FitSpeed_HeartRate_Model_23062023.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6qfPHY5nvu5I"
      },
      "source": [
        "# Load Dependency\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ZSxp-_c2dYF",
        "outputId": "2f4ef4b9-dff1-4e6e-e4d5-1e5e31e24469",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sat Apr 22 17:31:57 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   56C    P8    10W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gtmC_Fm59jOq",
        "outputId": "34e530ed-9028-4bad-ede8-667e85ed00ff",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting scikit-tensor-py3\n",
            "  Downloading scikit_tensor_py3-0.4.1-py3-none-any.whl (48 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.4/48.4 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting numpy==1.16.*\n",
            "  Downloading numpy-1.16.6.zip (5.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.1/5.1 MB\u001b[0m \u001b[31m55.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting scipy==1.3.*\n",
            "  Downloading scipy-1.3.3.tar.gz (23.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.8/23.8 MB\u001b[0m \u001b[31m27.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: numpy, scipy\n",
            "  Building wheel for numpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for numpy: filename=numpy-1.16.6-cp39-cp39-linux_x86_64.whl size=9574957 sha256=5be3d5bdfcbbfae5dbd46d9cd88eda5b5dbc7f8303e7b5472ca108c0c5a113c4\n",
            "  Stored in directory: /root/.cache/pip/wheels/ce/72/38/377a1055ac9b575ebba22e507856aecdc37f9bf6372703e5f9\n",
            "  Building wheel for scipy (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for scipy: filename=scipy-1.3.3-cp39-cp39-linux_x86_64.whl size=54851684 sha256=cb932111d6a771194a2c557bca01fb3003a4084c52797ad981c3e555798aab05\n",
            "  Stored in directory: /root/.cache/pip/wheels/46/57/a5/a862889f1d5614f5a5f3baefb306c35414aed6e2d6e56f7d9d\n",
            "Successfully built numpy scipy\n",
            "Installing collected packages: scipy, numpy, scikit-tensor-py3\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.10.1\n",
            "    Uninstalling scipy-1.10.1:\n",
            "      Successfully uninstalled scipy-1.10.1\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.22.4\n",
            "    Uninstalling numpy-1.22.4:\n",
            "      Successfully uninstalled numpy-1.22.4\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "xarray 2022.12.0 requires numpy>=1.20, but you have numpy 1.16.6 which is incompatible.\n",
            "xarray-einstats 0.5.1 requires numpy>=1.20, but you have numpy 1.16.6 which is incompatible.\n",
            "xarray-einstats 0.5.1 requires scipy>=1.6, but you have scipy 1.3.3 which is incompatible.\n",
            "tensorflow 2.12.0 requires numpy<1.24,>=1.22, but you have numpy 1.16.6 which is incompatible.\n",
            "tables 3.8.0 requires numpy>=1.19.0, but you have numpy 1.16.6 which is incompatible.\n",
            "statsmodels 0.13.5 requires numpy>=1.17; python_version != \"3.10\" or platform_system != \"Windows\" or platform_python_implementation == \"PyPy\", but you have numpy 1.16.6 which is incompatible.\n",
            "sklearn-pandas 2.2.0 requires numpy>=1.18.1, but you have numpy 1.16.6 which is incompatible.\n",
            "sklearn-pandas 2.2.0 requires scipy>=1.5.1, but you have scipy 1.3.3 which is incompatible.\n",
            "seaborn 0.12.2 requires numpy!=1.24.0,>=1.17, but you have numpy 1.16.6 which is incompatible.\n",
            "scikit-learn 1.2.2 requires numpy>=1.17.3, but you have numpy 1.16.6 which is incompatible.\n",
            "scikit-image 0.19.3 requires numpy>=1.17.0, but you have numpy 1.16.6 which is incompatible.\n",
            "scikit-image 0.19.3 requires scipy>=1.4.1, but you have scipy 1.3.3 which is incompatible.\n",
            "pywavelets 1.4.1 requires numpy>=1.17.3, but you have numpy 1.16.6 which is incompatible.\n",
            "pytensor 2.10.1 requires numpy>=1.17.0, but you have numpy 1.16.6 which is incompatible.\n",
            "pymc 5.1.2 requires scipy>=1.4.1, but you have scipy 1.3.3 which is incompatible.\n",
            "pyerfa 2.0.0.3 requires numpy>=1.17, but you have numpy 1.16.6 which is incompatible.\n",
            "plotnine 0.10.1 requires numpy>=1.19.0, but you have numpy 1.16.6 which is incompatible.\n",
            "plotnine 0.10.1 requires scipy>=1.5.0, but you have scipy 1.3.3 which is incompatible.\n",
            "pandas 1.5.3 requires numpy>=1.20.3; python_version < \"3.10\", but you have numpy 1.16.6 which is incompatible.\n",
            "optax 0.1.4 requires numpy>=1.18.0, but you have numpy 1.16.6 which is incompatible.\n",
            "opencv-python 4.7.0.72 requires numpy>=1.17.0; python_version >= \"3.7\", but you have numpy 1.16.6 which is incompatible.\n",
            "opencv-python 4.7.0.72 requires numpy>=1.17.3; python_version >= \"3.8\", but you have numpy 1.16.6 which is incompatible.\n",
            "opencv-python 4.7.0.72 requires numpy>=1.19.3; python_version >= \"3.9\", but you have numpy 1.16.6 which is incompatible.\n",
            "opencv-python-headless 4.7.0.72 requires numpy>=1.17.0; python_version >= \"3.7\", but you have numpy 1.16.6 which is incompatible.\n",
            "opencv-python-headless 4.7.0.72 requires numpy>=1.17.3; python_version >= \"3.8\", but you have numpy 1.16.6 which is incompatible.\n",
            "opencv-python-headless 4.7.0.72 requires numpy>=1.19.3; python_version >= \"3.9\", but you have numpy 1.16.6 which is incompatible.\n",
            "opencv-contrib-python 4.7.0.72 requires numpy>=1.17.0; python_version >= \"3.7\", but you have numpy 1.16.6 which is incompatible.\n",
            "opencv-contrib-python 4.7.0.72 requires numpy>=1.17.3; python_version >= \"3.8\", but you have numpy 1.16.6 which is incompatible.\n",
            "opencv-contrib-python 4.7.0.72 requires numpy>=1.19.3; python_version >= \"3.9\", but you have numpy 1.16.6 which is incompatible.\n",
            "numba 0.56.4 requires numpy<1.24,>=1.18, but you have numpy 1.16.6 which is incompatible.\n",
            "moviepy 1.0.3 requires numpy>=1.17.3; python_version != \"2.7\", but you have numpy 1.16.6 which is incompatible.\n",
            "ml-dtypes 0.1.0 requires numpy>1.20, but you have numpy 1.16.6 which is incompatible.\n",
            "mizani 0.8.1 requires numpy>=1.19.0, but you have numpy 1.16.6 which is incompatible.\n",
            "mizani 0.8.1 requires scipy>=1.5.0, but you have scipy 1.3.3 which is incompatible.\n",
            "matplotlib 3.7.1 requires numpy>=1.20, but you have numpy 1.16.6 which is incompatible.\n",
            "librosa 0.10.0.post2 requires numpy!=1.22.0,!=1.22.1,!=1.22.2,>=1.20.3, but you have numpy 1.16.6 which is incompatible.\n",
            "jaxlib 0.4.7+cuda11.cudnn86 requires numpy>=1.21, but you have numpy 1.16.6 which is incompatible.\n",
            "jaxlib 0.4.7+cuda11.cudnn86 requires scipy>=1.7, but you have scipy 1.3.3 which is incompatible.\n",
            "jax 0.4.8 requires numpy>=1.21, but you have numpy 1.16.6 which is incompatible.\n",
            "jax 0.4.8 requires scipy>=1.7, but you have scipy 1.3.3 which is incompatible.\n",
            "imbalanced-learn 0.10.1 requires numpy>=1.17.3, but you have numpy 1.16.6 which is incompatible.\n",
            "gym 0.25.2 requires numpy>=1.18.0, but you have numpy 1.16.6 which is incompatible.\n",
            "gensim 4.3.1 requires numpy>=1.18.5, but you have numpy 1.16.6 which is incompatible.\n",
            "gensim 4.3.1 requires scipy>=1.7.0, but you have scipy 1.3.3 which is incompatible.\n",
            "cmdstanpy 1.1.0 requires numpy>=1.21, but you have numpy 1.16.6 which is incompatible.\n",
