price_model_with_Pcent_Input.ipynb

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    "colab": {
      "name": "price model with Pcent Input",
      "provenance": [],
      "collapsed_sections": [
        "WYalG82ITOko"
      ],
      "toc_visible": true,
      "authorship_tag": "ABX9TyPUrgjl0c7WRn47Ua/XC11h",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/YanzhaoZ/PreBit/blob/main/price_model_with_Pcent_Input.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xCu4r2213B7q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed61f64d-70ed-4c0c-dbe6-633514cfa772"
      },
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "872565a1-2b70-4b1e-9874-443a5bce687c"
      },
      "source": [
        "!pip install yfinance\n",
        "import yfinance as yf\n",
        "import pandas as pd\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting yfinance\n",
            "  Downloading https://files.pythonhosted.org/packages/7a/e8/b9d7104d3a4bf39924799067592d9e59119fcfc900a425a12e80a3123ec8/yfinance-0.1.55.tar.gz\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.6/dist-packages (from yfinance) (1.1.4)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from yfinance) (1.18.5)\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from yfinance) (2.23.0)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from yfinance) (0.0.9)\n",
            "Collecting lxml>=4.5.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/64/28/0b761b64ecbd63d272ed0e7a6ae6e4402fc37886b59181bfdf274424d693/lxml-4.6.1-cp36-cp36m-manylinux1_x86_64.whl (5.5MB)\n",
            "\u001b[K     |████████████████████████████████| 5.5MB 10.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24->yfinance) (2.8.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (2020.11.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (2.10)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance) (1.15.0)\n",
            "Building wheels for collected packages: yfinance\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.55-py2.py3-none-any.whl size=22618 sha256=ebe676869ba1bb44ca6ba08264ef14a55b0d1736acba485e94a407ab53fa7543\n",
            "  Stored in directory: /root/.cache/pip/wheels/04/98/cc/2702a4242d60bdc14f48b4557c427ded1fe92aedf257d4565c\n",
            "Successfully built yfinance\n",
            "Installing collected packages: lxml, yfinance\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed lxml-4.6.1 yfinance-0.1.55\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Xma4YZj3tvk"
      },
      "source": [
        "import torch\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.preprocessing import MinMaxScaler\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bDVM1wj74Fp5"
      },
      "source": [
        "# Loading the Price Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3c3XaB84IjI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d06913d-4f7a-44d8-e7df-de4c621f89b9"
      },
      "source": [
        "start_date ='2015-01-01'\n",
        "end_date = '2019-12-31'\n",
        "price = yf.download(\"BTC-USD\", start=start_date, end=end_date)\n",
        "\n",
        "#relative_price, will calculate the change in % compared to the day before. \n",
        "relative_price = (price/price.shift(1)-1)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LT3XXVz64TjA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "ae82db72-c044-4123-9bbd-446a72fe9dc3"
      },
      "source": [
        "relative_price.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
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              "      <th>2015-01-01</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2015-01-02</th>\n",
              "      <td>-0.019835</td>\n",
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              "      <td>0.002442</td>\n",
              "      <td>-0.002181</td>\n",
              "      <td>-0.103593</td>\n",
              "      <td>-0.107767</td>\n",
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              "    <tr>\n",
              "      <th>2015-01-04</th>\n",
              "      <td>-0.107036</td>\n",
              "      <td>-0.088593</td>\n",
              "      <td>-0.083499</td>\n",
              "      <td>-0.060079</td>\n",
              "      <td>-0.060079</td>\n",
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              "    <tr>\n",
              "      <th>2015-01-05</th>\n",
              "      <td>-0.057130</td>\n",
              "      <td>-0.030947</td>\n",
              "      <td>0.029005</td>\n",
              "      <td>0.038907</td>\n",
              "      <td>0.038907</td>\n",
              "      <td>-0.209716</td>\n",
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            ],
            "text/plain": [
              "                Open      High       Low     Close  Adj Close    Volume\n",
              "Date                                                                   \n",
              "2015-01-01       NaN       NaN       NaN       NaN        NaN       NaN\n",
              "2015-01-02 -0.019835 -0.014343 -0.001395  0.002492   0.002492 -0.021888\n",
              "2015-01-03  0.002442 -0.002181 -0.103593 -0.107767  -0.107767  3.205047\n",
              "2015-01-04 -0.107036 -0.088593 -0.083499 -0.060079  -0.060079  0.682956\n",
