LSTM_vehicle_trajectory_prediction.ipynb

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        "<a href=\"https://colab.research.google.com/github/KelseyWang2022/vehicle_trajectory_prediction/blob/main/LSTM_vehicle_trajectory_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "data = pd.read_csv('/content/gdrive/MyDrive/HCMC_vehicle__dataset.csv')\n",
        "# one-hot encode the class_name feature\n",
        "class_name_encoded = pd.get_dummies(data['Class_name'])\n",
        "# concatenate encoded class_name to the data\n",
        "data = pd.concat([data, class_name_encoded], axis=1)\n",
        "\n",
        "# # drop the original class_name column\n",
        "data = data.drop(['Class_name'], axis=1)\n",
        "data\n"
      ],
      "metadata": {
        "colab": {
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          "height": 461
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        "id": "MUl_mcEACkqh",
        "outputId": "3cd34c0e-f6b2-417b-967b-14cbbf799391"
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      "outputs": [
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7c8e6b3f-598f-436d-92e2-04ae0e314862 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "features = ['Bicycle', 'Bus', 'Car', 'Motorcycle', 'Truck',\n",
        "            'X_min', 'Y_min', 'X_max', 'Y_max',\n",
        "            'X_min_1', 'Y_min_1', 'X_max_1', 'Y_max_1',\n",
        "            'X_min_2', 'Y_min_2', 'X_max_2', 'Y_max_2',\n",
        "            'X_min_3', 'Y_min_3', 'X_max_3', 'Y_max_3',\n",
        "            'X_min_4', 'Y_min_4', 'X_max_4', 'Y_max_4',\n",
        "            'X_min_5', 'Y_min_5', 'X_max_5', 'Y_max_5',\n",
        "            'X_min_6', 'Y_min_6', 'X_max_6', 'Y_max_6',\n",
        "            'X_min_7', 'Y_min_7', 'X_max_7', 'Y_max_7',\n",
        "            ]\n",
        "\n",
        "target = ['X_max_8', 'X_min_8', 'Y_max_8', 'Y_min_8']\n"
      ],
      "metadata": {
        "id": "RJ5CkV5pC40q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = data[features]\n",
        "y = data[target]\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)# Split the data into training and test sets\n",
        "timesteps = X_train.shape[1]#the dimensions of feature\n",
        "X_train = X_train.values.reshape((X_train.shape[0], timesteps, 1))#X_train.shape is  (160993, 37, 1), #the input is 3D,(batch_size, samples, features), link：https://blog.csdn.net/Fwuyi/article/details/123409946\n",
        "X_test = X_test.values.reshape((X_test.shape[0], timesteps, 1))#X_test.shape is  (17889, 37, 1)\n",
        "y_train = y_train.values.reshape((y_train.shape[0], 4))#y_train.shape is  (160993, 4)\n",
        "y_test = y_test.values.reshape((y_test.shape[0], 4))#y_test.shape is  (17889, 4)\n"
      ],
      "metadata": {
        "id": "x_C7jAtPC7J3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U04qFaTODe1O",
        "outputId": "a26b6fec-0184-4e06-ef5c-c3f0288f6c4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Input, Dense, LSTM, Conv1D, MaxPooling1D, Flatten, GRU\n",
        "from keras.models import Sequential\n",
        "from keras.models import Model\n",
        "from keras.models import load_model\n",
        "from tensorflow.keras.callbacks import EarlyStopping#earlystopping is used for preventing overfitting\n",
        "import time\n",
        "\n",
        "#1.define the input shape\n",
        "start = time.time()\n",
        "input_shape = (X_train.shape[1], X_train.shape[2])#(37,1), Define the input shape, it should be 3D (samples, timesteps, features)(batch_size, timesteps, features)\n",
        "\n",
        "\n",
        "#2. Define the model\n",
        "model = Sequential()\n",
        "model.add(LSTM(50, input_shape=input_shape))\n",
        "model.add(Dense(4)) # 2 output units for X_8, Y_8\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "\n",
        "#3. train the model\n",
        "model.fit(X_train, y_train, epochs=1, batch_size=32, validation_data=(X_test, y_test), callbacks=EarlyStopping(monitor='val_loss', patience=2))#early stopping is used to solve the problem of overfitting\n",
        "print('Training time: ', time.time() - start)\n",
        "\n",
        "#4.evaluate the model\n",
        "score = model.evaluate(X_test, y_test, batch_size=32)\n",
        "print(\"MSE: %.2f\" % score)\n",
        "\n",
        "#5. save the definition and weights of the model\n",
        "model.save('LSTM.h5')#save the model definition and weights,so you can load this model by using lstm.h5,the structure of Transformer also defined in this file\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "zhJDaV0SDExw",
        "outputId": "81953ade-5c59-4e28-d916-2d47a8e81b95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5032/5032 [==============================] - 105s 21ms/step - loss: 46022.7734 - val_loss: 21340.9961\n",
            "Training time:  105.89327120780945\n",
            "560/560 [==============================] - 3s 6ms/step - loss: 21340.9961\n",
            "MSE: 21341.00\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-bd44cb6668d5>\u001b[0m in \u001b[0;36m<cell line: 31>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;31m#6.load this model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m \u001b[0mLSTM\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/LSTM.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'keras' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.metrics import mean_absolute_percentage_error, mean_absolute_error\n",
        "from sklearn.metrics import mean_squared_error, r2_score#load the evaluation metrics\n",
        "#6.load this model\n",
        "LSTM = load_model('/content/LSTM.h5')#by using kreas, model.save is using to save the definition and weights of NN, and load_model is using to load model.\n",
        "\n",
        "#7. predict the data\n",
        "prediction = LSTM.predict(X_test)# .predict is using for prediction\n",
        "prediction = pd.DataFrame(prediction)\n",
        "prediction.to_csv('prediction.csv')\n",
        "print(mean_absolute_error(y_test, prediction))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L2QDs_-zFomt",
        "outputId": "18b30db2-3950-4a46-daec-c8679aa718d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "560/560 [==============================] - 3s 5ms/step\n",
            "106.13303298370177\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# New section"
      ],
      "metadata": {
        "id": "gHgYKZ4nV5vh"
      }
    }
  ]
}