gan_stock_prediction.ipynb

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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/skynunu/StockPrediction_Project_bigdata/blob/main/gan_stock_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9qIQCCztu5S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9186c0d-ddbc-4ed7-ac0b-903fb125482e"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#%cd /content/drive/MyDrive/bigdata_term_project_2021/\n",
        "%cd /content/drive/MyDrive/수업/빅데이터/과제/코드/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gXQHODQg8ceI",
        "outputId": "3efb83bd-29d6-4b05-8a3f-8c7a376c8d5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/수업/빅데이터/과제/코드\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p90iaEHo1Yg6"
      },
      "source": [
        "import sys\n",
        "from pathlib import Path\n",
        "from datetime import timedelta\n",
        "import dateutil\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch.autograd as autograd\n",
        "import torch.nn as nn\n",
        "import torch.functional as F\n",
        "import torch.optim as optim\n",
        "from tqdm.notebook import trange\n",
        "from contextlib2 import redirect_stdout\n",
        "import random\n",
        "from ipywidgets import FloatProgress\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "import sys\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#기본 hyperparameter 설정\n",
        "WINDOW_GIVEN = 19\n",
        "BATCH_SIZE = 64\n",
        "WINDOW_SIZE = WINDOW_GIVEN + 1\n",
        "EPOCHS = 60\n",
        "\n",
        "PATH = str(WINDOW_GIVEN)+\"_\"+str(BATCH_SIZE)+\"_\"+str(EPOCHS)\n",
        "GENERATOR_PATH = \"model/\"+ PATH +\"_generator_model.pt\"\n",
        "DISCRIMINATOR_PATH = \"model/\" +PATH+\"_discriminator_model.pt\""
      ],
      "metadata": {
        "id": "Zxo0qdPGmUCo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Dataset(Dataset):\n",
        "    def __init__(self, df, stride=1):\n",
        "        self.tag_values = np.array(df, dtype=np.float32)\n",
        "        self.valid_idxs = []\n",
        "        for L in trange(df.shape[0] - WINDOW_SIZE + 1):\n",
        "          self.valid_idxs.append(L)\n",
        "        self.valid_idxs = np.array(self.valid_idxs, dtype=np.int32)[::stride]\n",
        "        self.n_idxs = len(self.valid_idxs)\n",
        "        print(f\"# of valid windows: {self.n_idxs}\")\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.n_idxs\n",
        "    def __getitem__(self, idx):\n",
        "        i = self.valid_idxs[idx]\n",
        "        last = i + WINDOW_GIVEN\n",
        "        item = {}\n",
        "        item[\"given\"] = torch.from_numpy(self.tag_values[i : i + WINDOW_GIVEN])\n",
        "        item[\"answer\"] = torch.from_numpy(self.tag_values[last])\n",
        "\n",
        "        if i == 0  :\n",
        "          item['class'] = 0\n",
        "        elif self.tag_values[i][3] <= self.tag_values[i-1][3] : \n",
        "          item['class'] = 0\n",
        "        else :\n",
        "          item['class'] = 1  \n",
        "  \n",
        "        \n",
        "        return item"
      ],
      "metadata": {
        "id": "u7yhdejjc9-N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X7aM8fC-84HV"
      },
      "source": [
        "def make_dataset(raw_data, scaler):\n",
        "    delete_columns = ['Unnamed: 0', \"Date\", \"Close\", 'year', 'month']\n",
        "    raw_data= raw_data.drop(columns =delete_columns )\n",
        "    raw_data = raw_data.dropna()\n",
        "\n",
        "    data_normal= scaler.transform(raw_data)\n",
        "    data_normal_pd = pd.DataFrame(data_normal)\n",
        "    DATASET_TRAIN = Dataset(data_normal)\n",
        "    return DATASET_TRAIN\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_result(dataset, batch_size, model):\n",
        "    dataloader = DataLoader(dataset, batch_size=batch_size)\n",
        "    predict_list, answer_list, label_list = [], [], []\n",
        "    with torch.no_grad():\n",
        "        for batch in dataloader:\n",
        "            given = batch[\"given\"].cuda()\n",
        "            answer = batch[\"answer\"].cuda()\n",
        "            label = batch[\"class\"].cuda()\n",
        "            guess = model(given)\n",
        "\n",
        "            predict_list.append(guess.cpu().numpy())\n",
        "            answer_list.append(answer.cpu().numpy())\n",
        "            label_list.append(label.cpu().numpy())\n",
        "\n",
        "    return (\n",
        "        np.concatenate(predict_list),\n",
        "        np.concatenate(answer_list),\n",
        "        np.concatenate(label_list),\n",
        "    )"
      ],
      "metadata": {
        "id": "NCTTlqs5HLXg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DeIgK4rGJXas"
      },
      "source": [
        "#예측한 값들을 전날 기준으로 상승 감소여부를 판단해서 클래스로 산출하는 함수\n",
        "def check_class(result) :\n",
        "  length = result.shape[0] \n",
        "  result = result[:,0]\n",
        "  class_list = [0]\n",
        "  for i in range(1,length) :\n",
        "    if result[i]>result[i-1] :\n",
        "      class_list.append(1)\n",
        "    else :\n",
        "      class_list.append(0)\n",
        "  return class_list"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def model_test_score(dataset, model, scaler) : \n",
        "  test_predict, test_answer,  test_label = get_result(dataset , BATCH_SIZE, model)\n",
        "  test_scaled = scaler.inverse_transform(test_predict[:,3].reshape(-1, 1))\n",
        "  predict_label = check_class(test_scaled) #예측한 값들을 전날 기준으로 상승 감소여부를 판단해서 클래스로 산출\n",
        "\n",
        "  accuracy = np.round(accuracy_score(predict_label, test_label),3)\n",
        "  f1 = np.round(f1_score(predict_label, test_label),3)\n",