            "chex 0.1.7 requires numpy>=1.18.0, but you have numpy 1.16.6 which is incompatible.\n",
            "astropy 5.2.2 requires numpy>=1.20, but you have numpy 1.16.6 which is incompatible.\n",
            "arviz 0.15.1 requires numpy>=1.20.0, but you have numpy 1.16.6 which is incompatible.\n",
            "arviz 0.15.1 requires scipy>=1.8.0, but you have scipy 1.3.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed numpy-1.16.6 scikit-tensor-py3-0.4.1 scipy-1.3.3\n"
          ]
        }
      ],
      "source": [
        "!pip install scikit-tensor-py3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "X8zAb32P2vQW",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random\n",
        "import os\n",
        "# from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler,OneHotEncoder\n",
        "from sklearn.metrics import mean_squared_error \n",
        "from sklearn.metrics import mean_absolute_error\n",
        "import joblib\n",
        "#\n",
        "# from keras.models import Sequential,Model\n",
        "# from keras.layers import Dense,Input,Embedding,Concatenate,Reshape,Flatten\n",
        "# from keras.layers import LSTM,Dropout,Activation,Bidirectional\n",
        "# from keras.optimizers import Adagrad, Adam, SGD, RMSprop\n",
        "# from keras.models import load_model,save_model\n",
        "# from keras.utils import plot_model\n",
        "#\n",
        "\n",
        "\n",
        "import plotly.express as px\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras.models import Sequential,Model\n",
        "from tensorflow.keras.layers import Dense, LSTM,Dropout,Activation,Input,Bidirectional\n",
        "from tensorflow.keras.optimizers import Adagrad, Adam, SGD, RMSprop\n",
        "from tensorflow.keras.models import load_model,save_model\n",
        "from tensorflow.keras.utils import plot_model,to_categorical\n",
        "# import tensorflow as tf\n",
        "# from tensorflow import keras\n",
        "# drive.mount('/content/gdrive/') "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uAn8bR__pzBE",
        "outputId": "e2787e3e-20b8-41e1-b793-7fcbb16e44cf",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import pandas as pd\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qqWjFcrvAf5o"
      },
      "source": [
        "# Load Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hhzcaJOGIn7y",
        "outputId": "8ee5fd45-b409-445b-cc29-1005a3c0c9b6",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(50253, 12)\n",
            "(12578, 12)\n",
            "CPU times: user 24.1 s, sys: 7.91 s, total: 32 s\n",
            "Wall time: 46.7 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "# Load Raw Data\n",
        "# https://drive.google.com/file/d/1-RXYMh61x9asaVU_w5tDHtq0CMmp_Rdn/view?usp=sharing\n",
        "file_path_train = '/content/drive/MyDrive/MiniProject/TrainData.csv'\n",
        "# https://drive.google.com/file/d/1--znLUhuYqoMMqQo1Gnf6WafrzzniIFL/view?usp=sharing\n",
        "file_path_test ='/content/drive/MyDrive/MiniProject/TestData.csv'\n",
        "cols = ['id','userId', 'gender', 'sport', 'calories', 'Route_id','derived_distance','altitude','derived_speed','heart_rate','distance','time_elapsed']\n",
        "train_df = pd.read_csv(file_path_train,usecols=cols)\n",
        "test_df = pd.read_csv(file_path_test,usecols=cols)\n",
        "train_df.rename(columns={'derived_speed': 'speed'},inplace=True)\n",
        "\n",
        "test_df.rename(columns={'derived_speed': 'speed'},inplace=True)\n",
        "print(train_df.shape)\n",
        "print(test_df.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0C3vxwbeeQDE",
        "outputId": "24d641b9-de66-4be8-8fc2-6813dc18008c",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eGZQi2Gn5jfa",
        "outputId": "1b26ad1e-4c90-440b-cecd-26da688bbf06",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Route_id  sport        \n",
              "0         bike              4722\n",
              "1         bike              2756\n",
              "2         bike             10666\n",
              "3         bike               788\n",
              "4         bike               259\n",
              "5         mountain bike      549\n",
              "6         mountain bike      131\n",
              "7         mountain bike     1941\n",
              "8         run               3271\n",
              "9         run               3353\n",
              "10        run              17465\n",
              "11        run                819\n",
              "12        run               3089\n",
              "13        run                444\n",
              "dtype: int64"
            ]
          },
          "execution_count": 111,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df.groupby(['Route_id','sport']).size()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "8D_2GDAIgQWf",
        "outputId": "4646cc16-8d0c-4317-c74e-45a3d1ddb3bb",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>userId</th>\n",
              "      <th>gender</th>\n",
              "      <th>sport</th>\n",
              "      <th>calories</th>\n",
              "      <th>distance</th>\n",
              "      <th>altitude</th>\n",
              "      <th>heart_rate</th>\n",
              "      <th>derived_distance</th>\n",
              "      <th>speed</th>\n",
              "      <th>time_elapsed</th>\n",
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              "      <td>bike</td>\n",
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              "      <td>282989006</td>\n",
              "      <td>1912029</td>\n",
              "      <td>female</td>\n",
              "      <td>run</td>\n",
              "      <td>848.631</td>\n",
              "      <td>10.89</td>\n",
              "      <td>[498.8, 498.4, 497.4, 495.2, 493.4, 491.0, 490...</td>\n",
              "      <td>[82, 93, 104, 117, 128, 135, 137, 139, 138, 14...</td>\n",
              "      <td>[0.010829722470982562, 0.010618715062935378, 0...</td>\n",
              "      <td>[7.797400179107444, 8.821489242357188, 8.31756...</td>\n",
              "      <td>[5, 4, 7, 8, 11, 7, 19, 6, 13, 13, 13, 11, 11,...</td>\n",
              "      <td>8</td>\n",
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              "  "
            ],
            "text/plain": [
              "          id    userId   gender sport  calories  distance  \\\n",
              "0  611012078   2568526     male   run   830.588     10.02   \n",
              "1  326004692  11889307  unknown  bike   530.000     46.04   \n",
              "2  282989006   1912029   female   run   848.631     10.89   \n",
              "\n",
              "                                            altitude  \\\n",
              "0  [137.8, 137.8, 138.2, 138.8, 138.8, 138.6, 139...   \n",
              "1  [18.4, 17.6, 17.6, 17.6, 19.0, 19.8, 21.4, 22....   \n",
              "2  [498.8, 498.4, 497.4, 495.2, 493.4, 491.0, 490...   \n",
              "\n",
              "                                          heart_rate  \\\n",
              "0  [140, 140, 141, 149, 149, 150, 153, 157, 160, ...   \n",
              "1  [80, 75, 68, 71, 78, 71, 67, 66, 64, 75, 68, 6...   \n",
              "2  [82, 93, 104, 117, 128, 135, 137, 139, 138, 14...   \n",
              "\n",
              "                                    derived_distance  \\\n",
              "0  [0.0016049429213742246, 0.01260242289257531, 0...   \n",
              "1  [0.04356151376031798, 0.11363834298901357, 0.0...   \n",
              "2  [0.010829722470982562, 0.010618715062935378, 0...   \n",
              "\n",
              "                                               speed  \\\n",
