deepLearning_LSTM.ipynb

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  "metadata": {
    "colab": {
      "name": "deepLearning_LSTM.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hfarruda/deeplearningtutorial/blob/master/deepLearning_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dTQnbI5ALRnR"
      },
      "source": [
        "#Long Short-Term Memory (LSTM)\n",
        "\n",
        "This example is part of the [*Deep Learning Tutorial*](https://github.com/hfarruda/deeplearningtutorial), authored by Henrique F. de Arruda, Alexandre Benatti, César Comin, and Luciano da Fontoura Costa. This code is not suitable for other data and/or applications, which will require modifications in the structure and parameters. This code has absolutely no warranty.\n",
        "\n",
        "If you publish a paper related on this material, please cite:\n",
        "\n",
        "H. F. de Arruda, A. Benatti, C. H. Comin, L. da F. Costa, \"Learning Deep Learning (CDT-15),\" 2019.\n",
        "\n",
        "This is the third example of deep learning implementation. Here we use a LSTM network to predict the Bitcoin prices along time by using the input as a temporal series.\n",
        "\n",
        "\n",
        "First of all, we import the necessary libraries. Here we opt for using Keras (using TensorFlow backend)."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install yfinance"
      ],
      "metadata": {
        "id": "rV_VpYzoxiji"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z_tGqT1mKu_4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f83cedc2-f626-4df5-d333-6a875972154e"
      },
      "source": [
        "%tensorflow_version 1.x\n",
        "import numpy as np\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.utils.vis_utils import plot_model\n",
        "from keras.layers import Dense, Dropout, LSTM\n",
        "from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "#import pandas_datareader\n",
        "import yfinance as yf"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TensorFlow 1.x selected.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using TensorFlow backend.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DBk2wzXpMb7i"
      },
      "source": [
        "If you have a GPU, you can use the following code to allocate processing into it.  Otherwise, proceed to (*)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBk-5FD3Mf5j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f079924d-f08a-4123-c63f-914a472e81d4"
      },
      "source": [
        "import tensorflow as tf \n",
        "from keras import backend as K\n",
        "\n",
        "print(K.tensorflow_backend._get_available_gpus())\n",
        "\n",
        "number_of_cpu_cores = 8\n",
        "config = tf.ConfigProto(device_count = {'GPU': 1 , 'CPU': number_of_cpu_cores}) \n",
        "session = tf.Session(config=config) \n",
        "keras.backend.set_session(session)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['/job:localhost/replica:0/task:0/device:GPU:0']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SkpEIvMCMhc0"
      },
      "source": [
        "(*) Here, we use the Bitcoin daily prices dataset, which is available at\n",
        "[yhaoo-stock-market](https://finance.yahoo.com/). The data contains seven columns, organized as follows: date, opening stock price, high daily price, low daily price, closing stock price, the currency volume traded on the day, and the adjusted closing price."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7XsGYIwMb_aI"
      },
      "source": [
        "train_size = 1500\n",
        "start_date = '2015-01-01'# Bitcoin started on '2010-07-16'\n",
        "end_date = '2020-04-01'\n",
        "\n",
        "\n",
        "tickerData = yf.Ticker(\"BTC-USD\")\n",
        "dataset = tickerData.history(period='max', interval='1d', start=start_date, end=end_date)\n",
        "data_oerder = ['Open','High', 'Low', 'Close', 'Volume']\n",
        "dataset = dataset[data_oerder]\n",
        "\n",
        "\n",
        "train_dataset = dataset.iloc[0:train_size, 1::].values\n",
        "test_dataset = dataset.iloc[train_size::, 1::].values\n",
        "\n",
        "min_max_scaler = MinMaxScaler(feature_range=(0,1))\n",
        "normalized_train_dataset = min_max_scaler.fit_transform(train_dataset)\n",
        "\n",
        "min_max_scaler_train = MinMaxScaler(feature_range=(0,1))\n",
        "normalized_train_price = min_max_scaler_train.fit_transform(train_dataset[:,0:1])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xndlWJv8Oi0N"
      },
      "source": [
        "In the following, we define the network topology."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "imLL0-wBOf8o"
      },
      "source": [
        "window_size = 50\n",
        "number_of_lstm_layers = 3\n",
        "activation = 'sigmoid' \n",
        "return_sequences = True\n",
        "units_first_layer = 100\n",
        "units = 50\n",
        "\n",
        "data = []\n",
        "train_price = []\n",