        "  precision = np.round(precision_score(predict_label, test_label),3)\n",
        "  recall = np.round(recall_score(predict_label, test_label),3)\n",
        "\n",
        "  return accuracy, f1, precision, recall"
      ],
      "metadata": {
        "id": "B3QQh3EngfQC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uz2u0d-55enK"
      },
      "source": [
        "##1. 데이터 불러오기 & 전처리\n",
        "\n",
        "###(1) 학습 데이터\n",
        "  * 아마존 애플 넷플릭스 페이스북 구글 2000년이후 데이터 불러오기\n",
        "  * 아마존 :  2014년, 2015년  /  애플 :  2017년 / 넷플릭스 :  2012년, 2017년 / 페이스북:  2017년 / 구글: 2008년 제외 \n",
        "  * 제외한 년도 데이터는 테스트 데이터로 사용\n",
        "\n",
        "\n",
        "  ### (2) 테스트 데이터\n",
        "  * 아마존 :  2014년, 2015년 /  애플 :  2017년 , 1985년   / 넷플릭스 :  2012년, 2017년 /  구글: 2008년  / 페이스북:  2017년 \n",
        "  * 데이터의 상승 하강 패턴을 고려해서 테스트 데이터셋 구성\n",
        "  * 상승년도 4년 : amazon 2015년 / apple 2017년 / netflix 2017년 / facebook 2017년\n",
        "  * 하강년도 4년 :  amazon 2014년 / apple 1985년 /  netflix 2012년 / google 2008년 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Ym4Ad2K14b3"
      },
      "source": [
        "# 학습 데이터 불러오기\n",
        "amazon = pd.read_csv('data/amazon_new.csv')\n",
        "apple = pd.read_csv('data/apple_new.csv')\n",
        "facebook = pd.read_csv('data/facebook_new.csv')\n",
        "google = pd.read_csv('data/google_new.csv')\n",
        "netflix = pd.read_csv('data/netflix_new.csv')\n",
        "\n",
        "# slinding window를 할때, 년도별 잘린 구간을 구분하고자, 잘린 구간을 기점으로 데이터셋 구성\n",
        "# 년도별 잘린 구간은 테스트 데이터셋으로 구성\n",
        "\n",
        "# 아마존 2014년, 2015년 제외\n",
        "amazon = amazon[amazon['year'] >=2000]\n",
        "amazon_train1 = amazon[amazon['year'] <2014] \n",
        "amazon_train2 = amazon[amazon['year'] >2015] \n",
        "\n",
        "# 애플 2017년 제외\n",
        "apple = apple[apple['year'] >=2000]\n",
        "apple_train1 = apple[apple['year'] <2017] \n",
        "apple_train2 = apple[apple['year'] >2017] \n",
        "\n",
        "# 넷플릭스 2012년, 2017년 제외\n",
        "netflix = netflix[netflix['year'] >=2000]\n",
        "netflix_train1 = netflix[netflix['year'] <=2012] \n",
        "netflix_train2 = netflix[(netflix['year']  >=2012) & (netflix['year']  <=2017)] \n",
        "netflix_train3 = netflix[netflix['year'] >=2017] \n",
        "\n",
        "# 페이스북 2017년 제외\n",
        "facebook = facebook[facebook['year'] >=2000]\n",
        "facebook_train1 = facebook[facebook['year'] <=2017] \n",
        "facebook_train2 = facebook[facebook['year'] >=2017]\n",
        "\n",
        " #구글 2008년 제외\n",
        "google = google[google['year'] >=2000]\n",
        "google_train1 = google[google['year'] <=2008] \n",
        "google_train2 = google[google['year'] >=2008] "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 테스트 데이터 불러오기\n",
        "test_amazon_inc = pd.read_csv('data/amazon_inc.csv')\n",
        "test_amazon_dec = pd.read_csv('data/amazon_dec.csv')\n",
        "\n",
        "test_apple_inc = pd.read_csv('data/apple_inc.csv')\n",
        "test_apple_dec = pd.read_csv('data/apple_dec.csv')\n",
        "\n",
        "test_netflix_inc = pd.read_csv('data/netflix_inc.csv')\n",
        "test_netflix_dec = pd.read_csv('data/netflix_dec.csv')\n",
        "\n",
        "test_google_dec = pd.read_csv('data/google_dec.csv')\n",
        "test_facebook_inc = pd.read_csv('data/facebook_inc.csv')"
      ],
      "metadata": {
        "id": "hpRTHywtSAyd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#전체 데이터 normalize 학습 \n",
        "all_dataset = pd.concat([amazon,apple,netflix,facebook,google], axis = 0)\n",
        "delete_columns = ['Unnamed: 0', \"Date\", \"Close\", 'year', 'month']\n",
        "all_dataset_drop= all_dataset.drop(columns =delete_columns)\n",
        "print(\"all_dataset.shape : \", all_dataset_drop.shape)\n",
        "scaler = MinMaxScaler()\n",
        "scaler =scaler.fit(all_dataset_drop)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fHd1TGOVUKzY",
        "outputId": "affbbc70-4197-4dcf-cf7f-b398812dd518"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "all_dataset.shape :  (20573, 5)\n"
          ]
        }
      ]
    },
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        "id": "77HINO3U8PwT",
        "outputId": "6572c576-8d13-4630-caa2-55c762322912"
      },
      "source": [
        "#학습 dataset 만들기\n",
        "\n",
        "# 아마존\n",
        "DATASET_amazon1 = make_dataset(amazon_train1, scaler)\n",
        "DATASET_amazon2 = make_dataset(amazon_train2, scaler)\n",
        "DATASET_amazon = torch.utils.data.ConcatDataset([DATASET_amazon1, DATASET_amazon2])\n",
        "\n",
        "# 애플\n",
        "DATASET_apple1 = make_dataset(apple_train1, scaler)\n",
        "DATASET_apple2 = make_dataset(apple_train2, scaler)\n",
        "DATASET_apple = torch.utils.data.ConcatDataset([DATASET_apple1, DATASET_apple2])\n",
        "\n",
        "# 넷플릭스\n",
        "DATASET_netflix1= make_dataset(netflix_train1, scaler)\n",
        "DATASET_netflix2 = make_dataset(netflix_train2, scaler)\n",
        "DATASET_netflix3 = make_dataset(netflix_train3, scaler)\n",
        "DATASET_netflix = torch.utils.data.ConcatDataset([DATASET_netflix1, DATASET_netflix2, DATASET_netflix3])\n",
        "\n",
        "# 페이스북\n",
        "DATASET_facebook1 = make_dataset(facebook_train1, scaler)\n",
        "DATASET_facebook2 = make_dataset(facebook_train2, scaler)\n",
        "DATASET_facebook = torch.utils.data.ConcatDataset([DATASET_facebook1, DATASET_facebook2])\n",
        "\n",
        "# 구글\n",
        "DATASET_google1 = make_dataset(google_train1, scaler)\n",
        "DATASET_google2 = make_dataset(google_train2, scaler)\n",
        "DATASET_google = torch.utils.data.ConcatDataset([DATASET_google1, DATASET_google2])\n",
        "\n",
        "# torch dataset 만들고 dataloader 만들기\n",
