eye_tracking.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/maxmelichov/eye-movmentprediction/blob/main/eye_tracking.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z7BTRYrTkOcq"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.impute import KNNImputer\n",
        "import matplotlib.pyplot as plt\n",
        "from pylab import *\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from tensorflow.python.keras.layers import Dense, LSTM, RepeatVector, Flatten,Dropout\n",
        "from keras.layers import TimeDistributed\n",
        "from sklearn.metrics import mean_squared_error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MnHKKVjISGQD",
        "outputId": "c9b23ecd-aabe-436f-bfcc-6e3cf9639314"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (72) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        }
      ],
      "source": [
        "df = pd.read_csv(\"data.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FxdHA1v0SH2u"
      },
      "outputs": [],
      "source": [
        "need_col = ['Participant name','Presented Stimulus name','Recording start time','Recording resolution height','Recording resolution width','Gaze point left X (DACSmm)','Gaze point left Y (DACSmm)','Gaze point right X (DACSmm)','Gaze point right Y (DACSmm)']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LjgwqLbsM0fM"
      },
      "source": [
        "Creating a new df only the needed columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T_WcTviySJcN"
      },
      "outputs": [],
      "source": [
        "new_df = pd.DataFrame()\n",
        "for col in need_col:\n",
        "    new_df = pd.concat([new_df,df[col]],axis=1)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3EaEy5sMM70F"
      },
      "source": [
        "Taking the relevent number of the participant"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IsRh0eiHegUr"
      },
      "outputs": [],
      "source": [
        "new_df = new_df.loc[(new_df['Presented Stimulus name']=='Slide4')| (new_df['Presented Stimulus name']=='Slide5')]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L9jNqoKRehvy",
        "outputId": "f1adaeec-94a3-49a9-9d3a-c45e7f57db31"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Participant name                 0\n",
              "Presented Stimulus name          0\n",
              "Recording start time             0\n",
              "Recording resolution height      0\n",
              "Recording resolution width       0\n",
              "Gaze point left X (DACSmm)     726\n",
              "Gaze point left Y (DACSmm)     726\n",
              "Gaze point right X (DACSmm)    604\n",
              "Gaze point right Y (DACSmm)    604\n",
              "dtype: int64"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_d7F0EUMejzA"
      },
      "outputs": [],
      "source": [
        "new_df.dropna(axis =0 , inplace = True)\n",
        "new_df.reset_index(inplace=True,drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hvpq2TQNekwj",
        "outputId": "e6bb9274-5fec-4c87-beff-023b2917e2de"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Participant name               0\n",
              "Presented Stimulus name        0\n",
              "Recording start time           0\n",
              "Recording resolution height    0\n",
              "Recording resolution width     0\n",
              "Gaze point left X (DACSmm)     0\n",
              "Gaze point left Y (DACSmm)     0\n",
              "Gaze point right X (DACSmm)    0\n",
              "Gaze point right Y (DACSmm)    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G4cWIWeberzh",
        "outputId": "1474cc7a-918d-4a77-f95f-8ee0b5903890"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        }
      ],
      "source": [
        "for col in new_df.columns[:2]:\n",
        "    for i in range(len(new_df)):\n",
        "        new_df[col][i] = int(new_df[col][i][-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "Awh9QvDEgTMg",
        "outputId": "a92c5ccd-ee79-4bcc-9b80-e696d2af2f89"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5c652aa4-7fcd-4ab3-9aec-d84d0f8adb7a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Participant name</th>\n",
              "      <th>Presented Stimulus name</th>\n",