              "2015-01-05 -0.057130 -0.030947  0.029005  0.038907   0.038907 -0.209716"
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          "metadata": {
            "tags": []
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          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HVFcYkrn8V6J"
      },
      "source": [
        "#add in the label\n",
        "#I can start with the % change of the daily closing price, then create labels.\n",
        "#another way could be to look at the next day High vs today Close.  \n",
        "\n",
        "#setting price change threshold\n",
        "Threshold = 0\n",
        "\n",
        "price_2= price.Close.to_frame()\n",
        "price['change']=price_2.apply(lambda x: x.shift(-1)/x -1)\n",
        "price['change_label']=price['change'].apply (lambda x: x> Treshold )\n",
        "#NaN > 0 gives false? Perhaps I could drop na before this step? \n",
        "\n",
        "\n",
        "#copy the label to relative_price part as well. \n",
        "relative_price['change'] = price['change']\n",
        "relative_price['change_label']=price['change_label']"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DsjYzgecBezO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "794d59ad-9d2a-4e9c-9522-fc035b34e21d"
      },
      "source": [
        "print (len(price),len(relative_price))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1826 1826\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JUHGLoLbAaRo"
      },
      "source": [
        "price = price.dropna()\n",
        "relative_price = relative_price.dropna()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LIfAXYnnAfNC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9c43706-bd20-481b-d04f-d67c4d57d026"
      },
      "source": [
        "#price would lose the last day, because no information on next day price to create the label\n",
        "#relative price, would ALSO lose the first day, because no information to calculate the relative changes .\n",
        "print (len(price),len(relative_price))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1825 1824\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "liUvoa7Gis8a",
        "outputId": "d47a813d-2a73-4f8e-99a3-cd00492052d2"
      },
      "source": [
        "relative_price['change_label'].value_counts()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     1000\n",
              "False     824\n",
              "Name: change_label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WYalG82ITOko"
      },
      "source": [
        "# Adding in technical indicators"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WDgrd_QYTfDb"
      },
      "source": [
        "def get_technical_indicators(dataset):\n",
        "    # Create 7 and 21 days Moving Average\n",
        "    dataset['ma7'] = dataset['Close'].rolling(window=7).mean()\n",
        "    dataset['ma21'] = dataset['Close'].rolling(window=21).mean()\n",
        "    \n",
        "    # Create MACD\n",
        "    #dataset['26ema'] = pd.ewma(dataset['Close'], span=26)\n",
        "    dataset['26ema'] = dataset['Close'].ewm(span=26).mean()\n",
        "    #dataset['12ema'] = pd.ewma(dataset['Close'], span=12)\n",
        "    dataset['12ema'] = dataset['Close'].ewm(span=12).mean()\n",
        "    dataset['MACD'] = (dataset['12ema']-dataset['26ema'])\n",
        "\n",
        "    # Create Bollinger Bands\n",
        "    #dataset['20sd'] = pd.stats.moments.rolling_std(dataset['Close'],20)\n",
        "    dataset['20sd'] = dataset[\"Close\"].rolling(window=20).std()\n",
        "    dataset['upper_band'] = dataset['ma21'] + (dataset['20sd']*2)\n",
        "    dataset['lower_band'] = dataset['ma21'] - (dataset['20sd']*2)\n",
        "    \n",
        "    # Create Exponential moving average\n",
        "    dataset['ema'] = dataset['Close'].ewm(com=0.5).mean()\n",
        "    \n",
        "    # Create Momentum\n",
        "    #dataset['momentum'] = dataset['Close']-1\n",
        "    \n",
        "    return dataset"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IScz7sVyTesK"
      },
      "source": [
        "price_processed = get_technical_indicators(price)\n",
        "#price.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "dn7FwTfraQGt",
        "outputId": "edb3f963-dd95-49f3-f741-d12c04f7fec5"
      },
      "source": [
        "price_processed.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>2019-12-27</th>\n",
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              "    <tr>\n",
              "      <th>2019-12-28</th>\n",
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              "      <td>7286.905273</td>\n",
              "      <td>7317.990234</td>\n",
              "      <td>7317.990234</td>\n",
              "      <td>21365673026</td>\n",
              "      <td>0.014302</td>\n",
              "      <td>True</td>\n",
              "      <td>7330.278669</td>\n",
              "      <td>7239.293062</td>\n",
              "      <td>7351.095662</td>\n",
              "      <td>7274.769698</td>\n",
              "      <td>-76.325964</td>\n",
              "      <td>178.778125</td>\n",
              "      <td>7596.849312</td>\n",
              "      <td>6881.736812</td>\n",
              "      <td>7305.140042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-29</th>\n",
