Raj_Singh_19210213_predicting_memorability.ipynb

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  "metadata": {
    "colab": {
      "name": "ML.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZ6qIFA7FQFn",
        "colab_type": "code",
        "outputId": "03ff54d4-2df9-493b-ac27-326c4d690a0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "from google.colab import drive\n",
        "import os\n",
        "\n",
        "drive.mount('/content/gdrive/',force_remount=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MgrjiahSHx9e",
        "colab_type": "code",
        "outputId": "6cdfacfd-c8cb-4a76-f484-89532e025e3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "source": [
        "import time\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# loading bar\n",
        "# fix issue with tqdm and colab\n",
        "!pip install --force https://github.com/chengs/tqdm/archive/colab.zip\n",
        "from tqdm import tqdm_notebook as tqdm"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting https://github.com/chengs/tqdm/archive/colab.zip\n",
            "  Using cached https://github.com/chengs/tqdm/archive/colab.zip\n",
            "Building wheels for collected packages: tqdm\n",
            "  Building wheel for tqdm (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for tqdm: filename=tqdm-4.28.1-py2.py3-none-any.whl size=47867 sha256=686dc2cd874b44734bcc3bf881967a445a50aa5933273c4634c555941a24d2cc\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-_7x7pad9/wheels/41/18/ee/d5dd158441b27965855b1bbae03fa2d8a91fe645c01b419896\n",
            "Successfully built tqdm\n",
            "\u001b[31mERROR: spacy 2.2.4 has requirement tqdm<5.0.0,>=4.38.0, but you'll have tqdm 4.28.1 which is incompatible.\u001b[0m\n",
            "Installing collected packages: tqdm\n",
            "  Found existing installation: tqdm 4.28.1\n",
            "    Uninstalling tqdm-4.28.1:\n",
            "      Successfully uninstalled tqdm-4.28.1\n",
            "Successfully installed tqdm-4.28.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "tqdm"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pzldtXkMH8RD",
        "colab_type": "code",
        "outputId": "92aa7758-2fdb-4ad1-cfe5-cbbf15a85ff6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "iterations = 100\n",
        "\n",
        "for i in tqdm(range(iterations)):\n",
        "    time.sleep(0.1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tqdm/_tqdm_notebook.py:88: TqdmExperimentalWarning: Detect Google Colab 0.0.1a2 and thus load dummy ipywidgets package. Note that UI is different from that in Jupyter. See https://github.com/tqdm/tqdm/pull/640\n",
            "  \" See https://github.com/tqdm/tqdm/pull/640\".format(colab.__version__), TqdmExperimentalWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div style=\"display:flex;flex-direction:row;\"><span></span><progress style='margin:2px 4px;' max='100' value='100'></progress>100% 100/100 [00:10&lt;00:00,  9.68it/s]</div>"
            ],
            "text/plain": [
              "<tqdm._fake_ipywidgets.HBox object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NhLPOs5SFVKi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "root_path = 'gdrive/My Drive/CA684_Assignment/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dYCfeQhvFuKb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "os.chdir('gdrive/My Drive/CA684_Assignment/')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5XGRt2TKF4DX",
        "colab_type": "code",
        "outputId": "14e4ef9c-a75a-49db-b477-7e7dbc0ad8ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "!pwd"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/.shortcut-targets-by-id/1UWIdExqb5P94RLqkaiODcwFC2v7g3lB2/CA684_Assignment\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1oFejIUHckP",
        "colab_type": "code",
        "outputId": "f9a871ac-19ef-45e0-bd92-9b002e741428",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Additional_Resources  Complete_Sources\tTest-set\n",
            "Assignment_Overview   Dev-set\t\tTutorials\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASImc0f-Hf5F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "ground_truth_file = 'Dev-set/Ground-truth/ground-truth.csv'\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R2w4WuD7WSQG",
        "colab_type": "code",
        "outputId": "bb1417bc-7bc2-4db0-a4a2-275a03c2775f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "ground_truth_file = 'Dev-set/Ground-truth/ground-truth.csv'\n",
        "ground_truth = pd.read_csv(ground_truth_file)\n",
        "ground_truth.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>video</th>\n",
              "      <th>short-term_memorability</th>\n",
              "      <th>nb_short-term_annotations</th>\n",
              "      <th>long-term_memorability</th>\n",
              "      <th>nb_long-term_annotations</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>video3.webm</td>\n",
              "      <td>0.924</td>\n",
              "      <td>34</td>\n",
              "      <td>0.846</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>video4.webm</td>\n",
              "      <td>0.923</td>\n",
              "      <td>33</td>\n",
              "      <td>0.667</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>video6.webm</td>\n",
              "      <td>0.863</td>\n",
              "      <td>33</td>\n",
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              "      <td>0.922</td>\n",
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              "      <td>0.818</td>\n",
              "      <td>11</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>video10.webm</td>\n",
              "      <td>0.950</td>\n",
              "      <td>34</td>\n",
              "      <td>0.900</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "          video  ...  nb_long-term_annotations\n",
              "0   video3.webm  ...                        13\n",
              "1   video4.webm  ...                        12\n",
              "2   video6.webm  ...                        10\n",
              "3   video8.webm  ...                        11\n",
              "4  video10.webm  ...                        10\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N4tOmP35lGNk",
        "colab_type": "code",
        "outputId": "d138e986-abac-4107-f3db-f6406fa638d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        }
      },
      "source": [
        "ground_truth.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>short-term_memorability</th>\n",
              "      <th>nb_short-term_annotations</th>\n",
              "      <th>long-term_memorability</th>\n",
              "      <th>nb_long-term_annotations</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6000.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.860243</td>\n",
              "      <td>36.291500</td>\n",
              "      <td>0.778942</td>\n",
              "      <td>12.764667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.080655</td>\n",
              "      <td>8.356285</td>\n",
              "      <td>0.144692</td>\n",
              "      <td>3.544815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.388000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.811000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.867000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.923000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>14.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.989000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       short-term_memorability  ...  nb_long-term_annotations\n",
              "count              6000.000000  ...               6000.000000\n",
              "mean                  0.860243  ...                 12.764667\n",
              "std                   0.080655  ...                  3.544815\n",
              "min                   0.388000  ...                  9.000000\n",
              "25%                   0.811000  ...                 10.000000\n",
              "50%                   0.867000  ...                 12.000000\n",
              "75%                   0.923000  ...                 14.000000\n",
              "max                   0.989000  ...                 40.000000\n",
              "\n",
              "[8 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q0pXIv9SmkUL",
        "colab_type": "code",
        "outputId": "c41784aa-76cf-4831-8cdc-fcefa6e32f17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import seaborn as sns\n",
        "import plotly as plt\n",
        "for label in ['short-term_memorability', 'long-term_memorability']:\n",
        "  sns.distplot(ground_truth[label], hist=False, label=label)\n",
        "plt.xlabel('memorability')\n",
        "plt.title('memorability density plot')\n",
        "\n",
        "None"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ERROR:root:Internal Python error in the inspect module.\n",
            "Below is the traceback from this internal error.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\", line 2882, in run_code\n",
            "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "  File \"<ipython-input-159-7436e384f0d3>\", line 5, in <module>\n",
            "    plt.xlabel('memorability')\n",
            "AttributeError: module 'plotly' has no attribute 'xlabel'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\", line 1823, in showtraceback\n",
            "    stb = value._render_traceback_()\n",
            "AttributeError: 'AttributeError' object has no attribute '_render_traceback_'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/ultratb.py\", line 1132, in get_records\n",
            "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/ultratb.py\", line 313, in wrapped\n",
            "    return f(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/core/ultratb.py\", line 358, in _fixed_getinnerframes\n",
            "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
            "  File \"/usr/lib/python3.6/inspect.py\", line 1490, in getinnerframes\n",
            "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
            "  File \"/usr/lib/python3.6/inspect.py\", line 1448, in getframeinfo\n",
            "    filename = getsourcefile(frame) or getfile(frame)\n",
            "  File \"/usr/lib/python3.6/inspect.py\", line 696, in getsourcefile\n",
            "    if getattr(getmodule(object, filename), '__loader__', None) is not None:\n",
            "  File \"/usr/lib/python3.6/inspect.py\", line 725, in getmodule\n",