        "DATASET_TRAIN = torch.utils.data.ConcatDataset([DATASET_amazon, DATASET_apple, DATASET_netflix, DATASET_facebook, DATASET_google])\n",
        "train_dataloader = DataLoader(DATASET_TRAIN, batch_size=BATCH_SIZE, shuffle=True, drop_last=True ) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e507e0771dfc424ea9e4548e36bd503f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/3502 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 3502\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "20e0d935dcee4ea2a8dbfea45d5ec3f0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 1144\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f3fac068dc8447e99a7c71075a806767",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 4258\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "66c34eaaa28a43839aeb3b5b322381b9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 653\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2d6f9d2a16714791ade7b316ef5125b1",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 3250\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "748dedeaf84448c0affba37ce8a0f304",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 986\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a8c7b023f69b4dbd87803ffb2f630542",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 892\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cf0e0f22208d4387ae23a81564465fd0",
              "version_minor": 0,
              "version_major": 2
            },
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 1144\n"
          ]
        },
        {
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        {
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          "name": "stdout",
          "text": [
            "# of valid windows: 643\n"
          ]
        },
        {
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          "data": {
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          "metadata": {}
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 1991\n"
          ]
        },
        {
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 2402\n"
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    {
      "cell_type": "code",
      "source": [
        "#테스트 dataset 만들기\n",
        "\n",
        "#증가 테스트 데이터셋\n",
        "DATASET_inc1 = make_dataset(test_amazon_inc, scaler)\n",
        "DATASET_inc2 = make_dataset(test_apple_inc, scaler)\n",
        "DATASET_inc3 = make_dataset(test_netflix_inc, scaler)\n",
        "DATASET_inc4 = make_dataset(test_facebook_inc, scaler)\n",
        "DATASET_TEST_INC = torch.utils.data.ConcatDataset([DATASET_inc1, DATASET_inc2,DATASET_inc3,DATASET_inc4])\n",
        "\n",
        "#감소 테스트 데이터셋\n",
        "DATASET_dec1 = make_dataset(test_amazon_dec, scaler)\n",
        "DATASET_dec2 = make_dataset(test_apple_dec, scaler)\n",
        "DATASET_dec3 = make_dataset(test_netflix_dec, scaler)\n",
        "DATASET_dec4 = make_dataset(test_google_dec, scaler)\n",
        "DATASET_TEST_DEC = torch.utils.data.ConcatDataset([DATASET_dec1, DATASET_dec2,DATASET_dec3,DATASET_dec4])"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "24eb106bb7704da19eccc5df90833c9e",
              "version_minor": 0,
              "version_major": 2
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 233\n"
          ]
        },
        {
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          "data": {
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              "model_id": "c1eaa3614e9f4a738bf59241c751f7b7",
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 232\n"
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              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 232\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "4047d3a9d0d9442682adc8420b677a7f",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 232\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "5fa02d80eac04badb70bfa0efff41234",
              "version_minor": 0,
              "version_major": 2
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 233\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "96fb8e69640c4dc0a02de77270600076",
              "version_minor": 0,
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          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 233\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "6ed3212ea7f4451d939a1ad2979f1245",
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 231\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d46e367be7df4118b11d9ee2fe02825e",
              "version_minor": 0,
              "version_major": 2
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# of valid windows: 234\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. GAN : generator, discriminator 구현\n",
        "###  - 논문 참조 : Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets\n",
        "### - github : https://github.com/kanelian63/Stock-Market-Prediction-Using-GAN"
      ],
      "metadata": {
        "id": "o56dvW-LHOF_"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZ5AmFfCgqKW"
      },
      "source": [
        "\"\"\"\n",
        "From the DCGAN paper, the authors specify that all model weights shall be randomly initialized from a Normal distribution with mean=0, stdev=0.02.\n",
        "The weights_init function takes an initialized model as input and reinitializes all convolutional, convolutional-transpose, and batch normalization layers to meet this criteria.\n",
        "This function is applied to the models immediately after initialization.\n",
        "\"\"\"\n",
        "# 논문에 초기 가중치를 어떻게 하라는 구체적인 설명이 없어서 DCGAN의 초기 가중치를 가져옴\n",
        "# custom weights initialization called on netG and netD from Normal distribution\n",
        "def weights_init(m):\n",
        "    classname = m.__class__.__name__\n",