              "      <td>7317.647461</td>\n",
              "      <td>7513.948242</td>\n",
              "      <td>7279.865234</td>\n",
              "      <td>7422.652832</td>\n",
              "      <td>7422.652832</td>\n",
              "      <td>22445257701</td>\n",
              "      <td>-0.017468</td>\n",
              "      <td>False</td>\n",
              "      <td>7317.573521</td>\n",
              "      <td>7232.545805</td>\n",
              "      <td>7356.396193</td>\n",
              "      <td>7297.520949</td>\n",
              "      <td>-58.875243</td>\n",
              "      <td>179.979293</td>\n",
              "      <td>7592.504391</td>\n",
              "      <td>6872.587219</td>\n",
              "      <td>7383.481902</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Open         High  ...   lower_band          ema\n",
              "Date                                  ...                          \n",
              "2019-12-25  7325.755859  7357.020020  ...  6839.420977  7296.493060\n",
              "2019-12-26  7274.799316  7388.302734  ...  6850.398948  7258.142218\n",
              "2019-12-27  7238.141113  7363.529297  ...  6863.554708  7279.439659\n",
              "2019-12-28  7289.031250  7399.041016  ...  6881.736812  7305.140042\n",
              "2019-12-29  7317.647461  7513.948242  ...  6872.587219  7383.481902\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W9O4ZSa0djOy",
        "outputId": "b94b977b-e0a1-4cfa-8fcc-829c0bd5a8a3"
      },
      "source": [
        "price_processed = price_processed.dropna()\n",
        "price_processed.isnull().value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Open   High   Low    Close  Adj Close  Volume  change  change_label  ma7    ma21   26ema  12ema  MACD   20sd   upper_band  lower_band  ema  \n",
              "False  False  False  False  False      False   False   False         False  False  False  False  False  False  False       False       False    1784\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "ytSx4T9xkIRU",
        "outputId": "3234c0fc-393e-4763-bc89-f2c14f85a1a6"
      },
      "source": [
        "price_processed.head()\n",
        "#although I'm not very sure about how 26ema can be calculated when I don't have 26 days of data before this day ? \n",
        "#and ema, with this particular set up, seems a bit off? : https://stackoverflow.com/questions/37924377/does-pandas-calculate-ewm-wrong\n",
        "#the link above provides an alternate view, but I think with the amount of days I have, it shouldn't really matter that much. \n",
        "#Let's keep going w/o it first. "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>change</th>\n",
              "      <th>change_label</th>\n",
              "      <th>ma7</th>\n",
              "      <th>ma21</th>\n",
              "      <th>26ema</th>\n",
              "      <th>12ema</th>\n",
              "      <th>MACD</th>\n",
              "      <th>20sd</th>\n",
              "      <th>upper_band</th>\n",
              "      <th>lower_band</th>\n",
              "      <th>ema</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-02-10</th>\n",
              "      <td>220.281998</td>\n",
              "      <td>221.807007</td>\n",
              "      <td>215.332001</td>\n",
              "      <td>219.839005</td>\n",
              "      <td>219.839005</td>\n",
              "      <td>21115100</td>\n",
              "      <td>-0.002975</td>\n",
              "      <td>False</td>\n",
              "      <td>222.477857</td>\n",
              "      <td>232.991715</td>\n",
              "      <td>229.198966</td>\n",
              "      <td>225.709504</td>\n",
              "      <td>-3.489462</td>\n",
              "      <td>15.323515</td>\n",
              "      <td>263.638745</td>\n",
              "      <td>202.344686</td>\n",
              "      <td>220.382635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-02-11</th>\n",
              "      <td>219.731995</td>\n",
              "      <td>223.406006</td>\n",
              "      <td>218.074005</td>\n",
              "      <td>219.184998</td>\n",
              "      <td>219.184998</td>\n",
              "      <td>17201900</td>\n",
              "      <td>0.011766</td>\n",
              "      <td>True</td>\n",
              "      <td>221.382429</td>\n",
              "      <td>232.624477</td>\n",
              "      <td>228.289994</td>\n",
              "      <td>224.679632</td>\n",
              "      <td>-3.610362</td>\n",
              "      <td>15.644742</td>\n",
              "      <td>263.913961</td>\n",
              "      <td>201.334992</td>\n",
              "      <td>219.584210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-02-12</th>\n",
              "      <td>219.207993</td>\n",
              "      <td>222.199005</td>\n",
              "      <td>217.613998</td>\n",
              "      <td>221.764008</td>\n",
              "      <td>221.764008</td>\n",
              "      <td>15206200</td>\n",
              "      <td>0.061611</td>\n",
              "      <td>True</td>\n",
              "      <td>222.047145</td>\n",
              "      <td>232.070096</td>\n",
              "      <td>227.707355</td>\n",
              "      <td>224.221244</td>\n",