            "    file = getabsfile(object, _filename)\n",
            "  File \"/usr/lib/python3.6/inspect.py\", line 709, in getabsfile\n",
            "    return os.path.normcase(os.path.abspath(_filename))\n",
            "  File \"/usr/lib/python3.6/posixpath.py\", line 383, in abspath\n",
            "    cwd = os.getcwd()\n",
            "OSError: [Errno 107] Transport endpoint is not connected\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m"
          ]
        },
        {
          "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": "L-D2h-EWp2N4",
        "colab_type": "text"
      },
      "source": [
        "Examine Videos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B1gRmpWep3Nd",
        "colab_type": "code",
        "outputId": "295bd52a-8b14-4467-a656-8a1f48207621",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        }
      },
      "source": [
        "# video captions\n",
        "video_captions = list(open('Dev-set/Captions/dev-set_video-captions.txt', 'r'))\n",
        "video_captions = [i.split('\\t') for i in video_captions]\n",
        "video_captions = [[a, b.strip()] for a, b in video_captions]\n",
        "video_captions = pd.DataFrame(video_captions, columns=['video', 'caption'])\n",
        "video_captions.head(10)"
      ],
      "execution_count": 0,
      "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>video</th>\n",
              "      <th>caption</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>video3.webm</td>\n",
              "      <td>blonde-woman-is-massaged-tilt-down</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>video4.webm</td>\n",
              "      <td>roulette-table-spinning-with-ball-in-closeup-shot</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>video6.webm</td>\n",
              "      <td>khr-gangsters</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>video8.webm</td>\n",
              "      <td>medical-helicopter-hovers-at-airport</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>video10.webm</td>\n",
              "      <td>couple-relaxing-on-picnic-crane-shot</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>video12.webm</td>\n",
              "      <td>passing-by-older-man-reading</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>video13.webm</td>\n",
              "      <td>christmas-village-and-national-christmas-tree</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>video16.webm</td>\n",
              "      <td>unhappy-friends-looking-for-something-interest...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>video17.webm</td>\n",
              "      <td>hoover-dam-tourists-traffic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>video18.webm</td>\n",
              "      <td>happy-girl-stretching-and-relaxing-on-the-station</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          video                                            caption\n",
              "0   video3.webm                 blonde-woman-is-massaged-tilt-down\n",
              "1   video4.webm  roulette-table-spinning-with-ball-in-closeup-shot\n",
              "2   video6.webm                                      khr-gangsters\n",
              "3   video8.webm               medical-helicopter-hovers-at-airport\n",
              "4  video10.webm               couple-relaxing-on-picnic-crane-shot\n",
              "5  video12.webm                       passing-by-older-man-reading\n",
              "6  video13.webm      christmas-village-and-national-christmas-tree\n",
              "7  video16.webm  unhappy-friends-looking-for-something-interest...\n",
              "8  video17.webm                        hoover-dam-tourists-traffic\n",
              "9  video18.webm  happy-girl-stretching-and-relaxing-on-the-station"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_rWVmQ4zJmnB",
        "colab_type": "text"
      },
      "source": [
        "*************************\n",
        "*************************\n",
        "IMPORTING!!!\n",
        "*************************\n",
        "*************************"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Q6PcxYEyqu_-",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from string import punctuation\n",
        "from collections import Counter\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import glob\n",
        "import nltk"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wmoixdQv9jPm",
        "colab_type": "text"
      },
      "source": [
        "********************************\n",
        "********************************\n",
        "LOADING FEATURES\n",
        "********************************\n",
        "********************************"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PCahZn-dqt4R",
        "colab": {}
      },
      "source": [
        "#Function to load C3D features\n",
        "def load_c3d(captions, c3dPath):\n",
        "    files = list(captions[\"video\"].values)\n",
        "    c3dfeatures = []\n",
        "    for file in files:\n",
        "        file = c3dPath+file[:-4]+'txt'\n",
        "        c3dfeatures.append(np.loadtxt(file))\n",
        "    #print(type(c3dfeatures))\n",
        "    return c3dfeatures\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ZUr_4C6qUxL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Function to load HMP features\n",
        "def load_hmp(captions, hmp_path):\n",
        "    files = list(captions[\"video\"].values)\n",
        "    hmp_features = []\n",
        "    for file in files:\n",
        "        file = hmp_path+file[:-4]+'txt'\n",
        "        with open(file) as f:\n",
        "            for line in f:\n",
        "                pairs=line.split()\n",
        "                HMP_temp = { int(p.split(':')[0]) : float(p.split(':')[1]) for p in pairs}\n",
        "                HMP = np.zeros(6075)\n",
        "            for idx in HMP_temp.keys():\n",
        "                HMP[idx-1] = HMP_temp[idx]\n",
        "            hmp_features.append(HMP)\n",
        "    return hmp_features"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0mbd_DUKgQnr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Function to load the captions into dataframes\n",
        "def load_captions(filename):\n",
        "    video_name = []\n",
        "    captions = []\n",
        "    dataframe = pd.DataFrame()\n",
        "    with open(filename) as file:\n",
        "        for line in file:\n",
        "            pair = line.split() #each line in the text file contains to words so, this code will split them into two words\n",
        "            video_name.append(pair[0]) #first word will be assigned as video name\n",
        "            captions.append(pair[1]) #second word will be assigned as caption\n",
        "        dataframe['video']=video_name #setting these two as column names of dataframe\n",
        "        dataframe['caption']=captions\n",
        "    return dataframe"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9AxMTKqgajy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Function to calculate Spearman coefficient scores\n",
        "def Get_score(Y_pred,Y_true):\n",
        "    '''Calculate the Spearmann\"s correlation coefficient'''\n",
        "    Y_pred = np.squeeze(Y_pred)\n",
        "    Y_true = np.squeeze(Y_true)\n",
        "    if Y_pred.shape != Y_true.shape:\n",
        "        print('Input shapes don\\'t match!')\n",
        "    else:\n",
        "        if len(Y_pred.shape) == 1:\n",
        "            Res = pd.DataFrame({'Y_true':Y_true,'Y_pred':Y_pred})\n",
        "            score_mat = Res[['Y_true','Y_pred']].corr(method='spearman',min_periods=1)\n",
        "            print('The Spearman\\'s correlation coefficient is: %.3f' % score_mat.iloc[1][0])\n",
        "        else:\n",
        "            for ii in range(Y_pred.shape[1]):\n",
        "                Get_score(Y_pred[:,ii],Y_true[:,ii])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WnAv7-99ghF8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#load the ground truth dataset\n",
        "csv_path ='Dev-set/Ground-truth/ground-truth.csv'\n",
        "dataset = pd.read_csv(csv_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hVkEA7dQgtTX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#load captions\n",
        "#captions_pathtest ='Test-set/Captions_test/test-set-1_video-captions.txt'\n",
        "#captionstest = load_captions(captions_pathtest)\n",
        "\n",
        "#load captions\n",
        "captions_path ='Dev-set/Captions/dev-set_video-captions.txt'\n",
        "captions = load_captions(captions_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3BM0Z7kD4U2y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#load captions\n",
        "#captions_pathtest ='Dev-set/Captions/dev-set_video-captions.txt'\n",
        "#dev_captions = load_captions(captions_pathtest)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0LTXVU6gwZz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#load C3D features\n",
        "c3dPath = './Dev-set/C3D/'\n",
        "c3dfeatures = load_c3d(captions,c3dPath)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KQevJsEohd68",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#load HMP features\n",
        "hmp_path = './Dev-set/HMP/'\n",
        "hmp_features = load_hmp(captions,hmp_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gfldM0E2iIbL",
        "colab_type": "code",
        "outputId": "0b1584a6-cad9-4a00-b9ae-bc087d638726",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": 0,
      "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>video</th>\n",
              "      <th>short-term_memorability</th>\n",
              "      <th>nb_short-term_annotations</th>\n",
              "      <th>long-term_memorability</th>\n",
              "      <th>nb_long-term_annotations</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>video3.webm</td>\n",
              "      <td>0.924</td>\n",
              "      <td>34</td>\n",
              "      <td>0.846</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>video4.webm</td>\n",
              "      <td>0.923</td>\n",
              "      <td>33</td>\n",
              "      <td>0.667</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>video6.webm</td>\n",
              "      <td>0.863</td>\n",
              "      <td>33</td>\n",
              "      <td>0.700</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>video8.webm</td>\n",
              "      <td>0.922</td>\n",
              "      <td>33</td>\n",