              "      <th>Recording start time</th>\n",
              "      <th>Recording resolution height</th>\n",
              "      <th>Recording resolution width</th>\n",
              "      <th>Gaze point left X (DACSmm)</th>\n",
              "      <th>Gaze point left Y (DACSmm)</th>\n",
              "      <th>Gaze point right X (DACSmm)</th>\n",
              "      <th>Gaze point right Y (DACSmm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>15:48:26.718</td>\n",
              "      <td>1080</td>\n",
              "      <td>1920</td>\n",
              "      <td>222.5</td>\n",
              "      <td>122.9</td>\n",
              "      <td>238.9</td>\n",
              "      <td>131.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>15:48:26.718</td>\n",
              "      <td>1080</td>\n",
              "      <td>1920</td>\n",
              "      <td>220.6</td>\n",
              "      <td>119.0</td>\n",
              "      <td>240.7</td>\n",
              "      <td>131.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>15:48:26.718</td>\n",
              "      <td>1080</td>\n",
              "      <td>1920</td>\n",
              "      <td>222.8</td>\n",
              "      <td>120.5</td>\n",
              "      <td>235.9</td>\n",
              "      <td>128.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>15:48:26.718</td>\n",
              "      <td>1080</td>\n",
              "      <td>1920</td>\n",
              "      <td>237.4</td>\n",
              "      <td>122.7</td>\n",
              "      <td>238.7</td>\n",
              "      <td>135.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>15:48:26.718</td>\n",
              "      <td>1080</td>\n",
              "      <td>1920</td>\n",
              "      <td>236.7</td>\n",
              "      <td>126.4</td>\n",
              "      <td>251.9</td>\n",
              "      <td>135.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2218</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>15:53:16.560</td>\n",
              "      <td>1080</td>\n",
              "      <td>1920</td>\n",
              "      <td>471.9</td>\n",
              "      <td>215.2</td>\n",
              "      <td>458.4</td>\n",
              "      <td>215.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2219</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>15:53:16.560</td>\n",
              "      <td>1080</td>\n",
              "      <td>1920</td>\n",
              "      <td>471.1</td>\n",
              "      <td>213.8</td>\n",
              "      <td>458.1</td>\n",
              "      <td>215.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2220</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>15:53:16.560</td>\n",
              "      <td>1080</td>\n",
              "      <td>1920</td>\n",
              "      <td>464.4</td>\n",
              "      <td>212.4</td>\n",
              "      <td>457.6</td>\n",
              "      <td>218.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2221</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>15:53:16.560</td>\n",
              "      <td>1080</td>\n",
              "      <td>1920</td>\n",
              "      <td>468.1</td>\n",
              "      <td>213.4</td>\n",
              "      <td>455.9</td>\n",
              "      <td>224.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2222</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>15:53:16.560</td>\n",
              "      <td>1080</td>\n",
              "      <td>1920</td>\n",
              "      <td>470.3</td>\n",
              "      <td>215.1</td>\n",
              "      <td>458.7</td>\n",
              "      <td>217.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2223 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5c652aa4-7fcd-4ab3-9aec-d84d0f8adb7a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5c652aa4-7fcd-4ab3-9aec-d84d0f8adb7a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5c652aa4-7fcd-4ab3-9aec-d84d0f8adb7a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      Participant name  Presented Stimulus name Recording start time  \\\n",
              "0                    1                        4         15:48:26.718   \n",