              "0  [5.777794516947209, 10.005378447215753, 10.428...   \n",
              "1  [26.136908256190786, 34.09150289670407, 39.197...   \n",
              "2  [7.797400179107444, 8.821489242357188, 8.31756...   \n",
              "\n",
              "                                        time_elapsed  Route_id  \n",
              "0  [1, 4, 5, 1, 5, 6, 11, 4, 6, 7, 8, 7, 7, 9, 5,...         9  \n",
              "1  [6, 12, 7, 11, 8, 10, 11, 8, 9, 13, 6, 13, 11,...         0  \n",
              "2  [5, 4, 7, 8, 11, 7, 19, 6, 13, 13, 13, 11, 11,...         8  "
            ]
          },
          "execution_count": 112,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "id": "gjqHf9FTNRP3",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "# Encode gender feature\n",
        "\n",
        "conditions = [\n",
        "    (train_df['gender'] == 'male'),\n",
        "    (train_df['gender'] == 'female'),\n",
        "    (train_df['gender'] == 'unknown')]\n",
        "\n",
        "choices = [0, 1, 2]\n",
        "\n",
        "train_df['genderId'] = np.select(conditions, choices, default=0)\n",
        "\n",
        "conditions = [\n",
        "    (test_df['gender'] == 'male'),\n",
        "    (test_df['gender'] == 'female'),\n",
        "    (test_df['gender'] == 'unknown')]\n",
        "\n",
        "test_df['genderId'] = np.select(conditions, choices, default=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "id": "6QugUXdXSpWe",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "# Encode sport feature\n",
        "\n",
        "conditions = [\n",
        "    (train_df['sport'] == 'run'),\n",
        "    (train_df['sport'] == 'bike'),\n",
        "    (train_df['sport'] == 'mountain bike')]\n",
        "\n",
        "choices = [0, 1, 2]\n",
        "\n",
        "train_df['sportId'] = np.select(conditions, choices, default=0)\n",
        "\n",
        "conditions = [\n",
        "    (test_df['sport'] == 'run'),\n",
        "    (test_df['sport'] == 'bike'),\n",
        "    (test_df['sport'] == 'mountain bike')]\n",
        "\n",
        "test_df['sportId'] = np.select(conditions, choices, default=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "zLejQ5hrplSL",
        "outputId": "2327f0e7-e630-4381-91f5-bfd49e34fe7c",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
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            "text/plain": [
              "          id    userId   gender sport  calories  distance  \\\n",
              "0  611012078   2568526     male   run   830.588     10.02   \n",
              "1  326004692  11889307  unknown  bike   530.000     46.04   \n",
              "2  282989006   1912029   female   run   848.631     10.89   \n",
              "\n",
              "                                            altitude  \\\n",
              "0  [137.8, 137.8, 138.2, 138.8, 138.8, 138.6, 139...   \n",
              "1  [18.4, 17.6, 17.6, 17.6, 19.0, 19.8, 21.4, 22....   \n",
              "2  [498.8, 498.4, 497.4, 495.2, 493.4, 491.0, 490...   \n",
              "\n",
              "                                          heart_rate  \\\n",
              "0  [140, 140, 141, 149, 149, 150, 153, 157, 160, ...   \n",
              "1  [80, 75, 68, 71, 78, 71, 67, 66, 64, 75, 68, 6...   \n",
              "2  [82, 93, 104, 117, 128, 135, 137, 139, 138, 14...   \n",
              "\n",
              "                                    derived_distance  \\\n",
              "0  [0.0016049429213742246, 0.01260242289257531, 0...   \n",
              "1  [0.04356151376031798, 0.11363834298901357, 0.0...   \n",
              "2  [0.010829722470982562, 0.010618715062935378, 0...   \n",
              "\n",
              "                                               speed  \\\n",
              "0  [5.777794516947209, 10.005378447215753, 10.428...   \n",
              "1  [26.136908256190786, 34.09150289670407, 39.197...   \n",
              "2  [7.797400179107444, 8.821489242357188, 8.31756...   \n",
              "\n",
              "                                        time_elapsed  Route_id  genderId  \\\n",
              "0  [1, 4, 5, 1, 5, 6, 11, 4, 6, 7, 8, 7, 7, 9, 5,...         9         0   \n",
              "1  [6, 12, 7, 11, 8, 10, 11, 8, 9, 13, 6, 13, 11,...         0         2   \n",
              "2  [5, 4, 7, 8, 11, 7, 19, 6, 13, 13, 13, 11, 11,...         8         1   \n",
              "\n",
              "   sportId  \n",
              "0        0  \n",
              "1        1  \n",
              "2        0  "
            ]
          },
          "execution_count": 115,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
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        "id": "AZC4-Tm7AQqS",
        "outputId": "c4564b24-820b-48b8-9702-477e13c1f90e",
        "vscode": {
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        }
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-118-193143c29c89>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m# Reshape data using array.reshape(-1, 1) if data has a single feature\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mscaler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMinMaxScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     train_sequence_df[feature] = train_sequence_df.apply(lambda x: np.concatenate(\n\u001b[0m\u001b[1;32m      8\u001b[0m         scaler.fit_transform(np.array(eval(x[feature])).reshape(-1, 1)), axis=0)[0:499], axis=1)\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, raw, result_type, args, **kwargs)\u001b[0m\n\u001b[1;32m   9566\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9567\u001b[0m         )\n\u001b[0;32m-> 9568\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"apply\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   9569\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9570\u001b[0m     def applymap(\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    762\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    763\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 764\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    765\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    889\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    890\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 891\u001b[0;31m         \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_series_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m         \u001b[0;31m# wrap results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    905\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    906\u001b[0m                 \u001b[0;31m# ignore SettingWithCopy here in case the user mutates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 907\u001b[0;31m                 \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    908\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m                     \u001b[0;31m# If we have a view on v, we need to make a copy because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-118-193143c29c89>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mscaler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMinMaxScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     train_sequence_df[feature] = train_sequence_df.apply(lambda x: np.concatenate(\n\u001b[0;32m----> 8\u001b[0;31m         scaler.fit_transform(np.array(eval(x[feature])).reshape(-1, 1)), axis=0)[0:499], axis=1)\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     test_sequence_df[feature] = test_sequence_df.apply(lambda x: np.concatenate(\n",
            "\u001b[0;31mValueError\u001b[0m: source code string cannot contain null bytes"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# for features in sequence_df\n",