              "      <td>0.818</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>video10.webm</td>\n",
              "      <td>0.950</td>\n",
              "      <td>34</td>\n",
              "      <td>0.900</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          video  ...  nb_long-term_annotations\n",
              "0   video3.webm  ...                        13\n",
              "1   video4.webm  ...                        12\n",
              "2   video6.webm  ...                        10\n",
              "3   video8.webm  ...                        11\n",
              "4  video10.webm  ...                        10\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNkcM5N6vVU3",
        "colab_type": "code",
        "outputId": "fe882d76-b36f-442e-ac32-20a25ae67c9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "captions.head()"
      ],
      "execution_count": 0,
      "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>video</th>\n",
              "      <th>caption</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>video3.webm</td>\n",
              "      <td>blonde-woman-is-massaged-tilt-down</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>video4.webm</td>\n",
              "      <td>roulette-table-spinning-with-ball-in-closeup-shot</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>video6.webm</td>\n",
              "      <td>khr-gangsters</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>video8.webm</td>\n",
              "      <td>medical-helicopter-hovers-at-airport</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>video10.webm</td>\n",
              "      <td>couple-relaxing-on-picnic-crane-shot</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          video                                            caption\n",
              "0   video3.webm                 blonde-woman-is-massaged-tilt-down\n",
              "1   video4.webm  roulette-table-spinning-with-ball-in-closeup-shot\n",
              "2   video6.webm                                      khr-gangsters\n",
              "3   video8.webm               medical-helicopter-hovers-at-airport\n",
              "4  video10.webm               couple-relaxing-on-picnic-crane-shot"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-Xzwcht2r12",
        "colab_type": "code",
        "outputId": "215c7bf4-087e-4545-f926-4901ba8d1d30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83
        }
      },
      "source": [
        "print(f'Ground Truth : {dataset.shape}')\n",
        "print(f'Dev Captions : {captions.shape}')\n",
        "print(f'C3D          : ({len(c3dfeatures)})')\n",
        "print(f'HMP          : ({len(hmp_features)})')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ground Truth : (6000, 5)\n",
            "Dev Captions : (6000, 2)\n",
            "C3D          : (6000)\n",
            "HMP          : (6000)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oTwhxYA74ImT",
        "colab_type": "text"
      },
      "source": [
        "********************************\n",
        "********************************\n",
        "Data Preprocessing\n",
        "********************************\n",
        "********************************\n",
        "\n",
        "USING COUNTVECTORIZER ON CAPTION FEATURE\n",
        "********************************\n",
        "********************************"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1bubo2227G_",
        "colab_type": "code",
        "outputId": "481ed640-ee7f-485a-c818-426e65849192",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        }
      },
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ftz2CoX5ZjY",
        "colab_type": "code",
        "outputId": "c4c34990-d49a-4de2-c6ab-b27b32204529",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "\n",
        "#loading the nltk stopwords of English\n",
        "stopwords = nltk.corpus.stopwords.words('english')\n",
        "print(f'Length of Stopwords: {len(stopwords)}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Length of Stopwords: 179\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hwSlHndb-Qy9",
        "colab_type": "text"
      },
      "source": [
        "********************************\n",
        "********************************\n",
        "CLEANING DEVELOPMENT SET CAPTIONS\n",
        "********************************\n",
        "********************************"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwkJMMCX55e-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -q pyprind"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TgQRLY666EEu",
        "colab_type": "code",
        "outputId": "3c2f1657-8614-4501-a06f-c368d26e61ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "!pip install PyPrind\n",
        "#Removing punctuations and stop words from captions\n",
        "# setup prograss tracker\n",
        "#pbar = pyprind.ProgBar(len(captions['caption']), title='Counting word occurrences')\n",
        "for i, cap in enumerate(captions['caption']):\n",
        "    # replace punctuations with space\n",
        "    # convert words to lower case \n",
        "    text = ''.join([c if c not in punctuation else ' ' for c in cap]).lower()\n",
        "    #removing stopwords\n",
        "    rmv_stopwords= ' '.join([word for word in text.split() if word not in stopwords])\n",
        "    captions.loc[i,'caption'] = rmv_stopwords #updating the original captions \n",
        "    #pbar.update()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: PyPrind in /usr/local/lib/python3.6/dist-packages (2.11.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQQlFaKw6iRc",
        "colab_type": "code",
        "outputId": "92afdfe0-dee8-47aa-a6a3-7415a6a37c40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "captions.head()"
      ],
      "execution_count": 0,
      "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>video</th>\n",
              "      <th>caption</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>video3.webm</td>\n",
              "      <td>blonde woman massaged tilt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>video4.webm</td>\n",
              "      <td>roulette table spinning ball closeup shot</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>video6.webm</td>\n",
              "      <td>khr gangsters</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>video8.webm</td>\n",
              "      <td>medical helicopter hovers airport</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>video10.webm</td>\n",
              "      <td>couple relaxing picnic crane shot</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          video                                    caption\n",
              "0   video3.webm                 blonde woman massaged tilt\n",
              "1   video4.webm  roulette table spinning ball closeup shot\n",
              "2   video6.webm                              khr gangsters\n",
              "3   video8.webm          medical helicopter hovers airport\n",
              "4  video10.webm          couple relaxing picnic crane shot"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sl-2ZCv673ZM",
        "colab_type": "code",
        "outputId": "6f475272-4a56-4fc2-b651-f14281fcab7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "\n",
        "#implementing bag of words for the combined captions\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "vectorizer = CountVectorizer(analyzer = \"word\",max_features=3112) \n",
        "captions_bag = vectorizer.fit_transform(captions.caption).toarray()\n",
        "type(captions_bag)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VpHq1Ly-8LkE",
        "colab_type": "code",
        "outputId": "ce5d25c0-3492-40e2-f749-5d2259e1f197",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "captions_bag.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6000, 3112)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8TZB_epXAD6q",
        "colab_type": "text"
      },
      "source": [
        "********************************\n",
        "********************************\n",
        "APPLYING RNN ON CAPTIONS\n",
        "********************************\n",
        "********************************"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckifnrFmAAph",
        "colab_type": "code",
        "outputId": "c1c1a0c1-9d54-4503-f6ac-58f011f661db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "from keras.preprocessing.text import Tokenizer"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C4v6kNj4AAmI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "captions_pathtest ='Test-set/Captions_test/test-set-1_video-captions.txt'\n",
        "captionstest = load_captions(captions_pathtest)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "29Q-pNVC_ALS",
        "colab_type": "text"
      },
      "source": [
        "********************************\n",
        "********************************\n",
        "TEXT MAPPING\n",
        "********************************\n",
        "********************************"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QFfRX8Fn_K1v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "counts = Counter()\n",
        "counts.update(text.split())\n",
        "len_token = len(counts) # create length of token\n",
        "tokenizer = Tokenizer(num_words=len_token) # use tokenizer\n",
        "#print(len_token)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F_Nn8wKO_yX6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tokenizer.fit_on_texts(list(captionstest.caption.values))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBFnBjU8_6Xc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "one_hot_res = tokenizer.texts_to_matrix(list(captions.caption.values),mode='binary')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oW3WxrCt__Ww",
        "colab_type": "code",
        "outputId": "6be3a4bc-254b-4225-9066-5951551aef54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "one_hot_res.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6000, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_21le-Q__TB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# For RNN we will need to use sequence embedding instead of one hot encoding\n",
        "## Sequence Encoding of the text\n",
        "sequences = tokenizer.texts_to_sequences(list(captions.caption.values))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QhgCtgp4__Qq",
        "colab_type": "code",
        "outputId": "0bd710c5-79a9-4e39-d160-e12a884bfb4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "## set max length of all dimensions to 50\n",
