README.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Tennis stroke classification with ML"
      ],
      "metadata": {
        "id": "FPb1XgYZqrLj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This project details how to classify a tennis player stroke with Machine Learning with data gathered from an Apple Watch.\n",
        "\n",
        "The application required to gather this data is [TennisIO](https://github.com/ivangonzalezz/TennisIO). It needs the following REST API to export data from the device: [TennisIOAPI](https://github.com/ivangonzalezz/TennisIOAPI).\n",
        "\n",
        "It's recommended to run this notebook at [Google Colab](https://colab.research.google.com), where it's built."
      ],
      "metadata": {
        "id": "NO2OHRpIq69n"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "mOJ8jC0xbz12",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title\n",
        "# Import dependencies\n",
        "import pandas as pd\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import math\n",
        "import seaborn as sns\n",
        "import matplotlib.ticker as plticker\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading data"
      ],
      "metadata": {
        "id": "m5gyNnT3ssGl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data is loaded from JSON files. Each of them is a list of sensor data from the device at a given timestamp."
      ],
      "metadata": {
        "id": "jg-56bO_swgs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check files to load\n",
        "folder = 'trainments'\n",
        "files = os.listdir(folder)\n",
        "\n",
        "# Load data from .json files\n",
        "frames = []\n",
        "for f in files:\n",
        "    if '.json' in f:\n",
        "        d = pd.read_json(f'{folder}/{f}')\n",
        "        frames.append(d)\n",
        "        print(f'Loaded {f}')\n",
        "\n",
        "data = pd.concat(frames, ignore_index=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tuaY1r2RcNcP",
        "outputId": "b1e8e660-0bc8-477e-e298-cac1d6212f09"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded 20230624_113411.json\n",
            "Loaded 20230625_124903.json\n",
            "Loaded 20230625_125655.json\n",
            "Loaded 20230625_011052.json\n",
            "Loaded 20230625_011216.json\n",
            "Loaded 20230625_011333.json\n",
            "Loaded 20230625_013444.json\n",
            "Loaded 20230625_013330.json\n",
            "Loaded 20230625_013541.json\n",
            "Loaded 20230625_013636.json\n",
            "Loaded 20230625_095026.json\n",
            "Loaded 20230625_094910.json\n",
            "Loaded 20230625_094808.json\n",
            "Loaded 20230625_094747.json\n",
            "Loaded 20230625_094833.json\n",
            "Loaded 20230625_094944.json\n",
            "Loaded 20230625_094722.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare data"
      ],
      "metadata": {
        "id": "hAb3mrxQt1rh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Once loaded, we need to solve some tricky aspects present in data:\n",
        "\n",
        "* The timestamp gathered from the device has a precision of a second while it generates almost 50 samples for each second. We should expand the precision adding made up milliseconds to each sample.\n",
        "\n",
        "* Each file contains one kind of movement repeated in a period of time. We need to window the data for each movement done.\n",
        "\n",
        "* In order to train the model, we should convert all movements in summaries for each period. This will let us use more simple algorithms.\n",
        "\n",
        "* Finally, we'll split the data into train and test datasets in order to validate the model created with data not used to create it."
      ],
      "metadata": {
        "id": "fOevXhPHt7ME"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Timestamp precision"
      ],
      "metadata": {
        "id": "u6zRsOsRwnMc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, let's solve the timestamp precision issue. We'll need to know how many samples exist for each second in order to split the second in this number of samples."