        "for feature in train_sequence_df.columns:\n",
        "    # Reshape data using array.reshape(-1, 1) if data has a single feature\n",
        "    scaler = MinMaxScaler()\n",
        "    train_sequence_df[feature] = train_sequence_df.apply(lambda x: np.concatenate(\n",
        "        scaler.fit_transform(np.array(eval(x[feature])).reshape(-1, 1)), axis=0)[0:499], axis=1)\n",
        "    \n",
        "    test_sequence_df[feature] = test_sequence_df.apply(lambda x: np.concatenate(\n",
        "        scaler.transform(np.array(eval(x[feature])).reshape(-1, 1)), axis=0)[0:499], axis=1)\n",
        "\n",
        "\n",
        "# for features in context_df\n",
        "for feature in train_context_df[train_context_df.columns.difference(['id','userId',\n",
        "              'genderId', \n",
        "              'sportId', \n",
        "              'Route_id'])].columns:\n",
        "    # Reshape data using array.reshape(-1, 1) if data has a single feature\n",
        "    scaler = MinMaxScaler()\n",
        "    train_context_df[feature] = scaler.fit_transform(\n",
        "        train_context_df[feature].to_numpy().reshape(-1, 1))\n",
        "    \n",
        "    test_context_df[feature] = scaler.transform(\n",
        "        test_context_df[feature].to_numpy().reshape(-1, 1))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "ods9I6KXuDit",
        "outputId": "bf07d275-32d7-4b75-d8ef-e4bf8b8a9de9",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\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-0677b2ab-3b11-41b6-985b-ef34af8ce507 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-0677b2ab-3b11-41b6-985b-ef34af8ce507');\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": [
              "          id   userId  genderId  sportId  calories  distance  Route_id\n",
              "0  611012078  2568526         0        0   830.588     10.02         9"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "train_sequence_df = train_df[['altitude', 'heart_rate',\n",
        "                       'derived_distance', 'speed']].copy()\n",
        "train_context_df = train_df[['id','userId', 'genderId', 'sportId', 'calories', 'distance', 'Route_id']].copy()\n",
        "\n",
        "test_sequence_df = test_df[['altitude', 'heart_rate',\n",
        "                       'derived_distance', 'speed']].copy()\n",
        "test_context_df = test_df[['id','userId', 'genderId', 'sportId', 'calories', 'distance', 'Route_id']].copy()\n",
        "\n",
        "display(train_sequence_df.head(1))\n",
        "display(train_context_df.head(1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "wyiOP3zUv7MU",
        "outputId": "722c7118-12fc-4a3a-c808-f6ce51e49435",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
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              "  "
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              "                                            altitude  \\\n",
              "0  [0.9747634069400635, 0.9747634069400635, 0.981...   \n",
              "\n",
              "                                          heart_rate  \\\n",
              "0  [0.5714285714285715, 0.5714285714285715, 0.580...   \n",
              "\n",
              "                                    derived_distance  \\\n",
              "0  [0.0, 0.2570558362253222, 0.3811466729812352, ...   \n",
              "\n",
              "                                               speed  \n",
              "0  [0.0, 0.42660865297738426, 0.4693170206773962,...  "
            ]
          },
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              "      <th>id</th>\n",
              "      <th>userId</th>\n",
              "      <th>genderId</th>\n",
              "      <th>sportId</th>\n",
              "      <th>calories</th>\n",
              "      <th>distance</th>\n",
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              "            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",
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            "text/plain": [
              "          id   userId  genderId  sportId  calories  distance  Route_id\n",
              "0  611012078  2568526         0        0  0.133587  0.068544         9"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "display(train_sequence_df.head(1))\n",
        "display(train_context_df.head(1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "Hkbx4RlpRSr8",
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        "vscode": {
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      "outputs": [
        {
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              "          document.querySelector('#df-bfe63ee0-ac30-488f-ad74-b853e5ea48ba 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-bfe63ee0-ac30-488f-ad74-b853e5ea48ba');\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": [
              "          id   userId  genderId  sportId  calories  distance  Route_id  \\\n",
              "0  611012078  2568526         0        0  0.133587  0.068544         9   \n",
              "\n",
              "                                            altitude  \\\n",
              "0  [0.9747634069400635, 0.9747634069400635, 0.981...   \n",
              "\n",
              "                                          heart_rate  \\\n",
              "0  [0.5714285714285715, 0.5714285714285715, 0.580...   \n",
              "\n",
              "                                    derived_distance  \\\n",
              "0  [0.0, 0.2570558362253222, 0.3811466729812352, ...   \n",
              "\n",
              "                                               speed  \n",
              "0  [0.0, 0.42660865297738426, 0.4693170206773962,...  "
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df = pd.concat([train_context_df, train_sequence_df], axis=1)\n",
        "test_df = pd.concat([test_context_df, test_sequence_df], axis=1)\n",
        "\n",
        "train_df.head(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_QeA3FqmroX2"
      },
      "source": [
        "### Load embedding layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "UubsaZWxkRyE",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "# load embedding layer r = 13\n",
        "\n",
        "routeEmbed_file_path = '/content/drive/MyDrive/MiniProject/Personalized_Intelligent_Fitness_Recommender-main/entity embedding/routeEmbed_tensorD_13.m'\n",
        "userEmbed_file_path = '/content/drive/MyDrive/MiniProject/Personalized_Intelligent_Fitness_Recommender-main/entity embedding/userEmbed_tensorD_13.m'\n",
        "# # load embedding layer r = 11\n",
        "# routeEmbed_file_path = '/content/gdrive/My Drive/endomondoHR/Data/embedding/routeEmbed_tensorD_11.m'\n",
        "# userEmbed_file_path = '/content/gdrive/My Drive/endomondoHR/Data/embedding/userEmbed_tensorD_11.m'\n",
        "# # load embedding layer r = 2\n",
        "# routeEmbed_file_path = '/content/gdrive/My Drive/endomondoHR/Data/embedding/routeEmbed_tensorD_2.m'\n",
        "# userEmbed_file_path = '/content/gdrive/My Drive/endomondoHR/Data/embedding/userEmbed_tensorD_2.m'\n",
        "\n",
        "route_embed_df=joblib.load(routeEmbed_file_path)\n",
        "user_embed_df=joblib.load(userEmbed_file_path)\n",
        "route_embed_dict = {row['Route_id']:list(row['routeEmbed']) for index,row in route_embed_df.iterrows()}\n",
        "user_embed_dict = {row['userId']:list(row['userEmbed']) for index,row in user_embed_df.iterrows()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "PAiaFKmC_KCr",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "def flatten_workout(df,workout_id):\n",
