[LECTURE]kohi2022_ppf_bis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yy0WHpW3X9oX"
      },
      "source": [
        "# 약물 효과 예측 인공지능 모델 실습\n",
        "프로포폴, 레미펜타닐 투여 후 마취 심도(bispectral index) 예측 모델"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9SwIjOX9frk6"
      },
      "source": [
        "## 본 프로그램에서 이용할 라이브러리 설치 및 import"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ch6czkFZfw_G"
      },
      "source": [
        "!pip install vitaldb\n",
        "\n",
        "import vitaldb\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZjxFdZTXBZWb"
      },
      "source": [
        "# Data loading"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AabTJc6dflSy"
      },
      "source": [
        "##VitalDB Web API를 통해 데이터 로딩\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_trks = pd.read_csv('https://api.vitaldb.net/trks')\n",
        "df_cases = pd.read_csv(\"https://api.vitaldb.net/cases\")"
      ],
      "metadata": {
        "id": "HYLisyUb5TAU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Track DataFrame확인    ## .head(), .tail()도 사용가능.\n",
        "df_trks"
      ],
      "metadata": {
        "id": "576gN5jL5WWw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Track name 확인\n",
        "df_trks[df_trks.caseid == 1].tname\n",
        "\n",
        "## same as..\n",
        "#df_trks[df_trks['caseid'] == 1]['tname']"
      ],
      "metadata": {
        "id": "exb44MvU7iPN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 전체 목록 확인\n",
        "df_trks[df_trks.caseid == 1].tname.tolist()\n",
        "\n",
        "##same as,\n",
        "#print(df_trks[df_trks.caseid == 1].tname.tolist())"
      ],
      "metadata": {
        "id": "KHEI1_Hg8IDn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Cases DataFrame확인\n",
        "df_cases"
      ],
      "metadata": {
        "id": "aIUEfO8W5d3e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Cases DataFrame의 column명들만 추출해서 확인\n",
        "df_cases.columns"
      ],
      "metadata": {
        "id": "IJiGFn7k9eGB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0b_TyfelWg6e"
      },
      "source": [
        "# female은 1으로, male 은 0으로 치환\n",
        "df_cases['sex'] = (df_cases['sex'] == 'F').astype(int)  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xzuBetNgW5xv"
      },
      "source": [
        "##Case ID 선택 (inclusion, exclusion criteria)\n",
        "- 2시간 이상\n",
        "- 전신 마취\n",
        "- 18세 이상\n",
        "- PPF20_VOL, RFTN20_VOL, PPF20_CE, RFTN20_CE, BIS 트랙이 존재\n",
        "- 몸무게 35 kg 이상"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qsz5TKVf6Nb"
      },
      "source": [
        "caseids = list(\n",
        "  set(df_trks.loc[df_trks['tname'] == 'Orchestra/PPF20_VOL', 'caseid']) & ## loc를 사용하는게 아래보다 빠름.\n",
        "  set(df_trks[df_trks['tname'] == 'Orchestra/RFTN20_VOL']['caseid']) &\n",
        "  set(df_trks[df_trks['tname'] == 'Orchestra/PPF20_CE']['caseid']) &\n",
        "  set(df_trks[df_trks['tname'] == 'Orchestra/RFTN20_CE']['caseid']) &\n",
        "  set(df_trks[df_trks['tname'] == 'BIS/BIS']['caseid']) & \n",
        "  set(df_cases[df_cases['age'] > 18]['caseid']) & \n",
        "  set(df_cases[df_cases['weight'] > 35]['caseid']) &\n",
        "  set(df_cases[df_cases['caseend'] > 7200]['caseid']) &   ## 2시간 이상의 수술.\n",
        "  set(df_cases[df_cases['ane_type'] == 'General']['caseid'])  ## 전신 마취\n",
        "  )\n",
        "np.random.shuffle(caseids)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Included cases numeber: {len(caseids)}')"
      ],
      "metadata": {
        "id": "mU7I7ybp-KDm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aBusNmsKpc2Z"
      },
      "source": [
        "# 데이터 전처리\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 하나의 Caseid로 먼저 실습 "
      ],
      "metadata": {
        "id": "Ibxg8nPY0sgE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "caseid = 5180 #5180"