              "      <td>-3.486111</td>\n",
              "      <td>15.830085</td>\n",
              "      <td>263.730267</td>\n",
              "      <td>200.409925</td>\n",
              "      <td>221.037408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-02-13</th>\n",
              "      <td>221.968994</td>\n",
              "      <td>240.259003</td>\n",
              "      <td>221.261993</td>\n",
              "      <td>235.427002</td>\n",
              "      <td>235.427002</td>\n",
              "      <td>42744400</td>\n",
              "      <td>0.092997</td>\n",
              "      <td>True</td>\n",
              "      <td>223.927288</td>\n",
              "      <td>232.191430</td>\n",
              "      <td>228.386241</td>\n",
              "      <td>225.977068</td>\n",
              "      <td>-2.409173</td>\n",
              "      <td>15.415100</td>\n",
              "      <td>263.021630</td>\n",
              "      <td>201.361229</td>\n",
              "      <td>230.630471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-02-14</th>\n",
              "      <td>235.528000</td>\n",
              "      <td>259.808014</td>\n",
              "      <td>235.528000</td>\n",
              "      <td>257.321014</td>\n",
              "      <td>257.321014</td>\n",
              "      <td>49732500</td>\n",
              "      <td>-0.087424</td>\n",
              "      <td>False</td>\n",
              "      <td>228.151147</td>\n",
              "      <td>232.642573</td>\n",
              "      <td>230.896032</td>\n",
              "      <td>230.874410</td>\n",
              "      <td>-0.021622</td>\n",
              "      <td>15.707818</td>\n",
              "      <td>264.058209</td>\n",
              "      <td>201.226937</td>\n",
              "      <td>248.424167</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High  ...  lower_band         ema\n",
              "Date                                ...                        \n",
              "2015-02-10  220.281998  221.807007  ...  202.344686  220.382635\n",
              "2015-02-11  219.731995  223.406006  ...  201.334992  219.584210\n",
              "2015-02-12  219.207993  222.199005  ...  200.409925  221.037408\n",
              "2015-02-13  221.968994  240.259003  ...  201.361229  230.630471\n",
              "2015-02-14  235.528000  259.808014  ...  201.226937  248.424167\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bqV8t9jfaX2Q"
      },
      "source": [
        "#visualise the chart\n",
        "def plot_technical_indicators(dataset, last_days):\n",
        "    plt.figure(figsize=(16, 10), dpi=100)\n",
        "    shape_0 = dataset.shape[0]\n",
        "    xmacd_ = shape_0-last_days\n",
        "    \n",
        "    dataset = dataset.iloc[-last_days:, :]\n",
        "    x_ = range(3, dataset.shape[0])\n",
        "    x_ =list(dataset.index)\n",
        "    \n",
        "    # Plot first subplot\n",
        "    plt.subplot(2, 1, 1)\n",
        "    plt.plot(dataset['ma7'],label='MA 7', color='g',linestyle='--')\n",
        "    plt.plot(dataset['Close'],label='Closing Price', color='b')\n",
        "    plt.plot(dataset['ma21'],label='MA 21', color='r',linestyle='--')\n",
        "    plt.plot(dataset['upper_band'],label='Upper Band', color='c')\n",
        "    plt.plot(dataset['lower_band'],label='Lower Band', color='c')\n",
        "    plt.fill_between(x_, dataset['lower_band'], dataset['upper_band'], alpha=0.35)\n",
        "    plt.title('Technical indicators for Goldman Sachs - last {} days.'.format(last_days))\n",
        "    plt.ylabel('USD')\n",
        "    plt.legend()\n",
        "\n",
        "    # Plot second subplot\n",
        "    plt.subplot(2, 1, 2)\n",
        "    plt.title('MACD')\n",
        "    plt.plot(dataset['MACD'],label='MACD', linestyle='-.')\n",
        "    # plt.hlines(15, xmacd_, shape_0, colors='g', linestyles='--')\n",
        "    # plt.hlines(-15, xmacd_, shape_0, colors='g', linestyles='--')\n",
        "    #plt.plot(dataset['log_momentum'],label='Momentum', color='b',linestyle='-')\n",
        "\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 838
        },
        "id": "WfwKoSota1bR",
        "outputId": "df34a922-5f2e-4fc8-d313-c8e935dc7ce4"
      },
      "source": [
        "plot_technical_indicators(price_processed, 400)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1600x1000 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "sPy9BNTbpP6X",
        "outputId": "6ca9a95b-8f58-4bef-e182-cc9670aa1b76"
      },
      "source": [
        "price_processed.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>change</th>\n",
              "      <th>change_label</th>\n",
              "      <th>ma7</th>\n",
              "      <th>ma21</th>\n",
              "      <th>26ema</th>\n",
              "      <th>12ema</th>\n",
              "      <th>MACD</th>\n",
              "      <th>20sd</th>\n",
              "      <th>upper_band</th>\n",
              "      <th>lower_band</th>\n",
              "      <th>ema</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-02-10</th>\n",
              "      <td>220.281998</td>\n",
              "      <td>221.807007</td>\n",
              "      <td>215.332001</td>\n",
              "      <td>219.839005</td>\n",