              "1                    1                        4         15:48:26.718   \n",
              "2                    1                        4         15:48:26.718   \n",
              "3                    1                        4         15:48:26.718   \n",
              "4                    1                        4         15:48:26.718   \n",
              "...                ...                      ...                  ...   \n",
              "2218                 2                        5         15:53:16.560   \n",
              "2219                 2                        5         15:53:16.560   \n",
              "2220                 2                        5         15:53:16.560   \n",
              "2221                 2                        5         15:53:16.560   \n",
              "2222                 2                        5         15:53:16.560   \n",
              "\n",
              "      Recording resolution height  Recording resolution width  \\\n",
              "0                            1080                        1920   \n",
              "1                            1080                        1920   \n",
              "2                            1080                        1920   \n",
              "3                            1080                        1920   \n",
              "4                            1080                        1920   \n",
              "...                           ...                         ...   \n",
              "2218                         1080                        1920   \n",
              "2219                         1080                        1920   \n",
              "2220                         1080                        1920   \n",
              "2221                         1080                        1920   \n",
              "2222                         1080                        1920   \n",
              "\n",
              "      Gaze point left X (DACSmm)  Gaze point left Y (DACSmm)  \\\n",
              "0                          222.5                       122.9   \n",
              "1                          220.6                       119.0   \n",
              "2                          222.8                       120.5   \n",
              "3                          237.4                       122.7   \n",
              "4                          236.7                       126.4   \n",
              "...                          ...                         ...   \n",
              "2218                       471.9                       215.2   \n",
              "2219                       471.1                       213.8   \n",
              "2220                       464.4                       212.4   \n",
              "2221                       468.1                       213.4   \n",
              "2222                       470.3                       215.1   \n",
              "\n",
              "      Gaze point right X (DACSmm)  Gaze point right Y (DACSmm)  \n",
              "0                           238.9                        131.1  \n",
              "1                           240.7                        131.1  \n",
              "2                           235.9                        128.7  \n",
              "3                           238.7                        135.2  \n",
              "4                           251.9                        135.3  \n",
              "...                           ...                          ...  \n",
              "2218                        458.4                        215.6  \n",
              "2219                        458.1                        215.3  \n",
              "2220                        457.6                        218.1  \n",
              "2221                        455.9                        224.2  \n",
              "2222                        458.7                        217.5  \n",
              "\n",
              "[2223 rows x 9 columns]"
            ]
          },
          "execution_count": 116,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uthbp6WhNL0z"
      },
      "source": [
        "converting it to int"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vpit-WQ6SOW0"