        "    if classname.find('Conv') != -1:\n",
        "        nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
        "    elif classname.find('BatchNorm') != -1:\n",
        "        nn.init.normal_(m.weight.data, 1.0, 0.02)\n",
        "        nn.init.constant_(m.bias.data, 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KkpcRM33g0QR"
      },
      "source": [
        "# Generator\n",
        "class Generator(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, num_layers, output_dim):\n",
        "        super(Generator, self).__init__()\n",
        "        self.input_dim = input_dim\n",
        "        self.hidden_dim = hidden_dim\n",
        "        self.num_layers = num_layers\n",
        "        self.output_dim = output_dim\n",
        "        \n",
        "        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)\n",
        "       \n",
        "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        # Initialize hidden state with zeros\n",
        "        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_().cuda()\n",
        "        # Initialize cell state\n",
        "        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_().cuda()\n",
        "        \n",
        "        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))\n",
        "        \n",
        "        # Index hidden state of last time step\n",
        "        out = self.fc(out[:, -1,:])\n",
        "        return x[:,0,:]+out #skip connection\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lkapJVjrg46z",
        "outputId": "4e196918-4dc0-444d-8f4e-d4127ce35ea3"
      },
      "source": [
        "input_dim = 5\n",
        "hidden_dim = 100\n",
        "num_layers = 3\n",
        "output_dim = 5\n",
        "\n",
        "# generator 만들기\n",
        "generator = Generator(input_dim, hidden_dim, num_layers, output_dim)\n",
        "generator.cuda()\n",
        "\n",
        "\n",
        "# Apply the weights_init function to randomly initialize all weights\n",
        "#  to mean=0, stdev=0.2.\n",
        "generator.apply(weights_init)\n",
        "print(generator)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generator(\n",
            "  (lstm): LSTM(5, 100, num_layers=3, batch_first=True)\n",
            "  (fc): Linear(in_features=100, out_features=5, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hAyqDtJemN0e"
      },
      "source": [
        "# Descriminator\n",
        "class Discriminator(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, kernel_size,dim_features =32):\n",
        "        super(Discriminator, self).__init__()\n",
        "        self.in_channels = in_channels\n",
        "        self.out_channels = out_channels\n",
        "        self.kernel_size = kernel_size\n",
        "        \n",
        "        self.conv1d_1 = nn.Conv1d(in_channels=5, out_channels=dim_features, kernel_size=3, stride=1)\n",
        "        self.bn_1 = nn.BatchNorm1d(dim_features)\n",
        "        self.relu_1 = nn.ReLU()\n",
        "        \n",
        "        self.conv1d_2 = nn.Conv1d(in_channels=dim_features, out_channels=dim_features*2, kernel_size=3, stride=1)\n",
        "        self.bn_2 = nn.BatchNorm1d(dim_features*2)\n",
        "        self.relu_2 = nn.ReLU()\n",
        "\n",
        "        self.conv1d_3 = nn.Conv1d(in_channels=dim_features*2, out_channels=dim_features*4, kernel_size=3, stride=1)\n",
        "        self.bn_3 = nn.BatchNorm1d(dim_features*4)\n",
        "        self.relu_3 = nn.ReLU()\n",
        "\n",
        "        self.linear_1 = nn.Linear(1792, dim_features*6)\n",
        "        self.leaky_1 = nn.LeakyReLU()\n",
        "        self.linear_2 = nn.Linear(dim_features*6, 1)\n",
        "\n",
        "        self.ac_1 = nn.Sigmoid()\n",
        "        \n",
        "    def forward(self, x):\n",
        "        x = self.conv1d_1(x)\n",
        "        x = self.bn_1(x)\n",
        "        x = self.relu_1(x)\n",
        "\n",
        "        x = self.conv1d_2(x)\n",
        "        x = self.bn_2(x)\n",
        "        x = self.relu_2(x)\n",
        "\n",
        "        x = self.conv1d_3(x)\n",
        "        x = self.bn_3(x)\n",
        "        x = self.relu_3(x)\n",
        "\n",
        "        x = x.view(x.shape[0], -1)\n",
        "        \n",
        "        x = self.linear_1(x)\n",
        "        x = self.leaky_1(x)\n",
        "        x = self.linear_2(x)\n",
        "        \n",
        "        x = self.ac_1(x)\n",
        "        \n",
        "        return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rfy81g9Emql5",
        "outputId": "9bef3dca-bc32-4067-8cf5-a4b2c843c4f0"
      },
      "source": [
        "# Apply the weights_init function to randomly initialize all weights\n",
        "#  to mean=0, stdev=0.2.\n",
        "in_channels = 1\n",
        "out_channels = 5\n",
        "kernel_size = 20\n",
        "\n",
        "# Discriminator 만들기\n",
        "discriminator = Discriminator(in_channels, out_channels, kernel_size).cuda()\n",
        "discriminator.apply(weights_init)\n",
        "\n",
        "#  Discriminator 모델 출력\n",
        "print(discriminator)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Discriminator(\n",
            "  (conv1d_1): Conv1d(5, 32, kernel_size=(3,), stride=(1,))\n",
            "  (bn_1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (relu_1): ReLU()\n",
            "  (conv1d_2): Conv1d(32, 64, kernel_size=(3,), stride=(1,))\n",
            "  (bn_2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (relu_2): ReLU()\n",
            "  (conv1d_3): Conv1d(64, 128, kernel_size=(3,), stride=(1,))\n",
            "  (bn_3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (relu_3): ReLU()\n",
            "  (linear_1): Linear(in_features=1792, out_features=192, bias=True)\n",