              "      <td>219.839005</td>\n",
              "      <td>21115100</td>\n",
              "      <td>-0.002975</td>\n",
              "      <td>False</td>\n",
              "      <td>222.477857</td>\n",
              "      <td>232.991715</td>\n",
              "      <td>229.198966</td>\n",
              "      <td>225.709504</td>\n",
              "      <td>-3.489462</td>\n",
              "      <td>15.323515</td>\n",
              "      <td>263.638745</td>\n",
              "      <td>202.344686</td>\n",
              "      <td>220.382635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-02-11</th>\n",
              "      <td>219.731995</td>\n",
              "      <td>223.406006</td>\n",
              "      <td>218.074005</td>\n",
              "      <td>219.184998</td>\n",
              "      <td>219.184998</td>\n",
              "      <td>17201900</td>\n",
              "      <td>0.011766</td>\n",
              "      <td>True</td>\n",
              "      <td>221.382429</td>\n",
              "      <td>232.624477</td>\n",
              "      <td>228.289994</td>\n",
              "      <td>224.679632</td>\n",
              "      <td>-3.610362</td>\n",
              "      <td>15.644742</td>\n",
              "      <td>263.913961</td>\n",
              "      <td>201.334992</td>\n",
              "      <td>219.584210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-02-12</th>\n",
              "      <td>219.207993</td>\n",
              "      <td>222.199005</td>\n",
              "      <td>217.613998</td>\n",
              "      <td>221.764008</td>\n",
              "      <td>221.764008</td>\n",
              "      <td>15206200</td>\n",
              "      <td>0.061611</td>\n",
              "      <td>True</td>\n",
              "      <td>222.047145</td>\n",
              "      <td>232.070096</td>\n",
              "      <td>227.707355</td>\n",
              "      <td>224.221244</td>\n",
              "      <td>-3.486111</td>\n",
              "      <td>15.830085</td>\n",
              "      <td>263.730267</td>\n",
              "      <td>200.409925</td>\n",
              "      <td>221.037408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-02-13</th>\n",
              "      <td>221.968994</td>\n",
              "      <td>240.259003</td>\n",
              "      <td>221.261993</td>\n",
              "      <td>235.427002</td>\n",
              "      <td>235.427002</td>\n",
              "      <td>42744400</td>\n",
              "      <td>0.092997</td>\n",
              "      <td>True</td>\n",
              "      <td>223.927288</td>\n",
              "      <td>232.191430</td>\n",
              "      <td>228.386241</td>\n",
              "      <td>225.977068</td>\n",
              "      <td>-2.409173</td>\n",
              "      <td>15.415100</td>\n",
              "      <td>263.021630</td>\n",
              "      <td>201.361229</td>\n",
              "      <td>230.630471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-02-14</th>\n",
              "      <td>235.528000</td>\n",
              "      <td>259.808014</td>\n",
              "      <td>235.528000</td>\n",
              "      <td>257.321014</td>\n",
              "      <td>257.321014</td>\n",
              "      <td>49732500</td>\n",
              "      <td>-0.087424</td>\n",
              "      <td>False</td>\n",
              "      <td>228.151147</td>\n",
              "      <td>232.642573</td>\n",
              "      <td>230.896032</td>\n",
              "      <td>230.874410</td>\n",
              "      <td>-0.021622</td>\n",
              "      <td>15.707818</td>\n",
              "      <td>264.058209</td>\n",
              "      <td>201.226937</td>\n",
              "      <td>248.424167</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High  ...  lower_band         ema\n",
              "Date                                ...                        \n",
              "2015-02-10  220.281998  221.807007  ...  202.344686  220.382635\n",
              "2015-02-11  219.731995  223.406006  ...  201.334992  219.584210\n",
              "2015-02-12  219.207993  222.199005  ...  200.409925  221.037408\n",
              "2015-02-13  221.968994  240.259003  ...  201.361229  230.630471\n",
              "2015-02-14  235.528000  259.808014  ...  201.226937  248.424167\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UZb0Sy17pQ6i"
      },
      "source": [
        "# Format data for model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2zSOEWwCmOa"
      },
      "source": [
        "#now perhaps convert them to a list, and then tensor, to proceed to process the price data? \n",
        "\n",
        "x_input=relative_price.drop(['change','change_label'],axis=1).values\n",
        "\n",
        "# scaler = MinMaxScaler()\n",
        "# x_input= scaler.fit_transform(x_input)\n",
        "x_input=x_input.tolist()\n",
        "\n",
        "x_label=relative_price['change_label'].values\n",
        "x_label=x_label.reshape(-1,).tolist()\n",
        "#try to encode label \n",
        "x_label = [1 if x else 0 for x in x_label]\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eEHBjRo3DKK9",
        "outputId": "d0d0ebeb-80ee-4b5b-e2eb-2c60a0d72861"
      },
      "source": [