        "max_length=50\n",
        "X_seq = np.zeros((len(sequences),max_length))\n",
        "for i in range(len(sequences)):\n",
        "    n = len(sequences[i])\n",
        "    if n==0:\n",
        "        print(i)\n",
        "    else:\n",
        "        X_seq[i,-n:] = sequences[i]\n",
        "X_seq.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6000, 50)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuuhatVwBaee",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras import Sequential\n",
        "from keras import Sequential\n",
        "from keras import layers\n",
        "from keras import regularizers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R2MA5fupAkrg",
        "colab_type": "code",
        "outputId": "f4dab6b6-b703-4cef-bdab-629ea10efd94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 797
        }
      },
      "source": [
        "## sequences will be our X variable\n",
        "## predictor will be our Y variable\n",
        "predictor = dataset[['short-term_memorability','long-term_memorability']].values\n",
        "Y = predictor\n",
        "X = X_seq\n",
        "## Train-Test Split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "## Recurrent Neural Network Model \n",
        "## All hyperparameters are selected using GridSearch and Trial & Error Approach\n",
        "\n",
        "model=Sequential() # Create Sequential NN model\n",
        "\n",
        "## add Embedding layer for RNN to map our data into a format suitable for LSTM layer\n",
        "model.add(layers.Embedding(input_dim=5191, output_dim=20, input_length=50, init='uniform'))\n",
        "\n",
        "## add LSTM layer for some hidden layer and memory into the network\n",
        "model.add(layers.LSTM(150, activation='selu', recurrent_initializer='uniform', kernel_initializer='zeros', kernel_regularizer=regularizers.l2(0.001)))\n",
        "model.add(layers.Dropout(0.2))\n",
        "\n",
        "## layer of 30 neurons to map down from 150 hidden neurons with ridge regularizer\n",
        "model.add(layers.Dense(30, activation='selu', kernel_regularizer=regularizers.l2(0.001)))\n",
        "\n",
        "## Dropout layer\n",
        "model.add(layers.Dropout(0.6))\n",
        "\n",
        "## Output layer of 2 neurons for each score with sigmoid activation \n",
        "model.add(layers.Dense(2, activation='sigmoid'))\n",
        "\n",
        "## Compile the model \n",
        "# Optimizer Adamax\n",
        "model.compile(optimizer='adamax',loss='mse',metrics=['accuracy'])\n",
        "\n",
        "\n",
        "## Training the model \n",
        "# Number of epochs also comes from Gridsearch\n",
        "history = model.fit(X_train,Y_train,epochs=10, validation_data=(X_test,Y_test))\n",
        "\n",
        "# visualizing the model\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(1,len(loss)+1)\n",
        "\n",
        "plt.plot(epochs,loss,'bo',label='Training loss')\n",
        "plt.plot(epochs,val_loss,'b',label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "#plt.figure()\n",
        "#acc = history.history['acc']\n",
        "#val_acc = history.history['val_acc']\n",
        "#plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "#plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "#plt.title('Training and validation accuracy')\n",
        "#plt.xlabel('Epochs')\n",
        "#plt.ylabel('Acc')\n",
        "#plt.legend()\n",
        "#plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: UserWarning:\n",
            "\n",
            "Update your `Embedding` call to the Keras 2 API: `Embedding(input_dim=5191, output_dim=20, input_length=50, embeddings_initializer=\"uniform\")`\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/indexed_slices.py:434: UserWarning:\n",
            "\n",
            "Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 4800 samples, validate on 1200 samples\n",
            "Epoch 1/10\n",
            "4800/4800 [==============================] - 16s 3ms/step - loss: 0.0492 - accuracy: 0.6360 - val_loss: 0.0287 - val_accuracy: 0.7100\n",
            "Epoch 2/10\n",
            "4800/4800 [==============================] - 16s 3ms/step - loss: 0.0284 - accuracy: 0.6702 - val_loss: 0.0206 - val_accuracy: 0.7100\n",
            "Epoch 3/10\n",
            "4800/4800 [==============================] - 16s 3ms/step - loss: 0.0226 - accuracy: 0.6783 - val_loss: 0.0184 - val_accuracy: 0.7100\n",
            "Epoch 4/10\n",
            "4800/4800 [==============================] - 16s 3ms/step - loss: 0.0199 - accuracy: 0.6842 - val_loss: 0.0160 - val_accuracy: 0.7100\n",
            "Epoch 5/10\n",
            "4800/4800 [==============================] - 16s 3ms/step - loss: 0.0186 - accuracy: 0.6844 - val_loss: 0.0157 - val_accuracy: 0.7100\n",
            "Epoch 6/10\n",
            "4800/4800 [==============================] - 16s 3ms/step - loss: 0.0176 - accuracy: 0.6971 - val_loss: 0.0148 - val_accuracy: 0.7100\n",
            "Epoch 7/10\n",
            "4800/4800 [==============================] - 16s 3ms/step - loss: 0.0172 - accuracy: 0.7015 - val_loss: 0.0146 - val_accuracy: 0.7100\n",
            "Epoch 8/10\n",
            "4800/4800 [==============================] - 15s 3ms/step - loss: 0.0167 - accuracy: 0.7004 - val_loss: 0.0149 - val_accuracy: 0.7100\n",
            "Epoch 9/10\n",
            "4800/4800 [==============================] - 16s 3ms/step - loss: 0.0167 - accuracy: 0.7010 - val_loss: 0.0145 - val_accuracy: 0.7100\n",
            "Epoch 10/10\n",
            "4800/4800 [==============================] - 16s 3ms/step - loss: 0.0161 - accuracy: 0.7027 - val_loss: 0.0143 - val_accuracy: 0.7100\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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2AIMMu46Akhm",
        "colab_type": "code",
        "outputId": "60b2fdbe-0860-401d-a7e2-d5ba6eabbad3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "predictions = model.predict(X_test)\n",
        "predictions.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1200, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EjMPmHYPAkUT",
        "colab_type": "code",
        "outputId": "b8cc65dd-7b62-4bf5-b411-c5fcdd5b3895",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "Get_score(predictions, Y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Spearman's correlation coefficient is: 0.167\n",
            "The Spearman's correlation coefficient is: 0.076\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "snaqhmrd_-MH",
        "colab_type": "text"
      },
      "source": [
        "*************************************\n",
        "*************************************\n",
        "#COMBINING CAPTIONS AND C3D#\n",
        "*************************************\n",
        "*************************************"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9IKsj7M8NdG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "captions_c3d = (captions_bag.tolist())\n",
        "counter = 0\n",
        "for item in range(6000):\n",
        "    captions_c3d[counter] = np.append(captions_c3d[counter],c3dfeatures[counter],axis=0)\n",
        "    counter = counter+1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PYT2sQI-8TGw",
        "colab_type": "code",
        "outputId": "92d155ee-8913-478f-9ab1-e1dd7fa1a0b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "\n",
        "len(captions_c3d[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3213"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HnDxJDA-8Vpz",
        "colab_type": "code",
        "outputId": "447622a4-649a-4ec8-c4b4-f4354324c686",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "len(captions_c3d)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MmsewX9Y8b2Y",
        "colab_type": "text"
      },
      "source": [
        "*************************************\n",
        "*************************************\n",
        "#Combining Captions, C3D & HMP#\n",
        "*************************************\n",
        "*************************************"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UrrnaGQs8Ypn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "captions_c3d_hmp = captions_c3d\n",
        "counter = 0\n",
        "for item in range(6000):\n",
        "    captions_c3d_hmp[counter] = np.append(captions_c3d_hmp[counter],hmp_features[counter],axis=0)\n",
        "    counter = counter+1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJxrX4oQ8gl2",
        "colab_type": "code",
        "outputId": "aa8a6349-ae02-439b-e18d-f22300d90e05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "len(captions_c3d_hmp[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9288"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xiiCwj8-8ivT",
        "colab_type": "code",
        "outputId": "d4e842fa-ed23-40d6-d074-c1bc312395cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "len(captions_c3d_hmp)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNTlA52_8oR6",
        "colab_type": "text"
      },
      "source": [
        "**Now we have the following features**\n",
        "\n",
        "\n",
        "\n",
        "*   Captions in: captions_bag\n",
        "*   C3D in: c3dfeatures\n",
        "*   HMP in: hmp_features\n",
        "*   Captions and C3D:  captions_c3d\n",
        "*   Captions, C3D and HMP in: captions_c3d_hmp\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZX-HyVD8uaa",
        "colab_type": "text"
      },
      "source": [
        "**************************************\n",
        "**************************************\n",
        "\n",
        "#EXPERIMEMTS WITH DIFFERENT MODELS AND FEATURES/COMBINATIONS#\n",
        "\n",
        "**************************************\n",
        "**************************************"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b8KP2XOsCPCW",
        "colab_type": "text"
      },
      "source": [
        "*************************************\n",
        "**RANDOM FOREST WITH CAPTIONS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wps0y2-p8k_u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = captions_bag\n",
        "y = dataset[['short-term_memorability','long-term_memorability']].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qQPUd-C8xzn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjWRkVxE80uq",
        "colab_type": "code",
        "outputId": "93b457ec-7ecd-407a-fe44-c8a8983fa55e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83
        }
      },
      "source": [
        "print('X_train ', X_train.shape)\n",
        "print('X_test  ', X_test.shape)\n",