        "  # The original sequencial data is in one cell of pd.DataFrame, and this function is to flatten it\n",
        "  temp = df[df['id'] == workout_id].copy()\n",
        "  temp.reset_index(drop=1,inplace=True)\n",
        "  if len(temp) != 1:\n",
        "    return None  \n",
        "  context_info = ['id','userId','genderId','calories','sportId','Route_id']\n",
        "  context_dict = {col:temp[col][0] for col in context_info}\n",
        "  seq = []\n",
        "  speed = list(temp['speed'])[0]\n",
        "  altitude = list(temp['altitude'])[0]\n",
        "  distance = list(temp['derived_distance'])[0]\n",
        "  hr = list(temp['heart_rate'])[0]\n",
        "  seq = [[altitude[i],distance[i],hr[i],speed[i]] for i in range(499)]\n",
        "  sequence = np.array(seq)\n",
        "  context = np.array([[context_dict['id'],context_dict['userId'],context_dict['genderId'],context_dict['sportId'],context_dict['Route_id'],context_dict['calories']]]*499)\n",
        "  array = np.concatenate((context,\n",
        "                sequence),axis=1)\n",
        "  columns=['workoutId','userId','genderId','sportId','Route_id','calories','altitude','derived_distance','heart_rate','speed']\n",
        "  df_sub = pd.DataFrame(array,columns=columns)\n",
        "  df_sub = df_sub.astype({col:'float64' for col in columns if col not in ['gender','sport']})\n",
        "  return df_sub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "eEGyMU8Aysk_",
        "outputId": "47d0f312-811c-4040-82e9-629f0a6b1323",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
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              "      <td>0.0</td>\n",
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              "      <td>0.011364</td>\n",
              "      <td>0.548316</td>\n",
              "      <td>0.733333</td>\n",
              "      <td>0.608518</td>\n",
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              "      <td>2568526.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>499 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-47b881e5-fb39-48af-acaf-6b8fe815ae0d')\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-47b881e5-fb39-48af-acaf-6b8fe815ae0d 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-47b881e5-fb39-48af-acaf-6b8fe815ae0d');\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",
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              "  "
            ],
            "text/plain": [
              "       workoutId     userId  genderId  sportId  Route_id  calories  altitude  \\\n",
              "0    230506076.0  2568526.0       0.0      1.0       0.0  0.129091  0.048295   \n",
              "1    230506076.0  2568526.0       0.0      1.0       0.0  0.129091  0.053977   \n",
              "2    230506076.0  2568526.0       0.0      1.0       0.0  0.129091  0.053977   \n",
              "3    230506076.0  2568526.0       0.0      1.0       0.0  0.129091  0.053977   \n",
              "4    230506076.0  2568526.0       0.0      1.0       0.0  0.129091  0.053977   \n",
              "..           ...        ...       ...      ...       ...       ...       ...   \n",
              "494  230506076.0  2568526.0       0.0      1.0       0.0  0.129091  0.059659   \n",
              "495  230506076.0  2568526.0       0.0      1.0       0.0  0.129091  0.034091   \n",
              "496  230506076.0  2568526.0       0.0      1.0       0.0  0.129091  0.011364   \n",
              "497  230506076.0  2568526.0       0.0      1.0       0.0  0.129091  0.000000   \n",
              "498  230506076.0  2568526.0       0.0      1.0       0.0  0.129091  0.000000   \n",
              "\n",
              "     derived_distance  heart_rate     speed  \n",
              "0            0.102514    0.000000  0.019794  \n",
              "1            0.083503    0.066667  0.037591  \n",
              "2            0.542649    0.093333  0.281373  \n",
              "3            0.266649    0.200000  0.341796  \n",
              "4            0.277506    0.266667  0.314266  \n",
              "..                ...         ...       ...  \n",
              "494          0.180381    0.720000  0.747796  \n",
              "495          0.415899    0.773333  0.634597  \n",
              "496          0.548316    0.733333  0.608518  \n",
              "497          0.393595    0.733333  0.666787  \n",
              "498          0.553295    0.800000  0.269137  \n",
              "\n",
              "[499 rows x 10 columns]"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "flatten_workout(train_df,train_df['id'][50249])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AoGeeIXyfA4O"
      },
      "source": [
        "### Generate Scaler\n",
        "No need to run\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        },
        "id": "YC7AD_cLe2zo",
        "outputId": "a7edfadc-5bdd-47e8-d11c-d633905ef549",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'train_ids' is not defined"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "# 不需要跑\n",
        "features = ['calories','distance','duration','time_elapsed','altitude','derived_distance','speed','heart_rate']\n",
        "# features = ['calories','distance','duration','altitude','derived_distance','speed','heart_rate']\n",
        "# get minmaxscaler for different features\n",
        "features_dict = {f:[0] for f in features}\n",
        "count = 0\n",
        "for id in train_ids:\n",
        "  if count % 5000==0:\n",
        "    print(count)\n",
        "  df_sub =flatten_workout(train_df,id)\n",
        "  for feature in features:\n",
        "    features_dict[feature].append(max(df_sub[feature]))\n",
        "    features_dict[feature].append(min(df_sub[feature]))\n",
        "  count+=1\n",
        "features_mm = {k:MinMaxScaler().fit(np.array(features_dict[k]).reshape(-1,1)) for k,v in features_dict.items()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tt7K2xq7jV9F",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "# save scaler model\n",
        "# 不需要跑 \n",
        "dir = '/content/gdrive/My Drive/endomondoHR/Data/scaler_model'\n",
        "for k,model in features_mm.items():\n",
        "  path = os.path.join(dir,'scaler_'+k+'_2.m')\n",
        "  joblib.dump(model, path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UP8NMFX_sKqW"
      },
      "source": [
        "### Load Scaler"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uh8TC-PCs78e"
      },
      "source": [
        "## Data Loader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "kbFmvQigDAJo",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "# features we are going to use\n",
        "features = ['calories','altitude','derived_distance','speed','heart_rate']\n",
        "def convert_category(value,length):\n",
        "  array_list = [0]*length\n",
        "  array_list[int(value)] = 1\n",
        "  return [array_list]*499"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "_JjvsS6FdO4S",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "# generate scaled input\n",
        "def generate_scaled(df_sub):\n",
        "  gender = convert_category(df_sub['genderId'][0],3)\n",
        "  sport = convert_category(df_sub['sportId'][0],3)\n",
        "  route = [route_embed_dict[int(df_sub['Route_id'][0])]]*499\n",
        "  user = [user_embed_dict[int(df_sub['userId'][0])]]*499\n",
        "  feature_array = np.concatenate((gender,sport,route,user),axis=1)\n",
        "  # feature_array = np.concatenate((gender,sport,user),axis=1)\n",
        "  speed = []\n",
        "  for k in features:\n",
        "    # speed input\n",
        "    if k == 'speed':\n",
        "      speed = df_sub[k].values.reshape(-1,1)\n",