        "for i in range(window_size, train_size):\n",
        "    data.append(normalized_train_dataset[i-window_size:i, 0:5])\n",
        "    train_price.append(normalized_train_dataset[i, 0])\n",
        "data, train_price = np.array(data), np.array(train_price)\n",
        "\n",
        "lstm_model = Sequential()\n",
        "lstm_model.add(LSTM(units = units_first_layer, \n",
        "                    return_sequences = return_sequences, \n",
        "                    input_shape = (data.shape[1], 4)))\n",
        "lstm_model.add(Dropout(0.2))\n",
        "\n",
        "for i in range(number_of_lstm_layers-2):\n",
        "    lstm_model.add(LSTM(units = units, return_sequences = return_sequences))\n",
        "    lstm_model.add(Dropout(0.2))\n",
        "\n",
        "lstm_model.add(LSTM(units = units))\n",
        "lstm_model.add(Dropout(0.2))\n",
        "\n",
        "#Output layer\n",
        "lstm_model.add(Dense(units = 1, activation = activation))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lgj_GKsJVms-"
      },
      "source": [
        "In order to check the network topology, the subsequent command can be used."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZDE5FFHQTD2c",
        "outputId": "f87b32e1-9de9-42d6-a30f-8ebb89d00c2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "lstm_model.summary() \n",
        "#Saving the resultant figure as 'ff_model.png'.\n",
        "plot_model(lstm_model, to_file='lstm_model.png', show_shapes=True, \n",
        "           show_layer_names=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_19 (LSTM)               (None, 50, 100)           42000     \n",
            "_________________________________________________________________\n",
            "dropout_19 (Dropout)         (None, 50, 100)           0         \n",
            "_________________________________________________________________\n",
            "lstm_20 (LSTM)               (None, 50, 50)            30200     \n",
            "_________________________________________________________________\n",
            "dropout_20 (Dropout)         (None, 50, 50)            0         \n",
            "_________________________________________________________________\n",
            "lstm_21 (LSTM)               (None, 50)                20200     \n",
            "_________________________________________________________________\n",
            "dropout_21 (Dropout)         (None, 50)                0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 1)                 51        \n",
            "=================================================================\n",
            "Total params: 92,451\n",
            "Trainable params: 92,451\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wYn2dnj2TCm3"
      },
      "source": [
        "The training step is executed as follows.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i-npHi6vVdQB",
        "outputId": "e5286a2f-e0b2-4e2d-cc52-f2eb9c15f2af",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Here we set verbose as true\n",
        "verbose = 1\n",
        "\n",
        "batch_size = 32\n",
        "epochs = 10\n",
        "filepath = 'weights.h5' #name of the file with the network weights\n",
        "monitor = 'loss'\n",
        "optimizer = 'adam'\n",
        "loss = 'mean_squared_error'\n",
        "metrics = ['mean_absolute_error'] \n",
        "\n",
        "lstm_model.compile(optimizer = optimizer, loss = loss, metrics = metrics)\n",
        "\n",
        "early_stopping = EarlyStopping(monitor = monitor, min_delta = 1e-15, \n",
        "                               patience = 10, verbose = verbose)\n",
        "reduce_learning_rate_on_plateau = ReduceLROnPlateau(monitor = monitor, \n",
        "                                                    factor = 0.2, patience = 5, \n",
        "                                                    verbose = verbose)\n",
        "model_checkpoint = ModelCheckpoint(filepath = filepath, monitor = monitor, \n",
        "                      save_best_only = True, verbose = verbose)\n",
        "lstm_model.fit(data, train_price, epochs = epochs, batch_size = batch_size,\n",
        "              callbacks = [early_stopping, reduce_learning_rate_on_plateau, \n",
        "              model_checkpoint])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1450/1450 [==============================] - 6s 4ms/step - loss: 0.0567 - mean_absolute_error: 0.1899\n",
            "\n",
            "Epoch 00001: loss improved from inf to 0.05668, saving model to weights.h5\n",
            "Epoch 2/10\n",
            "1450/1450 [==============================] - 7s 4ms/step - loss: 0.0074 - mean_absolute_error: 0.0533\n",
            "\n",
            "Epoch 00002: loss improved from 0.05668 to 0.00736, saving model to weights.h5\n",
            "Epoch 3/10\n",
            "1450/1450 [==============================] - 5s 3ms/step - loss: 0.0032 - mean_absolute_error: 0.0331\n",
            "\n",