        "print('Y_train ', y_train.shape)\n",
        "print('Y_test  ', y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train  (4800, 3112)\n",
            "X_test   (1200, 3112)\n",
            "Y_train  (4800, 2)\n",
            "Y_test   (1200, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2tSVVf483l0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "captions_rf = RandomForestRegressor(n_estimators=100,random_state=45)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mz5qQcma86Xt",
        "colab_type": "code",
        "outputId": "065f5025-5981-4336-bc58-9e9d06397204",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        }
      },
      "source": [
        "\n",
        "captions_rf.fit(X_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                      random_state=45, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-zkcpv-88f7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "captions_pred = captions_rf.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hXt-drMVECt5",
        "colab_type": "code",
        "outputId": "aa0b1c54-7127-471f-8d69-22992da37543",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "Get_score(captions_pred, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Spearman's correlation coefficient is: 0.409\n",
            "The Spearman's correlation coefficient is: 0.176\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PFQOXke5EG3t",
        "colab_type": "text"
      },
      "source": [
        "****************************\n",
        "**SVR with Captions**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhVMSpiSEEVp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "svr_X = captions_bag\n",
        "svr_y_short = dataset[['short-term_memorability']].values\n",
        "svr_y_long = dataset[['long-term_memorability']].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SjXeNZJDEIwi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# Splitting the dataset into the Training set and Test set\n",
        "short_X_train,short_X_test,short_y_train,short_y_test = train_test_split(svr_X,svr_y_short,test_size=0.2,random_state=40)\n",
        "long_X_train,long_X_test,long_y_train,long_y_test = train_test_split(svr_X,svr_y_long,test_size=0.2,random_state=40)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-EwBC98EKYS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "short_X = StandardScaler()\n",
        "short_y = StandardScaler()\n",
        "short_X_train = short_X.fit_transform(short_X_train)\n",
        "short_y_train = short_y.fit_transform(short_y_train)\n",
        "long_X = StandardScaler()\n",
        "long_y = StandardScaler()\n",
        "long_X_train = long_X.fit_transform(long_X_train)\n",
        "long_y_train = long_y.fit_transform(long_y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iy3qKuX7EMRK",
        "colab_type": "code",
        "outputId": "7c17c45a-ae09-49b9-f0e3-be2eb10f1c66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "from sklearn.svm import SVR\n",
        "short_regressor = SVR(kernel = 'rbf')\n",
        "long_regressor = SVR(kernel = 'rbf')\n",
        "short_regressor.fit(short_X_train, short_y_train)\n",
        "long_regressor.fit(long_X_train,long_y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
              "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aGfCxV85EO7D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "short_pred = short_regressor.predict(short_X_test)\n",
        "short_pred = short_y.inverse_transform(short_pred)\n",
        "long_pred = long_regressor.predict(long_X_test)\n",
        "long_pred = long_y.inverse_transform(long_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIKRdd79Nv5E",
        "colab_type": "code",
        "outputId": "2429353a-b56f-4a7a-8728-770ce0ea45cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "Get_score(short_pred, short_y_test)\n",
        "Get_score(long_pred, long_y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Spearman's correlation coefficient is: 0.338\n",
            "The Spearman's correlation coefficient is: 0.170\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "23UMX1VGN2ER",
        "colab_type": "text"
      },
      "source": [
        "***********************************************\n",
        "**Random Forest with C3D**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82Cd1djHN0lR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = c3dfeatures\n",
        "y = dataset[['short-term_memorability','long-term_memorability']].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgJ6Jv2cN6Fd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Splitting the dataset into the Training set and Test set\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_8NBIQ3gN8Ph",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf_regressor = RandomForestRegressor(n_estimators=20,random_state=42,verbose=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r5Yd_b9kOAZ0",
        "colab_type": "code",
        "outputId": "5458f949-7911-4201-810a-bfd0718ed54d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        }
      },
      "source": [
        "rf_regressor.fit(X_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "building tree 1 of 20\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.4s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "building tree 2 of 20\n",
            "building tree 3 of 20\n",
            "building tree 4 of 20\n",
            "building tree 5 of 20\n",
            "building tree 6 of 20\n",
            "building tree 7 of 20\n",
            "building tree 8 of 20\n",
            "building tree 9 of 20\n",
            "building tree 10 of 20\n",
            "building tree 11 of 20\n",
            "building tree 12 of 20\n",
            "building tree 13 of 20\n",
            "building tree 14 of 20\n",
            "building tree 15 of 20\n",
            "building tree 16 of 20\n",
            "building tree 17 of 20\n",
            "building tree 18 of 20\n",
            "building tree 19 of 20\n",
            "building tree 20 of 20\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    8.4s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=20, n_jobs=None, oob_score=False,\n",
              "                      random_state=42, verbose=2, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Go3wdfkNOCpj",
        "colab_type": "code",
        "outputId": "95c80f0a-6abc-4329-967d-5a2e6b59091b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        }
      },
      "source": [
        "rf_pred1 = rf_regressor.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hVKPHgSlOFEy",
        "colab_type": "code",
        "outputId": "42ed20d6-4aab-4e0d-d06e-eaa9f950aadb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "\n",
        "Get_score(rf_pred1, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Spearman's correlation coefficient is: 0.266\n",
            "The Spearman's correlation coefficient is: 0.122\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MIIwyv2vUVDm",
        "colab_type": "text"
      },
      "source": [
        "*****************************************\n",
        "**SVR with C3D**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lPLdWIvOGpq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "svr_X = c3dfeatures\n",
        "svr_y_short = dataset[['short-term_memorability']].values\n",
        "svr_y_long = dataset[['long-term_memorability']].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7HuebOMJUZGL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Splitting the dataset into the Training set and Test set\n",
        "short_X_train,short_X_test,short_y_train,short_y_test = train_test_split(svr_X,svr_y_short,test_size=0.2,random_state=40)\n",
        "long_X_train,long_X_test,long_y_train,long_y_test = train_test_split(svr_X,svr_y_long,test_size=0.2,random_state=40)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4H2aNKnUeD6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "short_X = StandardScaler()\n",
        "short_y = StandardScaler()\n",
        "short_X_train = short_X.fit_transform(short_X_train)\n",
        "short_y_train = short_y.fit_transform(short_y_train)\n",
        "long_X = StandardScaler()\n",
        "long_y = StandardScaler()\n",
        "long_X_train = long_X.fit_transform(long_X_train)\n",
        "long_y_train = long_y.fit_transform(long_y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LasuFKwCUsYB",
        "colab_type": "code",
        "outputId": "9cdcd699-37c8-41b4-920e-1d061436db07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "from sklearn.svm import SVR\n",
        "short_regressor = SVR(kernel = 'rbf')\n",
        "long_regressor = SVR(kernel = 'rbf')\n",
        "short_regressor.fit(short_X_train, short_y_train)\n",
        "long_regressor.fit(long_X_train,long_y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
              "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L6rZj0-GUuaV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "short_pred = short_regressor.predict(short_X_test)\n",
        "short_pred = short_y.inverse_transform(short_pred)\n",
        "long_pred = long_regressor.predict(long_X_test)\n",
        "long_pred = long_y.inverse_transform(long_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uMIz88_RUyIE",
        "colab_type": "code",
        "outputId": "7bc9ee0e-af5c-49ec-da70-142ec4a6999a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "Get_score(short_pred, short_y_test)\n",
        "Get_score(long_pred, long_y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Spearman's correlation coefficient is: 0.242\n",
            "The Spearman's correlation coefficient is: 0.107\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TEn-VgzIU2Fg",
        "colab_type": "text"
      },
      "source": [
        "**********************************************\n",
        "\n",
        "\n",
        "**Random Forest with Captions and C3D**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v7Q6OonCUzop",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = captions_c3d\n",
        "y = dataset[['short-term_memorability','long-term_memorability']].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GP8v6mc_U8Kk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Splitting the dataset into the Training set and Test set\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a3PP0blyU-IU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf_regressor = RandomForestRegressor(n_estimators=50,random_state=45,verbose=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wMDUeQnQU_ku",