        "    # heart rate input\n",
        "    elif k == 'heart_rate': \n",
        "      hr = df_sub[k].values.reshape(-1,1)\n",
        "    else:\n",
        "      array = df_sub[k].values.reshape(-1,1)\n",
        "      feature_array = np.concatenate((feature_array,array),axis=1)\n",
        "  x1 = feature_array\n",
        "  y1 = speed\n",
        "  y2 = hr\n",
        "  return x1.reshape(1,x1.shape[0],x1.shape[1]),y1.reshape(y1.shape[0],1),y2.reshape(y2.shape[0],1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eDb886Jtuoid",
        "outputId": "bc528c74-cd1d-4e51-8bee-d87e4d6a7f56",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Inputs shape:(1, 499, 35)\n"
          ]
        }
      ],
      "source": [
        "x,y1,y2 = generate_scaled(flatten_workout(train_df,\t282989006))\n",
        "print('Inputs shape:{}'.format(x.shape))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "ptG-TauDxv2i",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "# batch_generater\n",
        "def batch_generator(df,batch_size=32):\n",
        "  ids = list(df['id'])\n",
        "  while True:\n",
        "    X1,Y1,Y2= [],[],[]\n",
        "    for id in random.sample(ids,batch_size):\n",
        "      x1,y1,y2 = generate_scaled(flatten_workout(df,id))\n",
        "      X1.append(x1[0])\n",
        "      Y1.append(y1.flatten())\n",
        "      Y2.append(y2.flatten())\n",
        "      ids.remove(id)\n",
        "    if len(ids) < batch_size:\n",
        "      ids = list(df['id'])\n",
        "    # yield np.array(X),[np.array(Y1),np.array(Y2),np.array(Y3)]\n",
        "    yield np.array(X1),[np.array(Y1),np.array(Y2)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FaYhcdwss4Du"
      },
      "source": [
        "# Modelling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1y14BBUmuS4c"
      },
      "source": [
        "## Build Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mxDmf_ogncH7",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "def single_lstm():\n",
        "  input = Input(shape=(x.shape[1],x.shape[2]),name='Input')\n",
        "  # lstm layer\n",
        "  lstm_layer1 = LSTM(64,return_sequences=True,name='lstm1')(input)\n",
        "  # dropout\n",
        "  drop_hr = Dropout(0.2)(lstm_layer1)\n",
        "  drop_speed = Dropout(0.2)(lstm_layer1)\n",
        "  speed_output = Dense(1,activation='selu',name='speed_output')(drop_speed)\n",
        "  hr_output = Dense(1,activation='selu',name='hr_output')(drop_hr)\n",
        "  model = Model(inputs=input,outputs = [speed_output,hr_output])\n",
        "  model.compile(loss={\n",
        "                    'speed_output': 'mae',\n",
        "                    'hr_output': 'mae'},\n",
        "                loss_weights={\n",
        "                    'speed_output': 1,\n",
        "                    'hr_output':1\n",
        "                },\n",
        "                optimizer=RMSprop(lr=0.005))\n",
        "  return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0UrA0_KZ2lP3",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "# single BiLSTM Model\n",
        "def Bilstm():\n",
        "  input = Input(shape=(x.shape[1],x.shape[2]),name='Input')\n",
        "  # bilstm layer\n",
        "  lstm_layer1 = Bidirectional(LSTM(64,return_sequences=True,name='lstm1'))(input)\n",
        "  # dropout\n",
        "  drop_hr = Dropout(0.2)(lstm_layer1)\n",
        "  drop_speed = Dropout(0.2)(lstm_layer1)\n",
        "  hr_output = Dense(1,activation='selu',name='hr_output')(drop_hr)\n",
        "  speed_output = Dense(1,activation='selu',name='speed_output')(drop_speed)\n",
        "\n",
        "  model = Model(inputs=input, outputs = [speed_output,hr_output])\n",
        "  model.compile(loss={\n",
        "                    'speed_output': 'mae',\n",
        "                    'hr_output': 'mae'},\n",
        "                loss_weights={\n",
        "                    'speed_output': 1,\n",
        "                    'hr_output':1\n",
        "                },\n",
        "                optimizer=RMSprop(lr=0.005))\n",
        "  return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pZZzrNA-f78Q",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "# 2 layer stacked LSTM model\n",
        "def two_layer_LSTM():\n",
        "  input = Input(shape=(x.shape[1],x.shape[2]),name='Input')\n",
        "  # first lstm layer\n",
        "  lstm_layer1 = LSTM(128,return_sequences=True,name='lstm1')(input)\n",
        "  # dropout layer\n",
        "  drop_hr = Dropout(0.2)(lstm_layer1)\n",
        "  hr_output = Dense(1,activation='selu',name='hr_output')(drop_hr)\n",
        "  # second lstm layer\n",
        "  lstm_layer2 = LSTM(64,return_sequences=True,name='lstm2')(lstm_layer1)\n",
        "  drop_speed = Dropout(0.2)(lstm_layer2)\n",
        "  speed_output = Dense(1,activation='selu',name='speed_output')(drop_speed)\n",
        "\n",
        "  model = Model(inputs=input, outputs = [speed_output,hr_output])\n",
        "  model.compile(loss={\n",
        "                    'speed_output': 'mae',\n",
        "                    'hr_output': 'mae'},\n",
        "                loss_weights={\n",
        "                    'speed_output': 1,\n",
        "                    'hr_output':1\n",
        "                },\n",
        "                optimizer=RMSprop(lr=0.005))\n",
        "  return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KEiehX6BOuec",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "#  2 layer stacked BiLSTM Model \n",
        "def two_layer_BiLSTM():\n",
        "  input = Input(shape=(x.shape[1],x.shape[2]),name='Input')\n",
        "  # first bilstm layer\n",
        "  lstm_layer1 = Bidirectional(LSTM(64,return_sequences=True,name='lstm1'))(input)\n",
        "  # dropout layer\n",
        "  drop_hr = Dropout(0.2)(lstm_layer1)\n",
        "  hr_output = Dense(1,activation='selu',name='hr_output')(drop_hr)\n",
        "  # second bilstm layer\n",
        "  lstm_layer2 = Bidirectional(LSTM(64,return_sequences=True,name='lstm2'))(lstm_layer1)\n",
        "  drop_speed = Dropout(0.2)(lstm_layer2)\n",
        "  speed_output = Dense(1,activation='selu',name='speed_output')(drop_speed)\n",
        "\n",
        "  model = Model(inputs=input, outputs = [speed_output,hr_output])\n",
        "  model.compile(loss={\n",
        "                    'speed_output': 'mae',\n",
        "                    'hr_output': 'mae'},\n",
        "                loss_weights={\n",
        "                    'speed_output': 1,\n",
        "                    'hr_output':1\n",
        "                },\n",
        "                optimizer=RMSprop(lr=0.005))\n",
        "  return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LHM28x5J2pYV",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.layers import Dense, Lambda, dot, Activation, concatenate\n",
        "from tensorflow.keras.layers import Layer\n",
        "\n",
        "\n",
        "class Attention(Layer):\n",
        "\n",
        "    def __init__(self, **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "\n",
        "    def __call__(self, hidden_states):\n",
        "        \"\"\"\n",
        "        Many-to-one attention mechanism for Keras.\n",
        "        @param hidden_states: 3D tensor with shape (batch_size, time_steps, input_dim).\n",
        "        @return: 2D tensor with shape (batch_size, 128)\n",
        "        @author: felixhao28.\n",
        "        \"\"\"\n",
        "        hidden_size = int(hidden_states.shape[2])\n",
        "        score_first_part = Dense(hidden_size, use_bias=False)(hidden_states)\n",
        "        h_t = Lambda(lambda x: x[:, -1, :], output_shape=(hidden_size,))(hidden_states)\n",