      },
      "outputs": [],
      "source": [
        "new_df['Participant name']=new_df['Participant name'].astype(\"int16\")\n",
        "new_df['Presented Stimulus name']=new_df['Presented Stimulus name'].astype(\"int16\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-2oQhOUJNOKa"
      },
      "source": [
        "Droping non int or float columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0Z1jG6-4SuWc"
      },
      "outputs": [],
      "source": [
        "new_df.to_csv(\"DF.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O-_dDcnrSvcC"
      },
      "outputs": [],
      "source": [
        "df= pd.read_csv(\"DF.csv\",parse_dates=['Recording start time'], index_col='Recording start time')\n",
        "\n",
        "df.drop('Unnamed: 0',axis = 1,inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EIJ2vrdvNeVU"
      },
      "source": [
        "**We had two different experiments**\n",
        "\n",
        "Time line 1: Participant 1,4,5 \n",
        "\n",
        "Time line 2: Participant 2,3,6,7"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dE58ZroHtdl3"
      },
      "outputs": [],
      "source": [
        "Timeline1 = df.loc[(df['Participant name']==1) | (df['Participant name'] ==4) | (df['Participant name'] ==5)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QhR7sNCdv1ZF"
      },
      "outputs": [],
      "source": [
        "Timeline1 =Timeline1.iloc[:,4:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P7FfC6fGte9C"
      },
      "outputs": [],
      "source": [
        "Timeline1=np.array(Timeline1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Timeline1[40].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KlGQDOdYagBR",
        "outputId": "ba6b2cf0-3939-4cef-ba66-5887d50f5e9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4,)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jnnn3m-btf26"
      },
      "outputs": [],
      "source": [
        "def datacreation(Timeline):\n",
        "  window  = 30   #time series length\n",
        "  skip    = 1\n",
        "  next    = 10    #predict point in next steps\n",
        "\n",
        "  x_data = []\n",
        "  y_data = [] \n",
        "\n",
        "  for i in range( 0, len(Timeline) - window - next, skip ):\n",
        "    x_data.append( Timeline[i:i+window] )\n",
        "    y_data.append( Timeline[i+window+next] )\n",
        "\n",
        "  x_data_np = np.array(x_data)\n",
        "  y_data_np = np.array(y_data)\n",
        "\n",
        "  print(\"x_data_np\", x_data_np.shape,\"y_data_np\", y_data_np.shape)\n",
        "  return x_data_np,y_data_np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UMPbIZ2Ltgti"
      },
      "outputs": [],
      "source": [
        "def dataengineering(x_data_np,y_data_np):\n",
        "  #normalize\n",
        "  scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "  x_data0 = scaler.fit_transform(x_data_np[:,:,0])\n",
        "  x_data1 = scaler.fit_transform(x_data_np[:,:,1])\n",
        "\n",
        "  y_data0 = scaler.fit_transform(y_data_np[:,0].reshape(-1,1))\n",
        "  y_data1 = scaler.fit_transform(y_data_np[:,1].reshape(-1,1))\n",
        "\n",
        "  x_data2 = scaler.fit_transform(x_data_np[:,:,2])\n",
        "  x_data3 = scaler.fit_transform(x_data_np[:,:,3])\n",
        "\n",
        "  y_data2 = scaler.fit_transform(y_data_np[:,2].reshape(-1,1))\n",
        "  y_data3 = scaler.fit_transform(y_data_np[:,3].reshape(-1,1))\n",
        "  # x_data0 = x_data_np[:,:,0] *1./1920\n",
        "  # x_data1 = x_data_np[:,:,1] *1./1080\n",
        "  # y_data0 = y_data_np[:,0] *1./1920\n",
        "  # y_data1 = y_data_np[:,1] *1./1080\n",
        "\n",
        "  # x_data2 = x_data_np[:,:,2] *1./1920\n",
        "  # x_data3 = x_data_np[:,:,3] *1./1080\n",
        "\n",
        "  # y_data2 = y_data_np[:,2] *1./1920\n",
        "  # y_data3 = y_data_np[:,3] *1./1080\t\n",
        "\n",
        "\n",
        "  #exapnad dim\n",
        "  x_data0 = x_data0.reshape((x_data0.shape[0],x_data0.shape[1],1))\n",
        "  x_data1 = x_data1.reshape((x_data1.shape[0],x_data1.shape[1],1))\n",
        "\n",
        "  y_data0 = y_data0.reshape((y_data0.shape[0],1))\n",