        "x_input[0]"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[-0.019835497362194032,\n",
              " -0.01434302849500968,\n",
              " -0.0013948538523216314,\n",
              " 0.0024917184690256278,\n",
              " 0.0024917184690256278,\n",
              " -0.021887501477624083]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGtR58hFcP9X"
      },
      "source": [
        "#roe relative price \n",
        "# x_input = relative_price.drop(['change','change_label'],axis = 1).values\n",
        "\n",
        "\n",
        "# # x_input= min_max_scaler.fit_transform(x_input)\n",
        "# x_input=x_input.tolist()\n",
        "\n",
        "# x_label=relative_price['change_label'].values\n",
        "# x_label=x_label.reshape(-1,).tolist()\n",
        "# #try to encode label \n",
        "# x_label = [1 if x else 0 for x in x_label]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2bpmrO8vI0Cy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ab1b322-db25-4897-b5b6-0e9fac941b77"
      },
      "source": [
        "x_input[0],x_label[0]"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([-0.019835497362194032,\n",
              "  -0.01434302849500968,\n",
              "  -0.0013948538523216314,\n",
              "  0.0024917184690256278,\n",
              "  0.0024917184690256278,\n",
              "  -0.021887501477624083],\n",
              " 0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWjvf91FJDYV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27f2d481-5e3a-477d-806c-e505e4d4f613"
      },
      "source": [
        "def process(data, window_size):\n",
        "    output = []\n",
        "    for i in range(window_size-1, len(data)):\n",
        "        raw_data = data[i-window_size+1:i+1]\n",
        "        output.append(raw_data)\n",
        "    return output\n",
        "\n",
        "window_size = 10\n",
        "x_processed = process(x_input, 10)\n",
        "split = round(len(x_processed)*0.7)\n",
        "x_train = x_processed[:split]\n",
        "x_eval = x_processed[split:]\n",
        "x_train = torch.tensor(x_train, dtype=torch.float)\n",
        "x_lab = torch.tensor(x_label[window_size-1:window_size-1+split],dtype=torch.float)\n",
        "\n",
        "\n",
        "x_eval = torch.tensor(x_eval, dtype=torch.float)\n",
        "x_lab_eval = torch.tensor(x_label[window_size-1+split:],dtype=torch.float)\n",
        "\n",
        "print(x_train.size())\n",
        "print(x_lab.size())\n",
        "print(x_train[0])\n",
        "print(x_label[0])\n",
        "print(x_train.type())\n",
        "print(x_lab.type())\n",
        "print(x_eval.size())\n",
        "print(x_lab_eval.size())"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([1270, 10, 6])\n",
            "torch.Size([1270])\n",
            "tensor([[-1.9835e-02, -1.4343e-02, -1.3949e-03,  2.4917e-03,  2.4917e-03,\n",
            "         -2.1888e-02],\n",
            "        [ 2.4421e-03, -2.1815e-03, -1.0359e-01, -1.0777e-01, -1.0777e-01,\n",
            "          3.2050e+00],\n",
            "        [-1.0704e-01, -8.8593e-02, -8.3499e-02, -6.0079e-02, -6.0079e-02,\n",
            "          6.8296e-01],\n",
            "        [-5.7130e-02, -3.0947e-02,  2.9005e-02,  3.8907e-02,  3.8907e-02,\n",
            "         -2.0972e-01],\n",
            "        [ 3.5939e-02,  3.3096e-02,  2.8715e-02,  4.2682e-02,  4.2682e-02,\n",
            "         -4.7124e-01],\n",
            "        [ 4.1754e-02,  3.8953e-02,  3.8075e-02,  2.8471e-02,  2.8471e-02,\n",
            "          6.9738e-02],\n",
            "        [ 2.8167e-02, -1.5461e-02, -3.1935e-03, -3.7331e-02, -3.7331e-02,\n",
            "         -1.9642e-01],\n",
            "        [-3.9954e-02, -1.0271e-02, -5.8191e-03,  2.4913e-02,  2.4913e-02,\n",
            "         -6.3250e-02],\n",
            "        [ 1.7423e-02, -1.0261e-02, -2.3409e-02, -5.3759e-02, -5.3759e-02,\n",
            "         -1.8454e-01],\n",
            "        [-4.4187e-02, -2.9463e-02, -3.2584e-02, -3.3246e-02, -3.3246e-02,\n",
            "          1.9238e-01]])\n",
            "0\n",
            "torch.FloatTensor\n",
            "torch.FloatTensor\n",
            "torch.Size([545, 10, 6])\n",
            "torch.Size([545])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xAFTd8rWLKHt"
      },
      "source": [
        "# Prepare the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpREgH9sLMXl"
      },
      "source": [
        "train_data = x_train\n",
        "train_label = x_lab\n",
        "\n",
        "eval_data = x_eval\n",
        "#print(test_data.size())\n",
        "eval_label = x_lab_eval\n",
        "#print(test_label)\n",
        "\n",
        "# batching  data\n",
        "batch_size = 64\n",
        "train_data = TensorDataset(train_data, train_label)\n",
        "train_loader = DataLoader(train_data, batch_size)\n",
        "train_batches = [batch for batch in train_loader]\n",
        "\n",
        "eval_data = TensorDataset(eval_data, eval_label)\n",
        "eval_loader = DataLoader(eval_data, batch_size) \n",
        "eval_batches = [batch for batch in eval_loader]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PSfqkcKTMgky"
      },
      "source": [
        "class OurModel(nn.Module):\n",
        "    def __init__(self, input_size,  hidden_size, num_layers):\n",