        "        score = dot([score_first_part, h_t], [2, 1])\n",
        "        attention_weights = Activation('softmax')(score)\n",
        "        # (batch_size, time_steps, hidden_size) dot (batch_size, time_steps) => (batch_size, hidden_size)\n",
        "        context_vector = dot([hidden_states, attention_weights], [1, 1])\n",
        "        pre_activation = concatenate([context_vector, h_t])\n",
        "        attention_vector = Dense(64, use_bias=True, activation='tanh')(pre_activation)\n",
        "        return attention_vector\n",
        "\n",
        "\n",
        "# USE heart rate predict speed\n",
        "def two_layer_BiLSTM_Attention():\n",
        "  input = Input(shape=(x.shape[1],x.shape[2]),name='Input')\n",
        "  # first lstm layer\n",
        "  lstm_layer1 = Bidirectional(LSTM(64,return_sequences=True,name='lstm1'))(input)\n",
        "  # attention layer\n",
        "  att_1 = Attention()(lstm_layer1)\n",
        "  drop_hr = Dropout(0.2)(att_1)\n",
        "  hr_output = Dense(1,activation='selu',name='hr_output')(att_1)\n",
        "\n",
        "  lstm_layer2 = Bidirectional(LSTM(64,return_sequences=True,name='lstm2'))(lstm_layer1)\n",
        "  att_2 = Attention()(lstm_layer2)\n",
        "  drop_speed = Dropout(0.2)(att_2)\n",
        "  speed_output = Dense(1,activation='selu',name='speed_output')(att_2)\n",
        "\n",
        "  model = Model(inputs=input, outputs = [speed_output,hr_output])\n",
        "  model.compile(loss={\n",
        "                    'speed_output': 'mae',\n",
        "                    'hr_output': 'mae'},\n",
        "                loss_weights={\n",
        "                    'speed_output': 1,\n",
        "                    'hr_output':1\n",
        "                },\n",
        "                optimizer=Adagrad(lr=0.001))\n",
        "  return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0oWPshLPuXW-"
      },
      "source": [
        "## Train Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qrC8CGpkHDGr",
        "outputId": "ba7fdca7-1ea3-4103-badd-d3cad985c15d",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "196/196 [==============================] - 345s 2s/step - loss: 0.4023 - speed_output_loss: 0.2002 - hr_output_loss: 0.2021 - val_loss: 1.0965 - val_speed_output_loss: 0.2209 - val_hr_output_loss: 0.8755\n",
            "Epoch 2/50\n",
            "196/196 [==============================] - 330s 2s/step - loss: 0.3381 - speed_output_loss: 0.1794 - hr_output_loss: 0.1588 - val_loss: 1.0257 - val_speed_output_loss: 0.1982 - val_hr_output_loss: 0.8275\n",
            "Epoch 3/50\n",
            "196/196 [==============================] - 331s 2s/step - loss: 0.3305 - speed_output_loss: 0.1757 - hr_output_loss: 0.1548 - val_loss: 0.9318 - val_speed_output_loss: 0.1681 - val_hr_output_loss: 0.7637\n",
            "Epoch 4/50\n",
            "196/196 [==============================] - 333s 2s/step - loss: 0.3277 - speed_output_loss: 0.1744 - hr_output_loss: 0.1534 - val_loss: 0.8118 - val_speed_output_loss: 0.1612 - val_hr_output_loss: 0.6506\n",
            "Epoch 5/50\n",
            "196/196 [==============================] - 333s 2s/step - loss: 0.3260 - speed_output_loss: 0.1734 - hr_output_loss: 0.1525 - val_loss: 0.8136 - val_speed_output_loss: 0.1388 - val_hr_output_loss: 0.6748\n",
            "Epoch 6/50\n",
            "196/196 [==============================] - 330s 2s/step - loss: 0.3248 - speed_output_loss: 0.1728 - hr_output_loss: 0.1520 - val_loss: 0.7697 - val_speed_output_loss: 0.1438 - val_hr_output_loss: 0.6260\n",
            "Epoch 7/50\n",
            "196/196 [==============================] - 331s 2s/step - loss: 0.3241 - speed_output_loss: 0.1725 - hr_output_loss: 0.1516 - val_loss: 0.7664 - val_speed_output_loss: 0.1535 - val_hr_output_loss: 0.6129\n",
            "Epoch 8/50\n",
            "196/196 [==============================] - 331s 2s/step - loss: 0.3235 - speed_output_loss: 0.1721 - hr_output_loss: 0.1514 - val_loss: 0.7379 - val_speed_output_loss: 0.1409 - val_hr_output_loss: 0.5970\n",
            "Epoch 9/50\n",
            "196/196 [==============================] - 330s 2s/step - loss: 0.3226 - speed_output_loss: 0.1717 - hr_output_loss: 0.1509 - val_loss: 0.7245 - val_speed_output_loss: 0.1433 - val_hr_output_loss: 0.5813\n",
            "Epoch 10/50\n",
            "196/196 [==============================] - 325s 2s/step - loss: 0.3225 - speed_output_loss: 0.1716 - hr_output_loss: 0.1508 - val_loss: 0.7291 - val_speed_output_loss: 0.1379 - val_hr_output_loss: 0.5912\n",
            "Epoch 11/50\n",
            "196/196 [==============================] - 327s 2s/step - loss: 0.3224 - speed_output_loss: 0.1716 - hr_output_loss: 0.1508 - val_loss: 0.7185 - val_speed_output_loss: 0.1466 - val_hr_output_loss: 0.5719\n",
            "Epoch 12/50\n",
            "196/196 [==============================] - 325s 2s/step - loss: 0.3215 - speed_output_loss: 0.1709 - hr_output_loss: 0.1506 - val_loss: 0.7267 - val_speed_output_loss: 0.1652 - val_hr_output_loss: 0.5615\n",
            "Epoch 13/50\n",
            "196/196 [==============================] - 326s 2s/step - loss: 0.3215 - speed_output_loss: 0.1713 - hr_output_loss: 0.1502 - val_loss: 0.6870 - val_speed_output_loss: 0.1470 - val_hr_output_loss: 0.5400\n",
            "Epoch 14/50\n",
            "196/196 [==============================] - 326s 2s/step - loss: 0.3208 - speed_output_loss: 0.1706 - hr_output_loss: 0.1502 - val_loss: 0.7259 - val_speed_output_loss: 0.1484 - val_hr_output_loss: 0.5775\n",
            "Epoch 15/50\n",
            "196/196 [==============================] - 326s 2s/step - loss: 0.3214 - speed_output_loss: 0.1709 - hr_output_loss: 0.1504 - val_loss: 0.6927 - val_speed_output_loss: 0.1586 - val_hr_output_loss: 0.5341\n",
            "Epoch 16/50\n",
            "196/196 [==============================] - 325s 2s/step - loss: 0.3208 - speed_output_loss: 0.1706 - hr_output_loss: 0.1503 - val_loss: 0.7087 - val_speed_output_loss: 0.1523 - val_hr_output_loss: 0.5564\n",
            "Epoch 17/50\n",
            "196/196 [==============================] - 325s 2s/step - loss: 0.3202 - speed_output_loss: 0.1704 - hr_output_loss: 0.1498 - val_loss: 0.6912 - val_speed_output_loss: 0.1441 - val_hr_output_loss: 0.5471\n",
            "Epoch 18/50\n",
            "196/196 [==============================] - 322s 2s/step - loss: 0.3204 - speed_output_loss: 0.1704 - hr_output_loss: 0.1500 - val_loss: 0.7056 - val_speed_output_loss: 0.1636 - val_hr_output_loss: 0.5420\n",
            "Epoch 19/50\n",
            "196/196 [==============================] - 323s 2s/step - loss: 0.3203 - speed_output_loss: 0.1703 - hr_output_loss: 0.1500 - val_loss: 0.6654 - val_speed_output_loss: 0.1481 - val_hr_output_loss: 0.5173\n",
            "Epoch 20/50\n",
            "196/196 [==============================] - 324s 2s/step - loss: 0.3202 - speed_output_loss: 0.1705 - hr_output_loss: 0.1497 - val_loss: 0.6884 - val_speed_output_loss: 0.1542 - val_hr_output_loss: 0.5341\n",
            "Epoch 21/50\n",
            "196/196 [==============================] - 324s 2s/step - loss: 0.3198 - speed_output_loss: 0.1701 - hr_output_loss: 0.1498 - val_loss: 0.6781 - val_speed_output_loss: 0.1568 - val_hr_output_loss: 0.5213\n",
            "Epoch 22/50\n",
            "196/196 [==============================] - 326s 2s/step - loss: 0.3199 - speed_output_loss: 0.1700 - hr_output_loss: 0.1500 - val_loss: 0.6920 - val_speed_output_loss: 0.1558 - val_hr_output_loss: 0.5361\n",
            "Epoch 23/50\n",