        "  y_data1 = y_data1.reshape((y_data1.shape[0],1))\n",
        "\n",
        "  x_data2 = x_data0.reshape((x_data2.shape[0],x_data2.shape[1],1))\n",
        "  x_data3 = x_data1.reshape((x_data3.shape[0],x_data3.shape[1],1))\n",
        "  \n",
        "  y_data2 = y_data2.reshape((y_data2.shape[0],1))\n",
        "  y_data3 = y_data3.reshape((y_data3.shape[0],1))\n",
        "\n",
        "\n",
        "  \n",
        "\n",
        "\n",
        "  #final array normalized\n",
        "  x_data = np.concatenate((x_data0,x_data1,x_data2,x_data3),axis=2)\n",
        "  y_data = np.concatenate((y_data0,y_data1,y_data2,y_data3),axis=1)\n",
        "\n",
        "\n",
        "  #Check correctness\n",
        "  print(\"y_data\", y_data.shape,\"x_data\", x_data.shape)\n",
        "\n",
        "  print(np.min(x_data[:,:,0]))\n",
        "  print(np.min(x_data[:,:,1]))\n",
        "  print(np.max(x_data[:,:,2]))\n",
        "  print(np.max(x_data[:,:,3]))\n",
        "\n",
        "  print(np.min(y_data[:,0]))\n",
        "  print(np.min(y_data[:,1]))\n",
        "  print(np.max(y_data[:,2]))\n",
        "  print(np.max(y_data[:,3]))\n",
        "  return x_data,y_data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2gzF3NbWth9P"
      },
      "outputs": [],
      "source": [
        "def test_train_split(x_data,y_data):\n",
        "  #Train on the start of sequence ; test on the end of sequence\n",
        "  index = int(x_data.shape[0]*0.8)\n",
        "\n",
        "  X_train = x_data[:index,:,:]\n",
        "  X_val = x_data[index:,:,:]\n",
        "\n",
        "  Y_train = y_data[:index,:]\n",
        "  Y_val = y_data[index:,:] \n",
        "\n",
        "  print('Train set shape', X_train.shape)\n",
        "  print('Validation set shape', X_val.shape)\n",
        "  print(\"y_train set shape\",Y_train.shape)\n",
        "  print('Validation label shape', Y_val.shape)\n",
        "  return X_train,X_val,Y_train,Y_val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1o7E29E6tzQ7"
      },
      "outputs": [],
      "source": [
        "def get_model(X_train,Y_train):\n",
        "\n",
        "#   model_lstm= keras.Sequential()\n",
        "\n",
        "#   model_lstm.add(keras.layers.LSTM(units=32,input_shape=(X_train.shape[1],X_train.shape[2])))\n",
        "#   model_lstm.add(keras.layers.Dropout(rate=0.5))\n",
        "\n",
        "#   model_lstm.add(keras.layers.RepeatVector(n=X_train.shape[2]))\n",
        "#   model_lstm.add(keras.layers.LSTM(units=32,return_sequences=True))\n",
        "\n",
        "#   model_lstm.add(keras.layers.Dropout(rate=0.5))\n",
        "\n",
        "#   model_lstm.add(keras.layers.TimeDistributed(keras.layers.Dense(units=Y_train.shape[1])))\n",
        "#   model_lstm.add(keras.layers.Flatten())\n",
        "#   model_lstm.add(Dense(32))\n",
        "#   model_lstm.add(Dense(Y_train.shape[1],activation='linear'))\n",
        "#   model_lstm.compile(loss='mae',optimizer ='adam')\n",
        "  model_lstm = Sequential()\n",
        "  model_lstm.add(LSTM(32, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2]))) #layer 1\n",
        "  model_lstm.add(LSTM(32, return_sequences=True)) # layer 2\n",
        "  model_lstm.add(LSTM(16))\n",
        "  model_lstm.add(Dense(64)) \n",
        "  model_lstm.add(Dense(Y_train.shape[1],activation='linear'))\n",
        "  model_lstm.compile(loss='mse', optimizer='adam')\n",
        "  return model_lstm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "WoP12EEKt0Uy",
        "outputId": "093bd4f7-4b59-473b-c269-b03b8ecbee41"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x_data_np (2146, 30, 4) y_data_np (2146, 4)\n",
            "y_data (2146, 4) x_data (2146, 30, 4)\n",
            "0.0\n",
            "0.0\n",
            "1.0\n",
            "1.0\n",
            "0.0\n",
            "0.0\n",
            "0.9999999999999999\n",
            "1.0\n",
            "Train set shape (1716, 30, 4)\n",
            "Validation set shape (430, 30, 4)\n",
            "y_train set shape (1716, 4)\n",
            "Validation label shape (430, 4)\n",
            "Epoch 1/50\n",
            "108/108 - 12s - loss: 0.0375 - val_loss: 0.0322 - 12s/epoch - 112ms/step\n",
            "Epoch 2/50\n",