            "  (leaky_1): LeakyReLU(negative_slope=0.01)\n",
            "  (linear_2): Linear(in_features=192, out_features=1, bias=True)\n",
            "  (ac_1): Sigmoid()\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. 주식 예측을 위한 GAN 모델 학습\n",
        "  * GAN 모델 하이퍼 파라미터 설정\n",
        "  * GAN 모델 학습\n",
        "  * GAN 모델 generator, discriminator loss 그래프 그리기"
      ],
      "metadata": {
        "id": "2akaUP2cIO58"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "C_K35SdBapZx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELO5BVi_npVF"
      },
      "source": [
        "# GAN 모델 하이퍼 파라미터 설정\n",
        "\n",
        "# Initialize BCELoss function\n",
        "adversarial_loss  = nn.BCELoss()\n",
        "\n",
        "# Establish convention for real and fake labels during training\n",
        "lr = 0.001\n",
        "beta1 = 0.9\n",
        "\n",
        "# Setup Adam optimizers for both G and D\n",
        "optimizer_d = optim.Adam(discriminator.parameters(), lr=lr, betas=(beta1, 0.999))\n",
        "optimizer_g = optim.Adam(generator.parameters(), lr=lr, betas=(beta1, 0.999))\n",
        "\n",
        "\n",
        "discriminator.cuda()\n",
        "adversarial_loss.cuda()\n",
        "\n",
        "G_losses = []\n",
        "D_losses = []\n",
        "D_losses_sum = []\n",
        "G_data = []\n",
        "\n",
        "lamda_adv = 0.01\n",
        "lamda_p = 20\n",
        "lamda_dpl = 0.8\n",
        "\n",
        "fake_label = torch.zeros(BATCH_SIZE,1, dtype=torch.float).cuda()\n",
        "real_label = torch.ones(BATCH_SIZE,1, dtype=torch.float).cuda()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qFhxVZ33wys7",
        "outputId": "5be7d545-91a8-4d4c-cbe6-bdcec7c31a78"
      },
      "source": [
        "\n",
        "for epoch in range(EPOCHS):\n",
        "    for batch in train_dataloader:\n",
        "        x_train = batch[\"given\"].cuda()\n",
        "        y_train = batch[\"answer\"].cuda()\n",
        "  \n",
        "      # train G\n",
        "        # gradient 0\n",
        "        generator.zero_grad()\n",
        "        \n",
        "        # product fake_data for d\n",
        "        generated_data = generator(x_train).reshape(BATCH_SIZE,1,5)\n",
        "        fake_data = torch.cat((x_train, generated_data), axis=1)\n",
        "        fake_data = fake_data.reshape(BATCH_SIZE, 5 ,WINDOW_SIZE)\n",
        "\n",
        "        # train d with fake_data\n",
        "        output_fake_d = discriminator(fake_data)\n",
        "        \n",
        "        loss_g_adv = adversarial_loss(output_fake_d, real_label)\n",
        "        \n",
        "        L_p = torch.norm(generated_data.reshape(BATCH_SIZE,5) - y_train)\n",
        "                \n",
        "        L_dpl = (torch.sign(generated_data - x_train.reshape(BATCH_SIZE,WINDOW_GIVEN,5)[0][-1])- torch.sign(y_train - x_train.reshape(BATCH_SIZE,WINDOW_GIVEN,5)[0][-1])).mean().abs()\n",
        "        \n",
        "        # Loss G\n",
        "        loss_g = lamda_adv*loss_g_adv + lamda_p*L_p + lamda_dpl*L_dpl\n",
        "        \n",
        "        loss_g.backward()\n",
        "        \n",
        "        loss_g_average = loss_g.mean().item()\n",
        "    \n",
        "        # update weight\n",
        "        optimizer_g.step()\n",
        "        \n",
        "        \n",
        "    # train D\n",
        "        # gradient 0\n",
        "        discriminator.zero_grad()\n",
        "\n",
        "        # train d with real data\n",
        "        real_data = torch.cat((x_train, y_train.reshape(BATCH_SIZE,1,5)),axis=1).reshape(BATCH_SIZE,5,WINDOW_SIZE)\n",
        "\n",
        "        output_real_d = discriminator(real_data)\n",
        "\n",
        "        # Loss D with real_data\n",
        "        # save gradient d with real_data\n",
        "        loss_real_d = adversarial_loss(output_real_d, real_label)\n",
        "\n",
        "        loss_real_d.backward()\n",
        "\n",
        "        \n",
        "        # train d with fake data\n",
        "        # product real fake_data\n",
        "        generated_data = generator(x_train).reshape(BATCH_SIZE,1,5)\n",
        "        fake_data = torch.cat((x_train, generated_data),axis=1).reshape(BATCH_SIZE,5,WINDOW_SIZE)\n",
        "        \n",
        "        # Loss D with fake_data\n",
        "        output_fake_d = discriminator(fake_data)\n",
        "        loss_fake_d = adversarial_loss(output_fake_d, fake_label)\n",
        "        \n",
        "        # save gradient d with fake_data\n",
        "        loss_fake_d.backward()\n",
        "        \n",
        "        loss_fake_d_average = loss_fake_d.mean().item()\n",
        "        \n",
        "        # sum of Loss D\n",
        "        loss_sum = loss_real_d + loss_fake_d\n",
        "        \n",
        "        loss_d_sum = loss_sum.mean().item()\n",
        "        \n",
        "        # gradient update\n",
        "        optimizer_d.step()\n",
        "        \n",
        "        # Output training stats\n",
        "       \n",
        "\n",
        "    # Save Losses for plotting later\n",
        "    D_losses.append(loss_fake_d_average)\n",
        "    G_losses.append(loss_g_average) \n",
        "    print('%d번째 학습 =>  discriminator loss : %f, genrator loss : %f' % (epoch+1, loss_fake_d_average, loss_g))\n",
        "\n",
        "\n",
        "#generator, discriminator 저장\n",
        "torch.save(generator.state_dict(), GENERATOR_PATH)\n",
        "torch.save(discriminator.state_dict(), DISCRIMINATOR_PATH)\n",
        "\n",
        "with open(GENERATOR_PATH, \"wb\") as f:\n",
        "    torch.save(generator.state_dict(),f)\n",
        "\n",
        "with open(DISCRIMINATOR_PATH, \"wb\") as f:\n",
        "    torch.save(generator.state_dict(),f)\n",
