_test_df.rename({'index': 'IMG_ID', 0: 'coarse', 1: 'middle', 2: 'fine'}, axis=1)\n",
        "#original_test_df['IMG_ID'] = original_test_df['IMG_ID'].str.replace('.jpg', '')\n",
        "\n",
        "display(original_test_df.head())\n",
        "smaller_loc = localizable.loc[:, ['IMG_ID', 'LOC']]\n",
        "display(smaller_loc.head())\n",
        "loc_cell_df = smaller_loc.merge(on='IMG_ID',right=original_test_df)\n",
        "loc_cell_df.head()\n",
        "display(df_predicted.head())\n",
        "\n",
        "c = df_predicted[df_predicted.p_key=='coarse']\n",
        "c = c[['img_id','pred_class']]\n",
        "c.columns = ['IMG_ID','coarse_pred']\n",
        "m = df_predicted[df_predicted.p_key=='middle']\n",
        "m = m[['img_id','pred_class']]\n",
        "m.columns = ['IMG_ID','middle_pred']\n",
        "f = df_predicted[df_predicted.p_key=='hierarchy']\n",
        "f = f[['img_id','pred_class']]\n",
        "f.columns = ['IMG_ID','fine_pred']\n",
        "full = c.merge(on='IMG_ID', right = m).merge(on='IMG_ID',right = f)\n",
        "display(full.head())\n",
        "loc_cell_df['IMG_ID'] = loc_cell_df['IMG_ID'].str.replace('.jpg', '')\n",
        "display(loc_cell_df.head())\n",
        "\n",
        "entire = full.merge(on='IMG_ID', right = loc_cell_df)\n",
        "display(entire.head())\n",
        "entire_fine = entire[entire['fine'] == entire['fine_pred'] ]\n",
        "entire_middle = entire[entire['middle'] == entire['middle_pred']]\n",
        "entire_coarse = entire[entire['coarse'] == entire['coarse_pred']]\n",
        "display(entire_fine.head())  \n",
        "display(entire_middle.head())  \n",
        "display(entire_coarse.head())  \n",
        "n = entire.shape[0]\n",
        "\n",
        "original = np.asarray([sum(entire.LOC == 0.0),sum(entire.LOC == 1.0),sum(entire.LOC == 2.0)] )\n",
        "#original = original/n\n",
        "data = np.zeros((7,3))\n",
        "data[0,:] = original\n",
        "\n",
        "fine_prop = np.asarray([sum(entire_fine.LOC == 0.0),sum(entire_fine.LOC == 1.0),sum(entire_fine.LOC == 2.0)])\n",
        "den = entire_fine.shape[0]\n",
        "#fine_prop = fine_prop/den\n",
        "data[5,:] = fine_prop\n",
        "data[6,:] = original - data[5,:]\n",
        "\n",
        "middle_prop = np.asarray([sum(entire_middle.LOC == 0.0),sum(entire_middle.LOC == 1.0),sum(entire_middle.LOC == 2.0)])\n",
        "den = entire_middle.shape[0]\n",
        "#middle_prop = middle_prop/den\n",
        "data[3,:] = middle_prop\n",
        "data[4,:] = original - data[3,:]\n",
        "\n",
        "coarse_prop = np.asarray([sum(entire_coarse.LOC == 0.0),sum(entire_coarse.LOC == 1.0),sum(entire_coarse.LOC == 2.0)])\n",
        "den = entire_coarse.shape[0]\n",
        "#coarse_prop = coarse_prop/den\n",
        "data[1,:] = coarse_prop\n",
        "data[2,:] = original - data[1,:]\n",
        "\n",
        "proportion = pd.DataFrame(data = data, index = ['original','right_coarse','wrong_coarse','right_middle','wrong_middle','right_fine','wrong_fine'], columns= ['c0','c1','c2'])\n",
        "display(proportion.head(10))\n",
        "proportion  =proportion.divide(other = proportion.sum(axis = 1), axis = 0)\n",
        "display(proportion.head(10))\n",
        "\n",
        "proportion.to_csv(r'\\resources\\proportion_3ktest.csv')\n"
      ]
    }
  ],
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