            "108/108 - 5s - loss: 0.0102 - val_loss: 0.0322 - 5s/epoch - 45ms/step\n",
            "Epoch 3/50\n",
            "108/108 - 5s - loss: 0.0092 - val_loss: 0.0311 - 5s/epoch - 46ms/step\n",
            "Epoch 4/50\n",
            "108/108 - 5s - loss: 0.0088 - val_loss: 0.0307 - 5s/epoch - 45ms/step\n",
            "Epoch 5/50\n",
            "108/108 - 5s - loss: 0.0084 - val_loss: 0.0288 - 5s/epoch - 46ms/step\n",
            "Epoch 6/50\n",
            "108/108 - 5s - loss: 0.0079 - val_loss: 0.0286 - 5s/epoch - 44ms/step\n",
            "Epoch 7/50\n",
            "108/108 - 5s - loss: 0.0078 - val_loss: 0.0283 - 5s/epoch - 44ms/step\n",
            "Epoch 8/50\n",
            "108/108 - 5s - loss: 0.0076 - val_loss: 0.0274 - 5s/epoch - 44ms/step\n",
            "Epoch 9/50\n",
            "108/108 - 5s - loss: 0.0077 - val_loss: 0.0267 - 5s/epoch - 45ms/step\n",
            "Epoch 10/50\n",
            "108/108 - 5s - loss: 0.0074 - val_loss: 0.0269 - 5s/epoch - 44ms/step\n",
            "Epoch 11/50\n",
            "108/108 - 5s - loss: 0.0075 - val_loss: 0.0273 - 5s/epoch - 44ms/step\n",
            "Epoch 12/50\n",
            "108/108 - 5s - loss: 0.0072 - val_loss: 0.0278 - 5s/epoch - 45ms/step\n",
            "Epoch 13/50\n",
            "108/108 - 5s - loss: 0.0071 - val_loss: 0.0268 - 5s/epoch - 44ms/step\n",
            "Epoch 14/50\n",
            "108/108 - 5s - loss: 0.0072 - val_loss: 0.0306 - 5s/epoch - 44ms/step\n"
          ]
        }
      ],
      "source": [
        "x_data_np,y_data_np= datacreation(Timeline1)\n",
        "x_data,y_data = dataengineering(x_data_np,y_data_np)\n",
        "X_train,X_val,Y_train,Y_val = test_train_split(x_data,y_data) \n",
        "model_lstm = get_model(X_train,Y_train)\n",
        "lstm_history = model_lstm.fit(X_train, Y_train, validation_data=(X_val, Y_val),\n",
        "epochs=50, verbose=2,batch_size = 16,\n",
        "callbacks = [keras.callbacks.EarlyStopping(monitor = 'val_loss', patience = 5, restore_best_weights = True)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "kqgofd8at1Yn",
        "outputId": "dcdc03da-dcfa-422a-dd6a-6078ae3412c5"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot(lstm_history.history['loss'], label='Train loss')\n",
        "plot(lstm_history.history['val_loss'], label='Validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "x840HEcPt2zR",
        "outputId": "4309e150-ac82-48e5-992c-a518fd9a0c56"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE: 0.026681\n"
          ]
        }
      ],
      "source": [
        "loss = model_lstm.evaluate(X_val, Y_val, verbose=0)\n",
        "print('MAE: %f' % loss)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "1B_oMrPkt3vO",
        "outputId": "6c3ce2ce-9ccb-4062-d5f1-2394f518ad47"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#X left  axis value prediction\n",
        "yhat = model_lstm.predict(X_val, verbose=0)\n",
        "plt.plot(Y_val[:,0], label='y0')\n",
        "plt.plot(yhat[:,0], label='y0hat')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "#Y left axis value prediction\n",
        "yhat = model_lstm.predict(X_val, verbose=0)\n",
        "plt.plot(Y_val[:,1], label='y1')\n",
        "plt.plot(yhat[:,1], label='y1hat')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "#X right axis value prediction\n",
        "yhat = model_lstm.predict(X_val, verbose=0)\n",
        "plt.plot(Y_val[:,2], label='X right')\n",
        "plt.plot(yhat[:,2], label='X right pred')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "#Y right axis value prediction\n",
        "yhat = model_lstm.predict(X_val, verbose=0)\n",
        "plt.plot(Y_val[:,3], label='Y right')\n",
        "plt.plot(yhat[:,3], label='Y right pred')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Mpujs5jht5L2"
      },
      "outputs": [],
      "source": [
        "Timeline2 = df.loc[(df['Participant name']==2) | (df['Participant name'] ==3) | (df['Participant name'] ==6)| (df['Participant name'] ==7)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "SwJkQ0wIxZ10"
      },
      "outputs": [],
      "source": [
        "Timeline2 =Timeline2.iloc[:,4:]\n",
        "Timeline2=np.array(Timeline2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "oEUG_VIB3MyU",