        "colab_type": "code",
        "outputId": "f611a986-5a09-4ded-c414-82583d661a7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "rf_regressor.fit(X_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "building tree 1 of 50\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   20.7s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "building tree 2 of 50\n",
            "building tree 3 of 50\n",
            "building tree 4 of 50\n",
            "building tree 5 of 50\n",
            "building tree 6 of 50\n",
            "building tree 7 of 50\n",
            "building tree 8 of 50\n",
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            "building tree 11 of 50\n",
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            "building tree 47 of 50\n",
            "building tree 48 of 50\n",
            "building tree 49 of 50\n",
            "building tree 50 of 50\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed: 14.7min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=50, n_jobs=None, oob_score=False,\n",
              "                      random_state=45, verbose=2, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4cWui7K8VA7Y",
        "colab_type": "code",
        "outputId": "1491c7ae-1d18-44bb-e5a7-2312072e5e67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        }
      },
      "source": [
        "rf_pred2 = rf_regressor.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kFaHI368VExg",
        "colab_type": "code",
        "outputId": "b6195d82-122b-4744-f82f-a4e37432f9bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "Get_score(rf_pred2, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Spearman's correlation coefficient is: 0.300\n",
            "The Spearman's correlation coefficient is: 0.120\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Polgva2nVIwT",
        "colab_type": "text"
      },
      "source": [
        "*******************************************\n",
        "**SVR with Captions and C3D**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nf_thJk6VGYz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "svr_X = captions_c3d\n",
        "svr_y_short = dataset[['short-term_memorability']].values\n",
        "svr_y_long = dataset[['long-term_memorability']].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0S41Zuk3VLLv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Splitting the dataset into the Training set and Test set\n",
        "short_X_train,short_X_test,short_y_train,short_y_test = train_test_split(svr_X,svr_y_short,test_size=0.2,random_state=40)\n",
        "long_X_train,long_X_test,long_y_train,long_y_test = train_test_split(svr_X,svr_y_long,test_size=0.2,random_state=40)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VyrefpzpVNMH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "short_X = StandardScaler()\n",
        "short_y = StandardScaler()\n",
        "short_X_train = short_X.fit_transform(short_X_train)\n",
        "short_y_train = short_y.fit_transform(short_y_train)\n",
        "long_X = StandardScaler()\n",
        "long_y = StandardScaler()\n",
        "long_X_train = long_X.fit_transform(long_X_train)\n",
        "long_y_train = long_y.fit_transform(long_y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-RU0TXYVPbc",
        "colab_type": "code",
        "outputId": "ab18a828-af82-4543-c91e-a895a04b36fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "from sklearn.svm import SVR\n",
        "short_regressor = SVR(kernel = 'rbf')\n",
        "long_regressor = SVR(kernel = 'rbf')\n",
        "short_regressor.fit(short_X_train, short_y_train)\n",
        "long_regressor.fit(long_X_train,long_y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
              "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6N-UiSeFVQ7D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "short_pred = short_regressor.predict(short_X_test)\n",
        "short_pred = short_y.inverse_transform(short_pred)\n",
        "long_pred = long_regressor.predict(long_X_test)\n",
        "long_pred = long_y.inverse_transform(long_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qXEFQJ5VTGF",
        "colab_type": "code",
        "outputId": "1135a2e8-b6cf-45d0-ffd5-839bad7a0fcd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "Get_score(short_pred, short_y_test)\n",
        "Get_score(long_pred, long_y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Spearman's correlation coefficient is: 0.355\n",
            "The Spearman's correlation coefficient is: 0.179\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1jN0ixFQVXKc",
        "colab_type": "text"
      },
      "source": [
        "**********************************************\n",
        "**Random Forest with Captions, C3D and HMP**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bq3Ia6h2VVTK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "X = captions_c3d_hmp\n",
        "y = dataset[['short-term_memorability','long-term_memorability']].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JTyI7Ym8VYwl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Splitting the dataset into the Training set and Test set\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qs_kgCNlVaSI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf_regressor = RandomForestRegressor(n_estimators=100,random_state=45,verbose=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hYiwZQrSVbbk",
        "colab_type": "code",
        "outputId": "0279176d-95ae-4acd-f9e8-1878aa1002d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "rf_regressor.fit(X_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "building tree 1 of 100\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   20.7s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
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            "building tree 2 of 100\n",
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed: 29.6min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                      random_state=45, verbose=2, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KU8bArGvVdEu",
        "colab_type": "code",
        "outputId": "f1d0d2bd-bad3-4452-88c1-8863e14a888a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        }
      },
      "source": [
        "rf_pred3 = rf_regressor.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y9pATObMVffO",
        "colab_type": "code",
        "outputId": "22491f80-8481-4575-8cac-ba01590f9d91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "\n",
        "Get_score(rf_pred3, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Spearman's correlation coefficient is: 0.322\n",
            "The Spearman's correlation coefficient is: 0.145\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "twccZEdBVj8Q",
        "colab_type": "text"
      },
      "source": [
        "************************************\n",
        "**SVR with Captions, C3D and HMP**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Br-GuyefVhfn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "svr_X = captions_c3d_hmp\n",
        "svr_y_short = dataset[['short-term_memorability']].values\n",
        "svr_y_long = dataset[['long-term_memorability']].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gkKd6GfWVmNL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Splitting the dataset into the Training set and Test set\n",
        "short_X_train,short_X_test,short_y_train,short_y_test = train_test_split(svr_X,svr_y_short,test_size=0.2,random_state=40)\n",
        "long_X_train,long_X_test,long_y_train,long_y_test = train_test_split(svr_X,svr_y_long,test_size=0.2,random_state=40)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x4KlzxUIVo-r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "short_X = StandardScaler()\n",
        "short_y = StandardScaler()\n",
        "short_X_train = short_X.fit_transform(short_X_train)\n",
        "short_y_train = short_y.fit_transform(short_y_train)\n",
        "long_X = StandardScaler()\n",
        "long_y = StandardScaler()\n",
        "long_X_train = long_X.fit_transform(long_X_train)\n",
        "long_y_train = long_y.fit_transform(long_y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZ2jCALsVq95",
        "colab_type": "code",
        "outputId": "6879b027-a85e-4cdc-9f16-24d129ad53ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "from sklearn.svm import SVR\n",
        "short_regressor = SVR(kernel = 'rbf')\n",
        "long_regressor = SVR(kernel = 'rbf')\n",
        "short_regressor.fit(short_X_train, short_y_train)\n",
        "long_regressor.fit(long_X_train,long_y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
              "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mm4on1yGVumS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "short_pred = short_regressor.predict(short_X_test)\n",
        "short_pred = short_y.inverse_transform(short_pred)\n",
        "long_pred = long_regressor.predict(long_X_test)\n",
        "long_pred = long_y.inverse_transform(long_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sNZyaF92Vw66",
        "colab_type": "code",
        "outputId": "98e0f836-d92f-4bdf-dbd9-4feaf36caf5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "Get_score(short_pred, short_y_test)\n",
        "Get_score(long_pred, long_y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Spearman's correlation coefficient is: 0.355\n",
            "The Spearman's correlation coefficient is: 0.179\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6NxNGI1gV1HL",
        "colab_type": "text"
      },
      "source": [
        "******************************\n",
        "******************************\n",
        "#SELECTING THE BEST MODEL WITH FEATURES#\n",
        "******************************\n",
        "******************************"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iFzpbC9PDbyz",
        "colab_type": "text"
      },
      "source": [
        "RANDOM FOREST WITH CAPTION HAS GIVEN US THE BEST RESULTS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcUD8GcAV5-y",
        "colab_type": "code",
        "outputId": "629ed150-c4aa-4145-de4d-eefff644829b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "Get_score(captions_pred, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Spearman's correlation coefficient is: 0.409\n",
            "The Spearman's correlation coefficient is: 0.176\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QQ3jP4iAV_Tq",
        "colab_type": "text"
      },
      "source": [
        "******************************\n",
        "******************************\n",