        "\n",
        "\n",
        "D_losses = pd.DataFrame(D_losses)\n",
        "G_losses = pd.DataFrame(G_losses)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1번째 학습 =>  discriminator loss : 0.684497, genrator loss : 5.236153\n",
            "2번째 학습 =>  discriminator loss : 0.649711, genrator loss : 5.189653\n",
            "3번째 학습 =>  discriminator loss : 0.477375, genrator loss : 7.929291\n",
            "4번째 학습 =>  discriminator loss : 0.377174, genrator loss : 5.722734\n",
            "5번째 학습 =>  discriminator loss : 0.371206, genrator loss : 3.639832\n",
            "6번째 학습 =>  discriminator loss : 0.170470, genrator loss : 4.766150\n",
            "7번째 학습 =>  discriminator loss : 0.236941, genrator loss : 3.741702\n",
            "8번째 학습 =>  discriminator loss : 0.421506, genrator loss : 3.759188\n",
            "9번째 학습 =>  discriminator loss : 0.262543, genrator loss : 4.139966\n",
            "10번째 학습 =>  discriminator loss : 0.319088, genrator loss : 4.145564\n",
            "11번째 학습 =>  discriminator loss : 0.145384, genrator loss : 6.900580\n",
            "12번째 학습 =>  discriminator loss : 0.362909, genrator loss : 2.478645\n",
            "13번째 학습 =>  discriminator loss : 0.508421, genrator loss : 3.392833\n",
            "14번째 학습 =>  discriminator loss : 0.023527, genrator loss : 5.159268\n",
            "15번째 학습 =>  discriminator loss : 0.154895, genrator loss : 1.907226\n",
            "16번째 학습 =>  discriminator loss : 0.133795, genrator loss : 6.194067\n",
            "17번째 학습 =>  discriminator loss : 0.901460, genrator loss : 2.473117\n",
            "18번째 학습 =>  discriminator loss : 0.009714, genrator loss : 3.283520\n",
            "19번째 학습 =>  discriminator loss : 0.024945, genrator loss : 3.839565\n",
            "20번째 학습 =>  discriminator loss : 0.044446, genrator loss : 3.362049\n",
            "21번째 학습 =>  discriminator loss : 0.026506, genrator loss : 3.339918\n",
            "22번째 학습 =>  discriminator loss : 0.031118, genrator loss : 2.500014\n",
            "23번째 학습 =>  discriminator loss : 0.021059, genrator loss : 3.146280\n",
            "24번째 학습 =>  discriminator loss : 0.024416, genrator loss : 3.499277\n",
            "25번째 학습 =>  discriminator loss : 0.003912, genrator loss : 3.169269\n",
            "26번째 학습 =>  discriminator loss : 0.014101, genrator loss : 2.662217\n",
            "27번째 학습 =>  discriminator loss : 0.000341, genrator loss : 2.619185\n",
            "28번째 학습 =>  discriminator loss : 0.002458, genrator loss : 1.975221\n",
            "29번째 학습 =>  discriminator loss : 0.010849, genrator loss : 4.418221\n",
            "30번째 학습 =>  discriminator loss : 0.207574, genrator loss : 1.929331\n",
            "31번째 학습 =>  discriminator loss : 0.025118, genrator loss : 3.930285\n",
            "32번째 학습 =>  discriminator loss : 0.065220, genrator loss : 3.098138\n",
            "33번째 학습 =>  discriminator loss : 0.012664, genrator loss : 2.163433\n",
            "34번째 학습 =>  discriminator loss : 0.007998, genrator loss : 2.077479\n",
            "35번째 학습 =>  discriminator loss : 0.014261, genrator loss : 2.590458\n",
            "36번째 학습 =>  discriminator loss : 0.022870, genrator loss : 3.683802\n",
            "37번째 학습 =>  discriminator loss : 0.013230, genrator loss : 1.916727\n",
            "38번째 학습 =>  discriminator loss : 0.019511, genrator loss : 2.595061\n",
            "39번째 학습 =>  discriminator loss : 1.346775, genrator loss : 1.920543\n",
            "40번째 학습 =>  discriminator loss : 0.008533, genrator loss : 3.467216\n",
            "41번째 학습 =>  discriminator loss : 0.214852, genrator loss : 2.812407\n",
            "42번째 학습 =>  discriminator loss : 0.016527, genrator loss : 2.842842\n",
            "43번째 학습 =>  discriminator loss : 0.047984, genrator loss : 2.619792\n",
            "44번째 학습 =>  discriminator loss : 0.269240, genrator loss : 4.219739\n",
            "45번째 학습 =>  discriminator loss : 0.009168, genrator loss : 2.310820\n",
            "46번째 학습 =>  discriminator loss : 0.074451, genrator loss : 3.088022\n",
            "47번째 학습 =>  discriminator loss : 0.824233, genrator loss : 1.955307\n",
            "48번째 학습 =>  discriminator loss : 0.136138, genrator loss : 2.054739\n",
            "49번째 학습 =>  discriminator loss : 0.146989, genrator loss : 2.196729\n",
            "50번째 학습 =>  discriminator loss : 1.067417, genrator loss : 3.129366\n",
            "51번째 학습 =>  discriminator loss : 0.072332, genrator loss : 3.250550\n",
            "52번째 학습 =>  discriminator loss : 0.175932, genrator loss : 2.083486\n",
            "53번째 학습 =>  discriminator loss : 0.122330, genrator loss : 2.827573\n",
            "54번째 학습 =>  discriminator loss : 0.239054, genrator loss : 2.043013\n",
            "55번째 학습 =>  discriminator loss : 0.002215, genrator loss : 1.559586\n",
            "56번째 학습 =>  discriminator loss : 0.261970, genrator loss : 2.295641\n",
            "57번째 학습 =>  discriminator loss : 0.051126, genrator loss : 1.933956\n",
            "58번째 학습 =>  discriminator loss : 0.639777, genrator loss : 2.404353\n",
            "59번째 학습 =>  discriminator loss : 0.029455, genrator loss : 1.520546\n",
            "60번째 학습 =>  discriminator loss : 0.045254, genrator loss : 2.542899\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# GAN 모델 generator, discriminator loss 그래프 그리기\n",
        "%matplotlib inline \n",
        "x_axis =np.arange(1,EPOCHS+1)\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(D_losses) # line1 plot(x레이블, y레이블)\n",
        "plt.plot(G_losses) # line2 plot(x레이블, y레이블)\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.title('loss graph')\n",
        "plt.legend(['discriminator', 'generator']) #plt.legend([라인1범례, 라인2범례])\n",
        "plt.grid(True) #격자 표시 여부\n",
        "\n",
        "fig = plt.gcf()\n",
        "fig.savefig(\"image/\"+PATH+\"_train_loss.png\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "Ik8VIs1fMkw0",