            "196/196 [==============================] - 325s 2s/step - loss: 0.3197 - speed_output_loss: 0.1701 - hr_output_loss: 0.1497 - val_loss: 0.6806 - val_speed_output_loss: 0.1540 - val_hr_output_loss: 0.5265\n",
            "Epoch 24/50\n",
            "196/196 [==============================] - 325s 2s/step - loss: 0.3194 - speed_output_loss: 0.1698 - hr_output_loss: 0.1497 - val_loss: 0.6951 - val_speed_output_loss: 0.1417 - val_hr_output_loss: 0.5534\n",
            "Epoch 25/50\n",
            "196/196 [==============================] - 325s 2s/step - loss: 0.3196 - speed_output_loss: 0.1698 - hr_output_loss: 0.1498 - val_loss: 0.6657 - val_speed_output_loss: 0.1416 - val_hr_output_loss: 0.5240\n",
            "Epoch 26/50\n",
            "196/196 [==============================] - 329s 2s/step - loss: 0.3193 - speed_output_loss: 0.1697 - hr_output_loss: 0.1496 - val_loss: 0.6821 - val_speed_output_loss: 0.1398 - val_hr_output_loss: 0.5423\n",
            "Epoch 27/50\n",
            "196/196 [==============================] - 326s 2s/step - loss: 0.3194 - speed_output_loss: 0.1698 - hr_output_loss: 0.1496 - val_loss: 0.6643 - val_speed_output_loss: 0.1508 - val_hr_output_loss: 0.5135\n",
            "Epoch 28/50\n",
            "196/196 [==============================] - 336s 2s/step - loss: 0.3189 - speed_output_loss: 0.1695 - hr_output_loss: 0.1494 - val_loss: 0.6326 - val_speed_output_loss: 0.1457 - val_hr_output_loss: 0.4869\n",
            "Epoch 29/50\n",
            "196/196 [==============================] - 333s 2s/step - loss: 0.3192 - speed_output_loss: 0.1695 - hr_output_loss: 0.1497 - val_loss: 0.6661 - val_speed_output_loss: 0.1487 - val_hr_output_loss: 0.5174\n",
            "Epoch 30/50\n",
            "196/196 [==============================] - 325s 2s/step - loss: 0.3187 - speed_output_loss: 0.1695 - hr_output_loss: 0.1492 - val_loss: 0.6507 - val_speed_output_loss: 0.1458 - val_hr_output_loss: 0.5050\n",
            "Epoch 31/50\n",
            "196/196 [==============================] - 328s 2s/step - loss: 0.3192 - speed_output_loss: 0.1696 - hr_output_loss: 0.1496 - val_loss: 0.6747 - val_speed_output_loss: 0.1485 - val_hr_output_loss: 0.5263\n",
            "Epoch 32/50\n",
            "196/196 [==============================] - 326s 2s/step - loss: 0.3187 - speed_output_loss: 0.1694 - hr_output_loss: 0.1493 - val_loss: 0.6588 - val_speed_output_loss: 0.1416 - val_hr_output_loss: 0.5172\n",
            "Epoch 33/50\n",
            "196/196 [==============================] - 326s 2s/step - loss: 0.3185 - speed_output_loss: 0.1692 - hr_output_loss: 0.1493 - val_loss: 0.6337 - val_speed_output_loss: 0.1406 - val_hr_output_loss: 0.4931\n",
            "Epoch 34/50\n",
            "196/196 [==============================] - 324s 2s/step - loss: 0.3189 - speed_output_loss: 0.1693 - hr_output_loss: 0.1496 - val_loss: 0.6404 - val_speed_output_loss: 0.1336 - val_hr_output_loss: 0.5068\n",
            "Epoch 35/50\n",
            " 90/196 [============>.................] - ETA: 2:54 - loss: 0.3184 - speed_output_loss: 0.1692 - hr_output_loss: 0.1492"
          ]
        }
      ],
      "source": [
        "batch_size = 256\n",
        "epochs = 50 \n",
        "history = model.fit(batch_generator(train_df,batch_size),\n",
        "            steps_per_epoch = len(train_df)/batch_size,\n",
        "            validation_data = batch_generator(test_df,batch_size),\n",
        "            validation_steps=1,epochs=epochs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VmGJqwSjo2ZZ"
      },
      "source": [
        "## Evaluate Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "rTFJkJcD_A8T",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "model = load_model('/content/drive/MyDrive/MiniProject/Personalized_Intelligent_Fitness_Recommender-main/workout Speed&HeartRate model/Trained_Model/2Layer_BiLSTM_r=13.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HWnQHgv0sojm",
        "outputId": "cf3f3a13-5e5b-4bb4-a760-833ae1bfb57c",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 385ms/step\n"
          ]
        }
      ],
      "source": [
        "speed,hr=model.predict(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XbLHoj5zh59H",
        "outputId": "f1b1be62-211d-4726-d46d-9656f1d08c3b",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
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          },
          "execution_count": 109,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hr=hr.reshape(-1,1)\n",
        "hr=scaler.inverse_transform(hr)\n",
        "hr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zcKa2rGsz-FY",
        "outputId": "9d92ce90-02a8-4536-d848-ad6af286052a",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['loss', 'speed_output_loss', 'hr_output_loss']"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "metrics=model.metrics_names\n",
        "metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BjZ4PSkbtfOa",
        "outputId": "e6a29163-1bfc-4291-b3b5-5111c0b77230",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-88-128d20604ad2>:2: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
            "  model.evaluate_generator(generator=batch_generator(train_df,batch_size),steps=len(train_df)/batch_size,verbose=1)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "196/196 [==============================] - 909s 5s/step - loss: 0.5497 - speed_output_loss: 0.3091 - hr_output_loss: 0.2406\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[0.5497052669525146, 0.30911096930503845, 0.24059432744979858]"
            ]
          },
          "execution_count": 88,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "batch_size = 256\n",
        "model.evaluate_generator(generator=batch_generator(test_df,batch_size),steps=len(test_df)/batch_size,verbose=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQxrshXO09Sb",
        "outputId": "9a7de4ab-d140-4d72-f769-5dc7b84aead3",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "41.27691\n"
          ]
        }
      ],
      "source": [
        "speed_loss = np.array([[0.309]])\n",
        "speed_unscaled = scaler.inverse_transform(speed_loss)\n",
        "print(speed_unscaled[0][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a0JRJ4cT21JE",
        "outputId": "1fcbf15b-5874-4ab4-9abd-49ebd31e87ef",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "32.38485662519931\n"
          ]
        }
      ],
      "source": [
        "hr_loss = np.array([[0.24059432744979858]])\n",
        "hr_unscaled = scaler.inverse_transform(hr_loss)\n",
        "print(hr_unscaled[0][0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQRwF5bkpcEw"
      },
      "source": [
        "## Save Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0VjPaGDYzsH",
        "outputId": "cb267346-35e9-454d-a8aa-eee8b9dadc1e",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/endomondoHR/Data/model/Final Model/[1115]single_user.h5\n"
          ]
        }
      ],
      "source": [
        "#save model\n",
        "dir = '/content/gdrive/My Drive/endomondoHR/Data/model/Final Model'\n",
        "path = os.path.join(dir,'[1210]bilstm.h5')\n",
        "model.save(path)\n",
        "print(path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AJP_f8aC5qtg"
      },
      "source": [
        "##Load Model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "QkpdQ5rP5tu4",
        "outputId": "d966dc9f-7598-4ecc-9d2d-a26ba4cfb6df",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "# Load trained model：\n",
        "model = load_model('/content/gdrive/My Drive/endomondoHR/Data/model/Final Model/[1210]bilstm.h5')\n",
        "plot_model(model)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "AoGeeIXyfA4O"
      ],
      "machine_shape": "hm",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}