      ],
      "metadata": {
        "id": "WFmLhUmHwvOL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Group samples by second\n",
        "grouped_per_second = data.groupby([\"identifier\", \"kind\", \"timestamp\"], as_index=False)['identifier'].count()\n",
        "grouped_per_second['first_index'] = grouped_per_second.apply(lambda row: data[data['timestamp'] == row['timestamp']].index[0], axis=1)\n",
        "\n",
        "grouped_per_second.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "ZPn3IowlqA9t",
        "outputId": "21fd8246-934b-4b7e-eb54-0465ba2f1ec8"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    kind           timestamp  identifier  first_index\n",
              "0  Drive 2023-06-24 11:34:11          12            0\n",
              "1  Drive 2023-06-24 11:34:12          50           12\n",
              "2  Drive 2023-06-24 11:34:13          50           62\n",
              "3  Drive 2023-06-24 11:34:14          50          112\n",
              "4  Drive 2023-06-24 11:34:15          51          162"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ccbb6a4f-c975-43eb-acf7-ebdc9488bc1e\">\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>kind</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>identifier</th>\n",
              "      <th>first_index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Drive</td>\n",
              "      <td>2023-06-24 11:34:11</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Drive</td>\n",
              "      <td>2023-06-24 11:34:12</td>\n",
              "      <td>50</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Drive</td>\n",
              "      <td>2023-06-24 11:34:13</td>\n",
              "      <td>50</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Drive</td>\n",
              "      <td>2023-06-24 11:34:14</td>\n",
              "      <td>50</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Drive</td>\n",
              "      <td>2023-06-24 11:34:15</td>\n",
              "      <td>51</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ccbb6a4f-c975-43eb-acf7-ebdc9488bc1e')\"\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-ccbb6a4f-c975-43eb-acf7-ebdc9488bc1e 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-ccbb6a4f-c975-43eb-acf7-ebdc9488bc1e');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Give timestamp millisecond precision and add an attribute to know the time since the trainment start\n",
        "data['timestamp_millis'] = data['timestamp'].dt.round('L')\n",
        "data['timestamp_millis'] = data.apply(\n",
        "    lambda row: row['timestamp_millis'] +\n",
        "        pd.to_timedelta(\n",
        "            np.linspace(50, 950, num=grouped_per_second[grouped_per_second['timestamp'] == row['timestamp']].iloc[0]['identifier'])\n",
        "            [row.name-grouped_per_second[grouped_per_second['timestamp'] == row['timestamp']].iloc[0]['first_index']]\n",
        "            .astype(int),\n",
        "            unit='ms'),\n",
        "    axis=1)\n",
        "\n",
        "data['time_since_start'] = data['timestamp_millis'] - data['identifier'].astype('datetime64[ns]')"
      ],
      "metadata": {
        "id": "L_nDNmjblAkM"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "eu2tytttgjsY",
        "outputId": "d9709199-3012-4fe5-94c4-b8bf38a8e0aa"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            identifier   kind     pitch      roll       yaw  \\\n",
              "0  24-06-2023 11:34:11  Drive -0.303339 -0.714798 -0.114031   \n",
              "1  24-06-2023 11:34:11  Drive -0.303299 -0.713937 -0.113978   \n",
              "2  24-06-2023 11:34:11  Drive -0.302059 -0.712966 -0.112469   \n",
              "3  24-06-2023 11:34:11  Drive -0.301500 -0.711424 -0.110062   \n",
              "4  24-06-2023 11:34:11  Drive -0.301774 -0.708930 -0.107489   \n",
              "\n",
              "            timestamp  xAcceleration  yAcceleration  zAcceleration  xRotation  \\\n",
              "0 2023-06-24 11:34:11      -0.018703      -0.023211      -0.023211  -0.155816   \n",
              "1 2023-06-24 11:34:11       0.021121      -0.032054      -0.032054   0.090451   \n",
              "2 2023-06-24 11:34:11      -0.011183      -0.004198      -0.004198   0.070849   \n",
              "3 2023-06-24 11:34:11      -0.039289       0.045942       0.045942   0.131693   \n",
              "4 2023-06-24 11:34:11      -0.051615       0.040752       0.040752   0.001550   \n",
              "\n",
              "   yRotation  zRotation        timestamp_millis       time_since_start  \n",
              "0   0.095353   0.022249 2023-06-24 11:34:11.050 0 days 00:00:00.050000  \n",
              "1   0.003979  -0.022444 2023-06-24 11:34:11.131 0 days 00:00:00.131000  \n",
              "2   0.060976   0.057538 2023-06-24 11:34:11.213 0 days 00:00:00.213000  \n",
              "3   0.058357   0.093916 2023-06-24 11:34:11.295 0 days 00:00:00.295000  \n",