        "        super(OurModel, self).__init__()\n",
        "        num_layers = num_layers\n",
        "        self.input_size = input_size\n",
        "        self.hidden_size = hidden_size\n",
        "        self.lstm = nn.LSTM(self.input_size, self.hidden_size, num_layers, batch_first=True, bidirectional=False, dropout=0.5)\n",
        "        self.ff = nn.Linear(self.hidden_size, 1)\n",
        "        self.dropout = nn.Dropout()\n",
        "\n",
        "        #need to change the loss function. \n",
        "    def forward(self, input):\n",
        "        output, (hn, cn) = self.lstm(input)\n",
        "        hn = self.dropout(hn[-1,:,:])\n",
        "        percent_change = self.ff(hn)\n",
        "        return percent_change\n",
        "\n",
        "\n",
        "        "
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J2JZAdtvR69O"
      },
      "source": [
        "#define model,\n",
        "\n",
        "model = OurModel(6, 512, 2)\n",
        "\n",
        "# for i in range(30):\n",
        "#     for step, batch in enumerate(train_batches):\n",
        "#         optimizer.step()\n",
        "#         model.zero_grad()\n",
        "        \n",
        "#         data, label = tuple(t for t in batch)\n",
        "#         model.train()\n",
        "#         loss = model(data, label)\n",
        "#         loss.backward()\n",
        "\n",
        "#     model.eval()\n",
        "#     output = evaluation(test_loader, model)\n",
        "#     print('prediction:', output)\n",
        "#     print(loss)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GRitBVF2tCVD"
      },
      "source": [
        "#define optimizer, loss function\n",
        "params = model.parameters()\n",
        "optimizer = optim.Adam(params)\n",
        "criterion = nn.BCEWithLogitsLoss()"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HzMyaJ0bsksz",
        "outputId": "3edb1214-cd59-4ae5-8afa-c740a8cd979f"
      },
      "source": [
        "#Move model to device\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model = model.to(device)\n",
        "criterion = criterion.to(device)\n",
        "\n",
        "print (device)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6LQy1RUJr4Lm",
        "outputId": "06707425-463d-425c-c44a-3d37425a0f56"
      },
      "source": [
        "#check number of parameters. \n",
        "def count_parameters(model):\n",
        "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "\n",
        "print(f'The model has {count_parameters(model):,} trainable parameters')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The model has 3,166,721 trainable parameters\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cNsy3zuvSFqo"
      },
      "source": [
        "def binary_accuracy(preds, y):\n",
        "    \"\"\"\n",
        "    Returns accuracy per batch, i.e. if you get 8/10 right, this returns 0.8, NOT 8\n",
        "    \"\"\"\n",
        "\n",
        "    #round predictions to the closest integer\n",
        "    rounded_preds = torch.round(torch.sigmoid(preds))\n",
        "    correct = (rounded_preds == y).float() #convert into float for division \n",
        "    acc = correct.sum() / len(correct)\n",
        "    return acc"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zi0CHIDNT-X3"
      },
      "source": [
        "#here, let's try to define a function to get the confusion matrix stats\n",
        "\n"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ps2oeWfXmVF4"
      },
      "source": [
        "def train(model, train_batches, optimizer, criterion):\n",
        "    \n",
        "    epoch_loss = 0\n",
        "    epoch_acc = 0\n",
        "    \n",
        "    model.train()\n",
        "\n",
        "    for step, batch in enumerate(train_batches):\n",
        "        \n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        data, label = tuple(t for t in batch)\n",
        "\n",
        "        data = data.to(device)\n",
        "        label = label.to(device)\n",
        "\n",
        "        predictions = model(data).squeeze(1)\n",
        "\n",
        "        loss = criterion(predictions, label)\n",
        "        \n",
        "        acc = binary_accuracy(predictions, label)\n",
        "        \n",
        "        loss.backward()\n",
        "        \n",
        "        optimizer.step()\n",
        "        \n",
        "        epoch_loss += loss.item()\n",
        "        epoch_acc += acc.item()\n",
        "        \n",
        "    return epoch_loss / len(train_batches), epoch_acc / len(train_batches)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-5ik76v7st1"
      },
      "source": [
        "def evaluate(model, eval_batches, criterion):\n",
        "    \n",
        "    epoch_loss = 0\n",
        "    epoch_acc = 0\n",
        "    \n",
        "    model.eval()\n",
        "    \n",
        "    with torch.no_grad():\n",
        "    \n",
        "        for step, batch in enumerate(eval_batches):\n",
        "\n",
        "            data, label = tuple(t for t in batch)\n",
        "            data = data.to(device)\n",
        "            label = label.to(device)\n",
        "            \n",
        "            predictions = model(data).squeeze(1)\n",
        "            \n",