        "#PREDICTING THE TEST RESULTS#\n",
        "******************************\n",
        "******************************"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CG_NyKvwDvXp",
        "colab_type": "text"
      },
      "source": [
        "NOW WE WILL BE USING THE ENTIRE 6000 DEVELOPMENT DATASET TO TRAIN OUR MODEL AND APPLY THE RESULTS ON THE 2000 DEVELOPMENT SET FOR RANDOM FOREST MODEL WITH CAPTIONS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLBPlIDHV8o-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = captions_bag\n",
        "y = dataset[['short-term_memorability','long-term_memorability']].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B3CO_eTrWBTm",
        "colab_type": "code",
        "outputId": "41555462-5e2f-4c2d-b0b1-ade7976d02c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(f'X: ({len(X)})')\n",
        "print(f'y:{y.shape}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X: (6000)\n",
            "y:(6000, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VK4DSiv-WJVH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf_regressor = RandomForestRegressor(n_estimators=100,random_state=45)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zg1gfrOUWLVQ",
        "colab_type": "code",
        "outputId": "6d7630d8-e704-4a1d-9772-aff1e6ba611b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        }
      },
      "source": [
        "rf_regressor.fit(X,y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                      random_state=45, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xp06NCpdEHXn",
        "colab_type": "text"
      },
      "source": [
        "IMPORTING TEST DATASET AND CAPTIONS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tTDYPzaKWM-9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#importing test Dataset\n",
        "csv_path ='Test-set/Ground-truth_test/ground_truth_template.csv'\n",
        "test_dataset = pd.read_csv(csv_path)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NoKcI3EoyTgA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V2c7hCa0XHtf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "#load the test set captions\n",
        "test_captions_path ='Test-set/Captions_test/test-set-1_video-captions.txt'\n",
        "test_captions = load_captions(test_captions_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e_06CM_4XJ3W",
        "colab_type": "code",
        "outputId": "1303346c-2671-4fed-b95b-fb5468856d96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "test_dataset.head()"
      ],
      "execution_count": 0,
      "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>video</th>\n",
              "      <th>short-term_memorability</th>\n",
              "      <th>nb_short-term_annotations</th>\n",
              "      <th>long-term_memorability</th>\n",
              "      <th>nb_long-term_annotations</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7494</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7495</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7496</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7497</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7498</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   video  ...  nb_long-term_annotations\n",
              "0   7494  ...                        12\n",
              "1   7495  ...                        10\n",
              "2   7496  ...                        13\n",
              "3   7497  ...                        10\n",
              "4   7498  ...                        10\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p573VZnyEUKX",
        "colab_type": "text"
      },
      "source": [
        "WE NEED TO FILL THE VALUES OF OUR PREDICTION HERE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xihxr3sdXMm9",
        "colab_type": "code",
        "outputId": "b332373b-fee1-4186-9515-76d1252ff85c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "test_captions.head()"
      ],
      "execution_count": 0,
      "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>video</th>\n",
              "      <th>caption</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>video7494.webm</td>\n",
              "      <td>green-jeep-struggling-to-drive-over-huge-rocks</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>video7495.webm</td>\n",
              "      <td>hiking-woman-tourist-is-walking-forward-in-mou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>video7496.webm</td>\n",
              "      <td>close-up-of-african-american-doctors-hands-usi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>video7497.webm</td>\n",
              "      <td>slow-motion-of-a-man-using-treadmill-in-the-gy...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>video7498.webm</td>\n",
              "      <td>slow-motion-of-photographer-in-national-park</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            video                                            caption\n",
              "0  video7494.webm     green-jeep-struggling-to-drive-over-huge-rocks\n",
              "1  video7495.webm  hiking-woman-tourist-is-walking-forward-in-mou...\n",
              "2  video7496.webm  close-up-of-african-american-doctors-hands-usi...\n",
              "3  video7497.webm  slow-motion-of-a-man-using-treadmill-in-the-gy...\n",
              "4  video7498.webm       slow-motion-of-photographer-in-national-park"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aV3IdyLhXOjb",
        "colab_type": "code",
        "outputId": "7e4ce022-1dfa-4d52-935d-c465196740d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "#printing the dimensions of test-set dataset and features\n",
        "print(f'Test-Dataset : {test_dataset.shape}')\n",
        "print(f'Test-Captions: {test_captions.shape}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test-Dataset : (2000, 5)\n",
            "Test-Captions: (2000, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7mRF3vwXmKv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i, cap in enumerate(test_captions['caption']):\n",
        "    # replace punctuations with space\n",
        "    # convert words to lower case \n",
        "    text = ''.join([c if c not in punctuation else ' ' for c in cap]).lower()\n",
        "    #removing stopwords\n",
        "    rmv_stopwords= ' '.join([word for word in text.split() if word not in stopwords])\n",
        "    test_captions.loc[i,'caption'] = rmv_stopwords #updating the original captions \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0lkPX58Xp-A",
        "colab_type": "code",
        "outputId": "744e77ea-fd68-4cdf-f7cc-102364ca0e48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        }
      },
      "source": [
        "\n",
        "test_captions.head(1410)"
      ],
      "execution_count": 0,
      "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>video</th>\n",
              "      <th>caption</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>video7494.webm</td>\n",
              "      <td>green jeep struggling drive huge rocks</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>video7495.webm</td>\n",
              "      <td>hiking woman tourist walking forward mountains...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>video7496.webm</td>\n",
              "      <td>close african american doctors hands using sph...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>video7497.webm</td>\n",
              "      <td>slow motion man using treadmill gym regular ph...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>video7498.webm</td>\n",
              "      <td>slow motion photographer national park</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1405</th>\n",
              "      <td>video9283.webm</td>\n",
              "      <td>man performing martial arts use nunchaku</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1406</th>\n",
              "      <td>video9284.webm</td>\n",
              "      <td>back close upof man dark room playing racing g...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1407</th>\n",
              "      <td>video9288.webm</td>\n",
              "      <td>point view little girl lying grass laughing gr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1408</th>\n",
              "      <td>video9289.webm</td>\n",
              "      <td>cute little girl sitting kitchen table pouring...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1409</th>\n",
              "      <td>video9290.webm</td>\n",
              "      <td>dealer shuffling splitting deck</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1410 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               video                                            caption\n",
              "0     video7494.webm             green jeep struggling drive huge rocks\n",
              "1     video7495.webm  hiking woman tourist walking forward mountains...\n",
              "2     video7496.webm  close african american doctors hands using sph...\n",
              "3     video7497.webm  slow motion man using treadmill gym regular ph...\n",
              "4     video7498.webm             slow motion photographer national park\n",
              "...              ...                                                ...\n",
              "1405  video9283.webm           man performing martial arts use nunchaku\n",
              "1406  video9284.webm  back close upof man dark room playing racing g...\n",
              "1407  video9288.webm  point view little girl lying grass laughing gr...\n",
              "1408  video9289.webm  cute little girl sitting kitchen table pouring...\n",
              "1409  video9290.webm                    dealer shuffling splitting deck\n",
              "\n",
              "[1410 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "otUUwiFlXsNV",
        "colab_type": "code",
        "outputId": "c3db8091-b024-4304-fea3-c6dfba256591",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "vectorizer = CountVectorizer(analyzer = \"word\",max_features=3122) \n",
        "test_captions_bag = vectorizer.fit_transform(test_captions.caption).toarray()\n",
        "type(test_captions_bag)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTQEcCvtXuZ-",
        "colab_type": "code",
        "outputId": "ac70517b-1b48-4510-ac77-31f5fe121ad9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "#print(f'Testing Bag of words size     : {len(test_captions_bag)}')\n",
        "#print(f'Development Bag of words size : {len(captions_bag)}')\n",
        "print(f'Development Vocabulary Size   : {len(captions_bag[0])}')\n",