        "outputId": "65692899-c5ae-485f-96e6-bff20b0c0d59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. 모델 테스트 하기\n",
        "### - 증가 패턴 데이터셋과 감소 패턴 데이터셋 결과 비교\n",
        "### - 증가 패턴 데이터셋 : amazon 2015년 / apple 2017년 / netflix 2017년 / facebook 2017년\n",
        "### - 감소 패턴 데이터셋 :  amazon 2014년 / apple 1985년 /  netflix 2012년 / google 2008년 \n",
        "### - 실험순서\n",
        "  \n",
        "  * 테스트 데이터셋에 대하여, generated data 만들기\n",
        "  \n",
        "  * 수정종가 예측값과 수정종가 정답값에 대하여 inverse_normalize 적용\n",
        "  \n",
        "  * 수정종가에 대해서 예측한 값 그래프 나타내기 실험\n",
        "\n",
        "  * 전날 기준으로 증가 감소에 대한 클래스 예측값 산출해서 실제 증감여부와 비교해서 정확도 산출\n",
        " "
      ],
      "metadata": {
        "id": "rSj4L-oq57eJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#모델 불러오기\n",
        "# Model Load\n",
        "with open(GENERATOR_PATH, \"rb\") as f:\n",
        "    SAVED_MODEL = torch.load(f)\n",
        "\n",
        "TEST_GENERATOR_PATH = \"model/\"+PATH+\"_generator_model.pt\"\n",
        "\n",
        "test_generator = Generator(input_dim, hidden_dim, num_layers, output_dim)\n",
        "test_generator.load_state_dict(torch.load(TEST_GENERATOR_PATH))\n",
        "test_generator.to(torch.device(\"cuda\")) # use GPU \n",
        "test_generator.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wicAsh0gUaDi",
        "outputId": "bac7d421-7ef5-4968-9786-5607b32f0962"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Generator(\n",
              "  (lstm): LSTM(5, 100, num_layers=3, batch_first=True)\n",
              "  (fc): Linear(in_features=100, out_features=5, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#수정종가에 대하여 MinMaxScaler 학습\n",
        "test_scaler = MinMaxScaler()\n",
        "all_dataset_adjclose = all_dataset_drop['Adj Close'].to_numpy()\n",
        "test_scaler.fit(all_dataset_adjclose.reshape(-1, 1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZfrcpqLq76in",
        "outputId": "2c255248-5e61-4ea6-d02a-55dd24bc4d40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler()"
            ]
          },
          "metadata": {},
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4-1. 증가 패턴 데이터셋 테스트\n"
      ],
      "metadata": {
        "id": "9qjTH2GcHymD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 테스트 데이터셋에 대하여, generated data 만들기\n",
        "# generated data가 test_predict\n",
        "# test answer는  test_predict기 예측하는 시점에서의 정답값\n",
        "# test_label는 전날 기준으로 상승(1) 하강(0) 여부를 나타내는 클래스\n",
        "test_predict_inc, test_answer_inc,  test_label_inc = get_result(DATASET_TEST_INC , BATCH_SIZE, test_generator)\n",
        "pred_scaled = test_scaler.inverse_transform(test_predict_inc[:,3].reshape(-1, 1))\n",
        "ans_scaled = test_scaler.inverse_transform(test_answer_inc[:,3].reshape(-1, 1))\n",
        "\n",
        "print(pred_scaled.shape)\n",
        "print(ans_scaled.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bMyz5YYtSs6-",
        "outputId": "c2fbd9c1-80db-4e3a-a362-2e2ba82813d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(929, 1)\n",
            "(929, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 증가패턴 수정종가 예측값 그래프 \n",
        "%matplotlib inline \n",
        "x_axis =np.arange(1,pred_scaled.shape[0]+1)\n",
        "from matplotlib import pyplot as plt\n",
        "plt.figure(figsize=(16,9))\n",
        "plt.plot(x_axis,pred_scaled) # line1 plot(x레이블, y레이블)\n",
        "plt.plot(x_axis,ans_scaled) # line2 plot(x레이블, y레이블)\n",
        "plt.xlabel('time')\n",
        "plt.ylabel('Adj close')\n",
        "plt.title('Adj close prediction result')\n",
        "plt.legend(['prediction', 'answer']) #plt.legend([라인1범례, 라인2범례])\n",
        "plt.grid(True) #격자 표시 여부\n",
        "\n",
        "fig = plt.gcf()\n",
        "fig.savefig(\"image/\" +PATH+\"_increase pattern.png\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "4D-lueNfDwxt",
        "outputId": "2d657fb4-b95b-47d7-c199-ef7de1e1c9d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q8WRpTlDnfnP",
        "outputId": "77eaeab9-f7e4-4067-a4fc-18af2d1a3f19"
      },
      "source": [
        "#전날 기준 증가 감소여부 예측 정확도 평가\n",
        "predict_label_inc = check_class(pred_scaled)\n",
        "accuracy, f1, precision, recall  = model_test_score(DATASET_TEST_INC, test_generator, test_scaler)\n",
        "\n",
        "print(\"=================GAN모델 예측 결과==================\")\n",
        "print(\"증가 패턴 수정 종가에 대한 증가 감소 예측 정확도 평가\")\n",
        "print(\"(1) accuracy score = \",  accuracy)\n",
        "print(\"(2) recall score = \",  recall)\n",
        "print(\"(3) precision score = \", precision)\n",
        "print(\"(4) f1 score = \",  f1 )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=================GAN모델 예측 결과==================\n",
            "증가 패턴 수정 종가에 대한 증가 감소 예측 정확도 평가\n",
            "(1) accuracy score =  0.703\n",
            "(2) recall score =  0.734\n",
            "(3) precision score =  0.705\n",
            "(4) f1 score =  0.719\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4-2. 감소 패턴 데이터셋 테스트\n"
      ],
      "metadata": {
        "id": "anp-LSabSAtg"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVU4EWmInxuM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1392eb9d-d27e-42d5-ccf0-30de729d859b"
      },
      "source": [
        "# 테스트 데이터셋에 대하여, generated data 만들기\n",
        "# generated data가 test_predict\n",
        "# test answer는  test_predict기 예측하는 시점에서의 정답값\n",
        "# test_label는 전날 기준으로 상승(1) 하강(0) 여부를 나타내는 클래스\n",
        "test_predict_dec, test_answer_dec,  test_label_dec = get_result(DATASET_TEST_DEC , BATCH_SIZE, test_generator)\n",
        "\n",
        "#예측값과 정답값에서 수정종가에 대하여 inverse_normalize 적용\n",
        "pred_scaled_dec = test_scaler.inverse_transform(test_predict_dec[:,3].reshape(-1, 1))\n",