              "4   0.138468   0.102855 2023-06-24 11:34:11.377 0 days 00:00:00.377000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bc6211f6-1883-4733-9b4f-d931332ee12b\">\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>identifier</th>\n",
              "      <th>kind</th>\n",
              "      <th>pitch</th>\n",
              "      <th>roll</th>\n",
              "      <th>yaw</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>xAcceleration</th>\n",
              "      <th>yAcceleration</th>\n",
              "      <th>zAcceleration</th>\n",
              "      <th>xRotation</th>\n",
              "      <th>yRotation</th>\n",
              "      <th>zRotation</th>\n",
              "      <th>timestamp_millis</th>\n",
              "      <th>time_since_start</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24-06-2023 11:34:11</td>\n",
              "      <td>Drive</td>\n",
              "      <td>-0.303339</td>\n",
              "      <td>-0.714798</td>\n",
              "      <td>-0.114031</td>\n",
              "      <td>2023-06-24 11:34:11</td>\n",
              "      <td>-0.018703</td>\n",
              "      <td>-0.023211</td>\n",
              "      <td>-0.023211</td>\n",
              "      <td>-0.155816</td>\n",
              "      <td>0.095353</td>\n",
              "      <td>0.022249</td>\n",
              "      <td>2023-06-24 11:34:11.050</td>\n",
              "      <td>0 days 00:00:00.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>24-06-2023 11:34:11</td>\n",
              "      <td>Drive</td>\n",
              "      <td>-0.303299</td>\n",
              "      <td>-0.713937</td>\n",
              "      <td>-0.113978</td>\n",
              "      <td>2023-06-24 11:34:11</td>\n",
              "      <td>0.021121</td>\n",
              "      <td>-0.032054</td>\n",
              "      <td>-0.032054</td>\n",
              "      <td>0.090451</td>\n",
              "      <td>0.003979</td>\n",
              "      <td>-0.022444</td>\n",
              "      <td>2023-06-24 11:34:11.131</td>\n",
              "      <td>0 days 00:00:00.131000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>24-06-2023 11:34:11</td>\n",
              "      <td>Drive</td>\n",
              "      <td>-0.302059</td>\n",
              "      <td>-0.712966</td>\n",
              "      <td>-0.112469</td>\n",
              "      <td>2023-06-24 11:34:11</td>\n",
              "      <td>-0.011183</td>\n",
              "      <td>-0.004198</td>\n",
              "      <td>-0.004198</td>\n",
              "      <td>0.070849</td>\n",
              "      <td>0.060976</td>\n",
              "      <td>0.057538</td>\n",
              "      <td>2023-06-24 11:34:11.213</td>\n",
              "      <td>0 days 00:00:00.213000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>24-06-2023 11:34:11</td>\n",
              "      <td>Drive</td>\n",
              "      <td>-0.301500</td>\n",
              "      <td>-0.711424</td>\n",
              "      <td>-0.110062</td>\n",
              "      <td>2023-06-24 11:34:11</td>\n",
              "      <td>-0.039289</td>\n",
              "      <td>0.045942</td>\n",
              "      <td>0.045942</td>\n",
              "      <td>0.131693</td>\n",
              "      <td>0.058357</td>\n",
              "      <td>0.093916</td>\n",
              "      <td>2023-06-24 11:34:11.295</td>\n",
              "      <td>0 days 00:00:00.295000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24-06-2023 11:34:11</td>\n",
              "      <td>Drive</td>\n",
              "      <td>-0.301774</td>\n",
              "      <td>-0.708930</td>\n",
              "      <td>-0.107489</td>\n",
              "      <td>2023-06-24 11:34:11</td>\n",
              "      <td>-0.051615</td>\n",
              "      <td>0.040752</td>\n",
              "      <td>0.040752</td>\n",
              "      <td>0.001550</td>\n",
              "      <td>0.138468</td>\n",
              "      <td>0.102855</td>\n",
              "      <td>2023-06-24 11:34:11.377</td>\n",
              "      <td>0 days 00:00:00.377000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc6211f6-1883-4733-9b4f-d931332ee12b')\"\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-bc6211f6-1883-4733-9b4f-d931332ee12b 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-bc6211f6-1883-4733-9b4f-d931332ee12b');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Window data"
      ],
      "metadata": {
        "id": "e2O3H492x3_k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We're going to window the data manually. First, let's define a filter for values of xAcceleration near 0. This will help us to identify each movement."
      ],
      "metadata": {
        "id": "OLTUUplnyE3U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set global attributes\n",
        "data['x_accel_smoothed'] = data.apply(lambda row: 0 if row['xAcceleration'] < 0.1 and row['xAcceleration'] > -0.1 else row['xAcceleration'], axis=1)"
      ],
      "metadata": {
        "id": "0pH_T6FWvYJz"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then, we plot this attribute and we can see a pattern for each movement."