        "print(f'Testing Vocabulary Size       : {len(test_captions_bag[0])}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Development Vocabulary Size   : 3112\n",
            "Testing Vocabulary Size       : 3112\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJctCkuw8OH5",
        "colab_type": "text"
      },
      "source": [
        "**PREDICTING AND EXPORTING RESULTS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZtjpIY9XxeJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_pred = rf_regressor.predict(test_captions_bag)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0gWYDnJ8RTN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred = pd.DataFrame()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWBEDm1r8Uhn",
        "colab_type": "code",
        "outputId": "a9898288-f883-4691-d8f4-919f2ede75db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "type(test_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ADjAD8g8Vwb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred['short-term'] = test_pred[:,0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-3QYQMG8Xfx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred['long-term'] = test_pred[:,1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4EQWOnN28Y_e",
        "colab_type": "code",
        "outputId": "2884310b-5804-40f4-9d06-d8a8e7308545",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        }
      },
      "source": [
        "\n",
        "pred.sort_values(by='short-term', ascending=False)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>short-term</th>\n",
              "      <th>long-term</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>926</th>\n",
              "      <td>0.953749</td>\n",
              "      <td>0.892380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1400</th>\n",
              "      <td>0.936548</td>\n",
              "      <td>0.949478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1299</th>\n",
              "      <td>0.934161</td>\n",
              "      <td>0.788914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1754</th>\n",
              "      <td>0.929084</td>\n",
              "      <td>0.917522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>939</th>\n",
              "      <td>0.928873</td>\n",
              "      <td>0.882550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>481</th>\n",
              "      <td>0.731869</td>\n",
              "      <td>0.774900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>820</th>\n",
              "      <td>0.727445</td>\n",
              "      <td>0.685896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1835</th>\n",
              "      <td>0.709939</td>\n",
              "      <td>0.762564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1438</th>\n",
              "      <td>0.690135</td>\n",
              "      <td>0.571422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>512</th>\n",
              "      <td>0.687093</td>\n",
              "      <td>0.605395</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2000 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      short-term  long-term\n",
              "926     0.953749   0.892380\n",
              "1400    0.936548   0.949478\n",
              "1299    0.934161   0.788914\n",
              "1754    0.929084   0.917522\n",
              "939     0.928873   0.882550\n",
              "...          ...        ...\n",
              "481     0.731869   0.774900\n",
              "820     0.727445   0.685896\n",
              "1835    0.709939   0.762564\n",
              "1438    0.690135   0.571422\n",
              "512     0.687093   0.605395\n",
              "\n",
              "[2000 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0phSKgwjes_3",
        "colab_type": "code",
        "outputId": "4df5fc5c-62b1-472e-b7ac-fb00e006a436",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        }
      },
      "source": [
        "pred.head(10)"
      ],
      "execution_count": 0,
      "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>short-term</th>\n",
              "      <th>long-term</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.854635</td>\n",
              "      <td>0.712699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.898883</td>\n",
              "      <td>0.781500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.841901</td>\n",
              "      <td>0.805625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.915282</td>\n",
              "      <td>0.826990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.866134</td>\n",
              "      <td>0.711727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.829725</td>\n",
              "      <td>0.740506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.829797</td>\n",
              "      <td>0.719513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.849537</td>\n",
              "      <td>0.729653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.778917</td>\n",
              "      <td>0.790459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.866541</td>\n",
              "      <td>0.675180</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   short-term  long-term\n",
              "0    0.854635   0.712699\n",
              "1    0.898883   0.781500\n",
              "2    0.841901   0.805625\n",
              "3    0.915282   0.826990\n",
              "4    0.866134   0.711727\n",
              "5    0.829725   0.740506\n",
              "6    0.829797   0.719513\n",
              "7    0.849537   0.729653\n",
              "8    0.778917   0.790459\n",
              "9    0.866541   0.675180"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "acVu3ATB8agU",
        "colab_type": "code",
        "outputId": "dee9fb27-d7d2-43e6-f731-11d786bcc683",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        }
      },
      "source": [
        "pred.describe()"
      ],
      "execution_count": 0,
      "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>short-term</th>\n",
              "      <th>long-term</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.848028</td>\n",
              "      <td>0.751163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.034564</td>\n",
              "      <td>0.067863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.687093</td>\n",
              "      <td>0.397318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.830368</td>\n",
              "      <td>0.721224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.849698</td>\n",
              "      <td>0.757218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.871004</td>\n",
              "      <td>0.792872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.953749</td>\n",
              "      <td>0.949478</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        short-term    long-term\n",
              "count  2000.000000  2000.000000\n",
              "mean      0.848028     0.751163\n",
              "std       0.034564     0.067863\n",
              "min       0.687093     0.397318\n",
              "25%       0.830368     0.721224\n",
              "50%       0.849698     0.757218\n",
              "75%       0.871004     0.792872\n",
              "max       0.953749     0.949478"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HAOysSxlEnIW",
        "colab_type": "text"
      },
      "source": [
        "EXPORTING CSV"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mw3d8b8a8cox",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "from google.colab import drive\n",
        "import os\n",
        "drive.mount('/content/gdrive/',force_remount=True)\n",
        "pred.to_csv('content/gdrive/My Drive/output2.csv')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b19zp9TaKWaZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!ls"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hbv4OrVr8fXH",
        "colab_type": "code",
        "outputId": "1799a24f-a364-45ce-9ae5-6c9172a52a29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        }
      },
      "source": [
        "pred.describe()"
      ],
      "execution_count": 0,
      "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>short-term</th>\n",
              "      <th>long-term</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.848028</td>\n",
              "      <td>0.751163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.034564</td>\n",
              "      <td>0.067863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.687093</td>\n",
              "      <td>0.397318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.830368</td>\n",
              "      <td>0.721224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.849698</td>\n",
              "      <td>0.757218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.871004</td>\n",
              "      <td>0.792872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.953749</td>\n",
              "      <td>0.949478</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        short-term    long-term\n",
              "count  2000.000000  2000.000000\n",
              "mean      0.848028     0.751163\n",
              "std       0.034564     0.067863\n",
              "min       0.687093     0.397318\n",
              "25%       0.830368     0.721224\n",
              "50%       0.849698     0.757218\n",
              "75%       0.871004     0.792872\n",
              "max       0.953749     0.949478"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jsedhiP71RDu",
        "colab_type": "text"
      },
      "source": [
        "*******************************\n",
        "**ENSEMBLE MODEL**\n",
        "*******************************"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "typJ8lUyE5j0",
        "colab_type": "text"
      },
      "source": [
        "RANDOM FOREST WITH C3D AND RANDOM FOREST WITH CAPTIONS WE OBTAIN A BETTER RESULT"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mT5EO0gp4sQI",
        "colab_type": "code",
        "outputId": "4ce786ac-3b9f-4d83-905e-c1a4bc0b424b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "finalpred2 = (0.5*captions_pred + 0.2*rf_pred1 + 0.2*rf_pred2 + 0.1*rf_pred3)\n",
        "Get_score(finalpred2, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Spearman's correlation coefficient is: 0.435\n",
            "The Spearman's correlation coefficient is: 0.201\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q-dWTk9uQU_r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}