        "ans_scaled_dec = test_scaler.inverse_transform(test_answer_dec[:,3].reshape(-1, 1))\n",
        "\n",
        "print(pred_scaled_dec.shape)\n",
        "print(ans_scaled_dec.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(931, 1)\n",
            "(931, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 감소 패턴 수정종가 예측값 그래프 \n",
        "%matplotlib inline \n",
        "x_axis =np.arange(1,pred_scaled_dec.shape[0]+1)\n",
        "from matplotlib import pyplot as plt\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(x_axis,pred_scaled_dec) # line1 plot(x레이블, y레이블)\n",
        "plt.plot(x_axis,ans_scaled_dec) # line2 plot(x레이블, y레이블)\n",
        "plt.xlabel('time')\n",
        "plt.ylabel('Adj close')\n",
        "plt.title('Adj close prediction result')\n",
        "plt.legend(['prediction', 'answer']) #plt.legend([라인1범례, 라인2범례])\n",
        "plt.grid(True) #격자 표시 여부\n",
        "fig = plt.gcf()\n",
        "fig.savefig(\"image/\"+PATH+ \"_decrease pattern.png\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "wDRSfDgjLkvg",
        "outputId": "3d065f9e-0a7a-42fd-cda1-e84278d4652c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#전날 기준 증가 감소여부 예측 정확도 평가\n",
        "\n",
        "predict_label_dec = check_class(pred_scaled_dec)\n",
        "accuracy, f1, precision, recall  = model_test_score(DATASET_TEST_DEC, test_generator, test_scaler)\n",
        "\n",
        "print(\"=================GAN모델 예측 결과===================\")\n",
        "print(\"감소패턴 수정 종가에 대한 증가 감소 예측 정확도 평가\")\n",
        "print(\"(1) accuracy score = \",  accuracy)\n",
        "print(\"(2) recall score = \",  recall)\n",
        "print(\"(3) precision score = \", precision)\n",
        "print(\"(4) f1 score = \",  f1 )\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I2Mf0BhDSONY",
        "outputId": "b8cecf33-092a-4da0-bf16-fc01ec2e04da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=================GAN모델 예측 결과===================\n",
            "감소패턴 수정 종가에 대한 증가 감소 예측 정확도 평가\n",
            "(1) accuracy score =  0.712\n",
            "(2) recall score =  0.678\n",
            "(3) precision score =  0.716\n",
            "(4) f1 score =  0.696\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. GAN모델 + XGBoost clssifier\n",
        "### - 학습이 완료된 GAN모델에 학습 데이터를 넣고, 데이터를 생성해서, XGBoost classifier에 학습 시킨다.\n",
        "### - GAN모델에서 생성된 데이터를 XGBoost Classifier에 넣고 증감여부를 y값으로 줘서 학습을 시킨 후 테스트해보기\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "UktGOQOdEg4U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#GAN에서 생성된 데이터를 XGBClassifier로 학습시킨다. \n",
        "train_predict, train_answer, train_label = get_result(DATASET_TRAIN, BATCH_SIZE, test_generator)\n",
        "train_predict.shape\n",
        "\n",
        "train_x, train_y = train_predict, train_label\n",
        "xgb_model = XGBClassifier(n_estimators =400, learning_rate = 0.1 , max_depth=4 )\n",
        "xgb_model.fit(train_x, train_y)\n"
      ],
      "metadata": {
        "id": "wGpMIqJhOyyS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e563a912-52b2-462a-c6d4-84fd4a777567"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(max_depth=4, n_estimators=400)"
            ]
          },
          "metadata": {},
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_result = xgb_model.predict(test_predict_inc)\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "accuracy = np.round(accuracy_score(xgb_result , test_label_inc),3)\n",
        "recall = np.round(recall_score(xgb_result,test_label_inc),3)\n",
        "precision = np.round(precision_score(xgb_result ,test_label_inc),3)\n",
        "f1 = np.round(f1_score(xgb_result ,test_label_inc),3)\n",
        "\n",
        "print(\"=================GAN모델+xgboost=====================\")\n",
        "print(\"증가 패턴 수정 종가에 대한 증가 감소 예측 정확도 평가\")\n",
        "print(\"(1) accuracy score = \",  accuracy)\n",
        "print(\"(2) recall score = \",  recall)\n",
        "print(\"(3) precision score = \", precision)\n",
        "print(\"(4) f1 score = \",  f1 )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kIIvePBiHQ4c",
        "outputId": "2f4b2069-bfcf-4f51-d3e0-94b75df5beef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=================GAN모델+xgboost=====================\n",
            "증가 패턴 수정 종가에 대한 증가 감소 예측 정확도 평가\n",
            "(1) accuracy score =  0.571\n",
            "(2) recall score =  0.594\n",
            "(3) precision score =  0.641\n",
            "(4) f1 score =  0.617\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_result = xgb_model.predict(test_predict_dec)\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "accuracy = np.round(accuracy_score(xgb_result , test_label_dec),3)\n",
        "recall = np.round(recall_score(xgb_result,test_label_dec),3)\n",
        "precision = np.round(precision_score(xgb_result ,test_label_dec),3)\n",
        "f1 = np.round(f1_score(xgb_result ,test_label_dec),3)\n",
        "\n",
        "print(\"==================GAN모델+xgboost=====================\")\n",
        "print(\"감소 패턴 수정 종가에 대한 증가 감소 예측 정확도 평가\")\n",
        "print(\"(1) accuracy score = \",  accuracy)\n",
        "print(\"(2) recall score = \",  recall)\n",
        "print(\"(3) precision score = \", precision)\n",
        "print(\"(4) f1 score = \",  f1 )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WlNZwPEyHTB1",
        "outputId": "c012a670-749e-46e7-a5db-82ce718ce56a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==================GAN모델+xgboost=====================\n",
            "감소 패턴 수정 종가에 대한 증가 감소 예측 정확도 평가\n",
            "(1) accuracy score =  0.615\n",
            "(2) recall score =  0.565\n",
            "(3) precision score =  0.716\n",
            "(4) f1 score =  0.632\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "tyDH2FRVShjl"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}