      ],
      "metadata": {
        "id": "ZY8Az_10bp3S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loc = plticker.MultipleLocator(base=1000000000)\n",
        "sns.set(rc={'figure.figsize':(20,10)})\n",
        "\n",
        "_tmp = data[data['identifier'] == '25-06-2023 09:50:26']\n",
        "\n",
        "axes = sns.lineplot(y='x_accel_smoothed', x='time_since_start', data=_tmp)\n",
        "axes.xaxis.set_major_locator(loc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "Fr07Q3M5XRn_",
        "outputId": "3e7a4ba5-0997-4575-8884-54634212ab25"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We define the start and the end of each movement in a file called windows.xlsx, that will be used to split the data."
      ],
      "metadata": {
        "id": "s5orGQOMb5yE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "windows = pd.read_excel('{}/windows.xlsx'.format(folder))\n",
        "windows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "UtTsqGiby_8Q",
        "outputId": "72b7553f-f223-4780-c496-58c7b2ca9f6a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Identifier  From    To      Kind\n",
              "0   2023-06-24 11:34:11  0.60  0.90     Drive\n",
              "1   2023-06-24 11:34:11  0.90  1.20     Drive\n",
              "2   2023-06-24 11:34:11  1.20  1.85     Drive\n",
              "3   2023-06-24 11:34:11  1.85  2.15     Drive\n",
              "4   2023-06-24 11:34:11  2.15  2.45     Drive\n",
              "..                  ...   ...   ...       ...\n",
              "188 2023-06-25 09:50:26  1.05  1.30  Backhand\n",
              "189 2023-06-25 09:50:26  1.30  1.50  Backhand\n",
              "190 2023-06-25 09:50:26  1.50  1.80  Backhand\n",
              "191 2023-06-25 09:50:26  1.80  2.05  Backhand\n",
              "192 2023-06-25 09:50:26  2.05  2.30  Backhand\n",
              "\n",
              "[193 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a0b5a3d7-46f1-488a-bce6-44f6d886b7ea\">\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>Identifier</th>\n",
              "      <th>From</th>\n",
              "      <th>To</th>\n",
              "      <th>Kind</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-06-24 11:34:11</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.90</td>\n",
              "      <td>Drive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-06-24 11:34:11</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.20</td>\n",
              "      <td>Drive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-06-24 11:34:11</td>\n",
              "      <td>1.20</td>\n",
              "      <td>1.85</td>\n",
              "      <td>Drive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-06-24 11:34:11</td>\n",
              "      <td>1.85</td>\n",
              "      <td>2.15</td>\n",
              "      <td>Drive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-06-24 11:34:11</td>\n",
              "      <td>2.15</td>\n",
              "      <td>2.45</td>\n",
              "      <td>Drive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>2023-06-25 09:50:26</td>\n",
              "      <td>1.05</td>\n",
              "      <td>1.30</td>\n",
              "      <td>Backhand</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>2023-06-25 09:50:26</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.50</td>\n",
              "      <td>Backhand</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>2023-06-25 09:50:26</td>\n",
              "      <td>1.50</td>\n",
              "      <td>1.80</td>\n",
              "      <td>Backhand</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>2023-06-25 09:50:26</td>\n",
              "      <td>1.80</td>\n",
              "      <td>2.05</td>\n",
              "      <td>Backhand</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>2023-06-25 09:50:26</td>\n",
              "      <td>2.05</td>\n",
              "      <td>2.30</td>\n",
              "      <td>Backhand</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>193 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0b5a3d7-46f1-488a-bce6-44f6d886b7ea')\"\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-a0b5a3d7-46f1-488a-bce6-44f6d886b7ea 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-a0b5a3d7-46f1-488a-bce6-44f6d886b7ea');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "splitted_data = []\n",
        "for index, row in windows.iterrows():\n",
        "    d = data[\n",
        "        (data['identifier'] == row['Identifier'].strftime('%d-%m-%Y %H:%M:%S')) &\n",
        "        (data['time_since_start'] >= pd.to_timedelta(row['From']*10, unit='S')) &\n",
        "        (data['time_since_start'] < pd.to_timedelta(row['To']*10, unit='S'))\n",
        "        ]\n",
        "    splitted_data.append(d)\n",
        "\n",
        "print(f'Total movements: {len(splitted_data)}')"
      ],
      "metadata": {
        "id": "uY29XYgv7_ya",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69b5799d-3eb4-42c7-b868-2857eb9749c7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total movements: 193\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Sum up features"
      ],
      "metadata": {
        "id": "A4aDVu82lmNH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have a really low number of samples to train a model. That's why we should build some features to avoid using a Neural Network model for this time series data.\n",
        "\n",
        "We're going to use the mean of each attribute for each movement."