            "Epoch 00003: loss improved from 0.00736 to 0.00316, saving model to weights.h5\n",
            "Epoch 4/10\n",
            "1450/1450 [==============================] - 5s 3ms/step - loss: 0.0032 - mean_absolute_error: 0.0344\n",
            "\n",
            "Epoch 00004: loss did not improve from 0.00316\n",
            "Epoch 5/10\n",
            "1450/1450 [==============================] - 5s 3ms/step - loss: 0.0023 - mean_absolute_error: 0.0291\n",
            "\n",
            "Epoch 00005: loss improved from 0.00316 to 0.00232, saving model to weights.h5\n",
            "Epoch 6/10\n",
            "1450/1450 [==============================] - 5s 3ms/step - loss: 0.0018 - mean_absolute_error: 0.0252\n",
            "\n",
            "Epoch 00006: loss improved from 0.00232 to 0.00178, saving model to weights.h5\n",
            "Epoch 7/10\n",
            "1450/1450 [==============================] - 5s 4ms/step - loss: 0.0016 - mean_absolute_error: 0.0247\n",
            "\n",
            "Epoch 00007: loss improved from 0.00178 to 0.00164, saving model to weights.h5\n",
            "Epoch 8/10\n",
            "1450/1450 [==============================] - 5s 3ms/step - loss: 0.0015 - mean_absolute_error: 0.0242\n",
            "\n",
            "Epoch 00008: loss improved from 0.00164 to 0.00149, saving model to weights.h5\n",
            "Epoch 9/10\n",
            "1450/1450 [==============================] - 5s 3ms/step - loss: 0.0016 - mean_absolute_error: 0.0257\n",
            "\n",
            "Epoch 00009: loss did not improve from 0.00149\n",
            "Epoch 10/10\n",
            "1450/1450 [==============================] - 5s 3ms/step - loss: 0.0014 - mean_absolute_error: 0.0234\n",
            "\n",
            "Epoch 00010: loss improved from 0.00149 to 0.00143, saving model to weights.h5\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7fe34a347a10>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dvFufFaJMjXB"
      },
      "source": [
        "The following code verifies the data in the network."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0yBjjCoCjCo3"
      },
      "source": [
        "test_price = test_dataset[:, 0:1]\n",
        "complete_dataset = dataset.iloc[:,1::]\n",
        "\n",
        "train_data = complete_dataset[len(complete_dataset) - len(test_dataset) - \n",
        "                                 window_size:].values\n",
        "train_data = min_max_scaler.transform(train_data)\n",
        "\n",
        "\n",
        "X_test = []\n",
        "for i in range(window_size,len(train_data)):\n",
        "    X_test.append(train_data[i-window_size:i, 0:5])\n",
        "X_test = np.array(X_test)\n",
        "\n",
        "calculated_prices = lstm_model.predict(X_test)\n",
        "calculated_prices = min_max_scaler_train.inverse_transform(calculated_prices)\n",
        "\n",
        "train_price = min_max_scaler_train.inverse_transform([train_price])\n",
        "train_price = train_price[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IY8n4My_X4HO"
      },
      "source": [
        "In the following, we plot the train set, as well as the prediction and the expected values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXWZWul0X3hH",
        "outputId": "00aaf3a5-fad8-4981-d96b-1d05eab6fddd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot(np.linspace(0,len(train_price)-1,len(train_price)), \n",
        "         train_price, label = 'Real price train', color = 'k')\n",
        "plt.plot(np.linspace(len(train_price),len(train_price)+len(test_price)-1,\n",
        "                     len(test_price)), test_price, label = 'Real price')\n",
        "plt.plot(np.linspace(len(train_price),len(train_price)+len(test_price)-1,\n",
        "                     len(test_price)), calculated_prices, label = 'Prevision')\n",
        "plt.title('BTC prevision')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Value')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EmXldp6aSUwa"
      },
      "source": [
        "## License\n",
        "\n",
        "This Deep Learning Tutorial is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 (CC BY-NC-ND 4.0) International License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IGfUENRWu4Pm"
      },
      "source": [
        "## Acknowledgments\n",
        "Henrique F. de Arruda acknowledges FAPESP for sponsorship (grant no. 2018/10489-0). H. F. de Arruda also thanks Soremartec S.A. and Soremartec Italia, Ferrero Group, for partial financial support (from 1st July 2021). His funders had no role in study design, data collection, and analysis, decision to publish, or manuscript preparation. Alexandre Benatti thanks Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. Luciano da F. Costa thanks CNPq (grant no. 307085/2018-0) and FAPESP (proc. 15/22308-2) for sponsorship. César H. Comin thanks FAPESP (Grant Nos. 2018/09125-4 and 2021/12354-8) for financial support. This work has been supported also by FAPESP grants 11/50761-2 and 15/22308-2."
      ]
    }
  ]
}