        "outputId": "74f239c7-a7ae-4b7c-ad13-002c1c9edec7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(5266, 4)"
            ]
          },
          "execution_count": 142,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Timeline2.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dE-jetUd0snd",
        "outputId": "8d60f4c2-14db-4e0d-c840-47819f537f9b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x_data_np (5226, 30, 4) y_data_np (5226, 4)\n",
            "y_data (5226, 4) x_data (5226, 30, 4)\n",
            "0.0\n",
            "0.0\n",
            "1.0000000000000002\n",
            "1.0000000000000002\n",
            "0.0\n",
            "0.0\n",
            "0.9999999999999999\n",
            "1.0\n",
            "Train set shape (4180, 30, 4)\n",
            "Validation set shape (1046, 30, 4)\n",
            "y_train set shape (4180, 4)\n",
            "Validation label shape (1046, 4)\n",
            "Epoch 1/50\n",
            "66/66 - 8s - loss: 0.0442 - val_loss: 0.0049 - 8s/epoch - 120ms/step\n",
            "Epoch 2/50\n",
            "66/66 - 3s - loss: 0.0115 - val_loss: 0.0042 - 3s/epoch - 38ms/step\n",
            "Epoch 3/50\n",
            "66/66 - 2s - loss: 0.0108 - val_loss: 0.0038 - 2s/epoch - 37ms/step\n",
            "Epoch 4/50\n",
            "66/66 - 3s - loss: 0.0102 - val_loss: 0.0035 - 3s/epoch - 38ms/step\n",
            "Epoch 5/50\n",
            "66/66 - 3s - loss: 0.0098 - val_loss: 0.0033 - 3s/epoch - 39ms/step\n",
            "Epoch 6/50\n",
            "66/66 - 2s - loss: 0.0097 - val_loss: 0.0046 - 2s/epoch - 37ms/step\n",
            "Epoch 7/50\n",
            "66/66 - 2s - loss: 0.0095 - val_loss: 0.0042 - 2s/epoch - 37ms/step\n",
            "Epoch 8/50\n",
            "66/66 - 2s - loss: 0.0094 - val_loss: 0.0047 - 2s/epoch - 37ms/step\n",
            "Epoch 9/50\n",
            "66/66 - 3s - loss: 0.0092 - val_loss: 0.0035 - 3s/epoch - 39ms/step\n",
            "Epoch 10/50\n",
            "66/66 - 2s - loss: 0.0090 - val_loss: 0.0045 - 2s/epoch - 37ms/step\n"
          ]
        }
      ],
      "source": [
        "x_data_np,y_data_np= datacreation(Timeline2)\n",
        "x_data,y_data = dataengineering(x_data_np,y_data_np)\n",
        "X_train,X_val,Y_train,Y_val = test_train_split(x_data,y_data) \n",
        "model_lstm = get_model(X_train,Y_train)\n",
        "lstm_history = model_lstm.fit(X_train, Y_train, validation_data=(X_val, Y_val),\n",
        "epochs=50, verbose=2,batch_size = 32,\n",
        "callbacks = [keras.callbacks.EarlyStopping(monitor = 'val_loss', patience =5, restore_best_weights = True)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QXr3w-n7uCXL",
        "outputId": "bee83830-313c-4fe2-ad09-a5362f843894"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE: 0.003315\n"
          ]
        }
      ],
      "source": [
        "loss = model_lstm.evaluate(X_val, Y_val, verbose=0)\n",
        "print('MAE: %f' % loss)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UE371WEuuEFX",
        "outputId": "f470e630-658a-41b0-b309-e64c9e8c6eaa"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#X left  axis value prediction\n",
        "yhat = model_lstm.predict(X_val, verbose=0)\n",
        "plt.plot(Y_val[:,0], label='y0')\n",
        "plt.plot(yhat[:,0], label='y0hat')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "#Y left axis value prediction\n",
        "yhat = model_lstm.predict(X_val, verbose=0)\n",
        "plt.plot(Y_val[:,1], label='y1')\n",
        "plt.plot(yhat[:,1], label='y1hat')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "#X right axis value prediction\n",
        "yhat = model_lstm.predict(X_val, verbose=0)\n",
        "plt.plot(Y_val[:,2], label='X right')\n",
        "plt.plot(yhat[:,2], label='X right pred')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "#Y right axis value prediction\n",
        "yhat = model_lstm.predict(X_val, verbose=0)\n",
        "plt.plot(Y_val[:,3], label='Y right')\n",
        "plt.plot(yhat[:,3], label='Y right pred')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F5XCuWus028f"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": [],
      "authorship_tag": "ABX9TyOEJX6xOGSTrRBVonU89eFA",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}