      ],
      "metadata": {
        "id": "HRbFwv0elrG_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_columns = ['xAcceleration', 'yAcceleration', 'zAcceleration', 'pitch', 'yaw', 'roll', 'xRotation', 'yRotation', 'zRotation']\n",
        "columns = numeric_columns + ['kind']\n",
        "\n",
        "grouped_movement_data = pd.DataFrame([], columns=columns)\n",
        "\n",
        "for index, movement in enumerate(splitted_data):\n",
        "    d = []\n",
        "    for column in numeric_columns:\n",
        "        d.append(movement[column].mean())\n",
        "    d.append(movement['kind'].iloc[0])\n",
        "    grouped_movement_data = pd.concat([grouped_movement_data, pd.DataFrame([d], columns=columns)], ignore_index=True)"
      ],
      "metadata": {
        "id": "6977mpBDd-Sa"
      },
      "execution_count": 11,
      "outputs": []
    },
	{
      "cell_type": "code",
      "source": [
        "grouped_movement_data.groupby('kind')['kind'].hist()"
      ],
      "metadata": {
        "id": "UEXkOAuymQp8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Split data into train and test sets"
      ],
      "metadata": {
        "id": "trfaOn2DmUhG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We're using the method **train_test_split** from **sklearn.model_selection** to automatically select the train and test sets from the whole data. It shuffles the samples and select a given percentage for testing."
      ],
      "metadata": {
        "id": "A459cOwOmcbd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(grouped_movement_data[numeric_columns], grouped_movement_data['kind'], test_size=0.2)\n",
        "y_train.hist()\n",
        "y_test.hist()"
      ],
      "metadata": {
        "id": "R8q47aDhXSws",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "outputId": "62987a0b-5ebe-4e88-f34c-862edbf4936e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Build the model"
      ],
      "metadata": {
        "id": "TGDtb9_em_w6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We're going to check the accuracy given from different algorithms: kNN, SVC and Decision Tree."
      ],
      "metadata": {
        "id": "Z78cWlbJnhgj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# kNN model\n",
        "nbrs = KNeighborsClassifier(n_neighbors=2).fit(X_train, y_train)\n",
        "y_pred = nbrs.predict(X_test)\n",
        "\n",
        "print(f'kNN gives an accuracy of {accuracy_score(y_test, y_pred)*100}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zodiuUZantgx",
        "outputId": "2f197d97-5b8e-4f35-ebb2-ddd18d1f1370"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "kNN gives an accuracy of 100.0%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SVC model\n",
        "svc = SVC()\n",
        "svc.fit(X_train, y_train)\n",
        "\n",
        "y_pred = svc.predict(X_test)\n",
        "print(f'SVC gives an accuracy of {accuracy_score(y_test, y_pred)*100}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SY_5t-S2_jzN",
        "outputId": "dc1ef1c8-fb38-406a-bfdb-1f1417fe820a"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC gives an accuracy of 100.0%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Decision Tree model\n",
        "tree = DecisionTreeClassifier()\n",
        "tree = tree.fit(X_train, y_train)\n",
        "\n",
        "y_pred = tree.predict(X_test)\n",
        "y_probab = tree.predict_proba(X_test)\n",
        "\n",
        "accuracy_score(y_test, y_pred)\n",
        "print(f'Decision Tree gives an accuracy of {accuracy_score(y_test, y_pred)*100}%')\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred, labels=tree.classes_)\n",
        "cmd = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=tree.classes_)\n",
        "fig, ax = plt.subplots(figsize=(5,5))\n",
        "cmd.plot(ax=ax)\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "O-5esmidBt1Z",
        "outputId": "e1a16d4a-0dbe-456e-a6cf-7d381d2ef506"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree gives an accuracy of 100.0%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusion"
      ],
      "metadata": {
        "id": "-kTNlwJPoZ60"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "All three models result in an accuracy near the 100%. Features selected to build these models are classifying correctly each tennis stroke."
      ],
      "metadata": {
        "id": "rku2Ncc5ogUg"
      }
    }
  ]
}