        "            loss = criterion(predictions, label)\n",
        "            \n",
        "            acc = binary_accuracy(predictions, label)\n",
        "\n",
        "            epoch_loss += loss.item()\n",
        "            epoch_acc += acc.item()\n",
        "        \n",
        "    return epoch_loss / len(eval_batches), epoch_acc / len(eval_batches)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OjDUGdyMmVvi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ff67a2a-249f-4a2b-d59c-06e87824ad1d"
      },
      "source": [
        "import time\n",
        "\n",
        "def epoch_time(start_time, end_time):\n",
        "    elapsed_time = end_time - start_time\n",
        "    elapsed_mins = int(elapsed_time / 60)\n",
        "    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n",
        "    return elapsed_mins, elapsed_secs\n",
        "\n",
        "N_EPOCHS = 10\n",
        "\n",
        "best_valid_loss = float('inf')\n",
        "\n",
        "for epoch in range(N_EPOCHS):\n",
        "\n",
        "    start_time = time.time()\n",
        "    \n",
        "    train_loss, train_acc = train(model, train_batches, optimizer, criterion)\n",
        "    valid_loss, valid_acc = evaluate(model, eval_batches, criterion)\n",
        "    \n",
        "    end_time = time.time()\n",
        "\n",
        "    epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
        "    \n",
        "    # if valid_loss < best_valid_loss:\n",
        "    #     best_valid_loss = valid_loss\n",
        "    #     torch.save(model.state_dict(), 'tut2-model.pt')\n",
        "    \n",
        "    print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n",
        "    print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')\n",
        "    print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc*100:.2f}%')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 01 | Epoch Time: 0m 0s\n",
            "\tTrain Loss: 0.688 | Train Acc: 54.62%\n",
            "\t Val. Loss: 0.695 |  Val. Acc: 51.07%\n",
            "Epoch: 02 | Epoch Time: 0m 0s\n",
            "\tTrain Loss: 0.691 | Train Acc: 56.26%\n",
            "\t Val. Loss: 0.696 |  Val. Acc: 51.07%\n",
            "Epoch: 03 | Epoch Time: 0m 0s\n",
            "\tTrain Loss: 0.687 | Train Acc: 56.26%\n",
            "\t Val. Loss: 0.704 |  Val. Acc: 51.07%\n",
            "Epoch: 04 | Epoch Time: 0m 0s\n",
            "\tTrain Loss: 0.688 | Train Acc: 56.26%\n",
            "\t Val. Loss: 0.695 |  Val. Acc: 51.07%\n",
            "Epoch: 05 | Epoch Time: 0m 0s\n",
            "\tTrain Loss: 0.687 | Train Acc: 56.26%\n",
            "\t Val. Loss: 0.702 |  Val. Acc: 51.07%\n",
            "Epoch: 06 | Epoch Time: 0m 0s\n",
            "\tTrain Loss: 0.688 | Train Acc: 56.26%\n",
            "\t Val. Loss: 0.697 |  Val. Acc: 51.07%\n",
            "Epoch: 07 | Epoch Time: 0m 0s\n",
            "\tTrain Loss: 0.686 | Train Acc: 56.26%\n",
            "\t Val. Loss: 0.700 |  Val. Acc: 51.07%\n",
            "Epoch: 08 | Epoch Time: 0m 0s\n",
            "\tTrain Loss: 0.687 | Train Acc: 56.26%\n",
            "\t Val. Loss: 0.699 |  Val. Acc: 51.07%\n",
            "Epoch: 09 | Epoch Time: 0m 0s\n",
            "\tTrain Loss: 0.687 | Train Acc: 56.26%\n",
            "\t Val. Loss: 0.699 |  Val. Acc: 51.07%\n",
            "Epoch: 10 | Epoch Time: 0m 0s\n",
            "\tTrain Loss: 0.686 | Train Acc: 56.26%\n",
            "\t Val. Loss: 0.700 |  Val. Acc: 51.07%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B_Cy6CcDmhme"
      },
      "source": [
        "predlist=torch.zeros(0,dtype=torch.long, device='cpu')\n",
        "lbllist=torch.zeros(0,dtype=torch.long, device='cpu')\n",
        "\n",
        "with torch.no_grad():\n",
        "  for step, batch in enumerate(eval_batches):\n",
        "    data, label = (t for t in batch)\n",
        "    data = data.to(device)\n",
        "    label = label.to(device)\n",
        "    predictions = model(data).squeeze(1)\n",
        "    predictions = torch.sigmoid(predictions)\n",
        "    predictions = torch.round(predictions)\n",
        "\n",
        "    predlist=torch.cat([predlist,predictions.view(-1).cpu()])\n",
        "    lbllist=torch.cat([lbllist,label.view(-1).cpu()])\n"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BK4i4cSNZ7W6",
        "outputId": "683e7aa7-8ed1-4b08-b9c0-b11b1dfadea4"
      },
      "source": [
        "conf_mat=confusion_matrix(lbllist.numpy(), predlist.numpy())\n",
        "print(conf_mat)\n"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  0 264]\n",
            " [  0 281]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0tRocEZ4bQyt",
        "outputId": "1aa96630-5d59-444c-e1aa-d0945a0c5ea9"
      },
      "source": [
        "predlist"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
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              "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "        1., 1., 1., 1., 1.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xKwO7VUYbZPB",
        "outputId": "7d399e83-70c4-4b7d-ff29-3cecaa2a6f54"
      },
      "source": [
        "lbllist"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ghtRiEec9Z0"
      },
      "source": [
        ""
      ],
      "execution_count": 31,
      "outputs": []
    }
  ]
}