      ],
      "metadata": {
        "id": "gBNmL8rs0_BZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 20초 간격으로 Propofol 및 BIS 데이터 추출\n",
        "tracknames = ['Orchestra/PPF20_VOL', 'BIS/BIS']\n",
        "vals = vitaldb.load_case(caseid, tracknames, 20)\n",
        "\n",
        "print(vals.shape) # Matrix shape 확인\n",
        "print()\n",
        "print(vals[:10]) # 데이터 일부 확인"
      ],
      "metadata": {
        "id": "sPOkPyQV067Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 시각화\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(20,5))\n",
        "for idx, trk in enumerate(tracknames):\n",
        "  plt.plot(vals[:,idx],label=trk)\n",
        "plt.legend(loc='center right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "LMKtjwIiG6my",
        "outputId": "f4ffb36b-85a4-4fba-fc5f-4182d207b678"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 결측치 확인\n",
        "print(np.isnan(vals).sum(axis=0)) # track\n",
        "print()\n",
        "np.where(np.isnan(vals).sum(axis=1) > 0)[0] #time"
      ],
      "metadata": {
        "id": "JwQhjYp-4RSz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# track - time별 결측치 확인\n",
        "for idx, trk in enumerate(tracknames):\n",
        "  print(f' Missing values of {trk} track at : {np.where(np.isnan(vals[:, idx]))[0]}')\n",
        "  print()"
      ],
      "metadata": {
        "id": "QLhG3kzn6oDV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#  결측치 처리 방법1 - 전후 값으로 치환\n",
        "vals_ = vals[10:20]\n",
        "print(\"#\"*4, \"Original\", \"#\"*4)\n",
        "print(vals_)\n",
        "print()\n",
        "print(\"#\"*4, \"Forward filling\", \"#\"*4)\n",
        "print(pd.DataFrame(vals_).fillna(method='ffill').values)\n",
        "print()\n",
        "print(\"#\"*4, \"Backward filling\", \"#\"*4)\n",
        "print(pd.DataFrame(vals_).fillna(method='bfill').values)\n",
        "print()\n",
        "#  결측치 처리 방법2 - 전후 값으로 치환\n",
        "print(\"#\"*4, \"Interpolation\", \"#\"*4) \n",
        "print(pd.DataFrame(vals_).interpolate().values)\n"
      ],
      "metadata": {
        "id": "BS-bF0-V7eTX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#  결측치 처리 방법3 - 특정값으로 치환\n",
        "print(np.nan_to_num(vals_, 0))"
      ],
      "metadata": {
        "id": "55J1gSay_WX0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 전체 실행 (TODO)\n",
        "- dataset을 담을 변수를 설정\n",
        "- propofol(ppf20), remifentanil(rftn20), bis를 로딩한다\n",
        "- 기록된 데이터가 짧거나, drug infusion이 실제 없었던 케이스, bis값이 적절하지 않은 케이스는 거르기\n",
        "- 결측값 및 음수 처리"
      ],
      "metadata": {
        "id": "euUUMCTRzuhm"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6CHJpySrDxG"
      },
      "source": [
        "######### TODO 1 #########\n",
        "\n",
        "# vital 파일로부터 dataset 을 만듬\n",
        "x_ppf_dose = []  # 각 레코드의 프로포폴 주입량\n",
        "x_rft_dose = []  # 각 레코드의 레미펜타닐 주입량\n",
        "x_aswh = []  # 각 레코드의 나이, 성별, 키, 몸무게\n",
        "x_caseid = []  # 각 레코드의 caseid\n",
        "y = []  # 각 레코드의 출력값 (bis)\n",
        "y_old = []  # 고전적 모델의 예측값\n",
        "\n",
        "LSTM_TIMEPOINTS = 180\n",
        "MAX_CASES = 50  # 본 예제에서 사용할 최대 case 수\n",
        "\n",
        "# 데이터 로딩했을 때 컬럼 순서\n",
        "PPF_DOSE = 0\n",
        "RFT_DOSE = 1\n",
        "PPF_CE = 2\n",
        "RFT_CE = 3\n",
        "BIS = 4\n",
        "\n",
        "icase = 0  # 현재 로딩 중인 케이스 번호\n",
        "ncase = min(MAX_CASES, len(caseids))\n",
        "for caseid in caseids:  # 본 연구에 사용할 각 case에 대하여\n",
        "    print('loading {} ({}/{})'.format(caseid, icase, ncase), end='...', flush=True)\n",
        "\n",
        "    ######### TODO 1 : Step 1 #########\n",
        "    # 10초 간격으로, propofol volume, remifentanil volume, propofol CE, remifentanil CE, BIS 데이터 추출\n",
        "    vals = #### INSERT CODE ####\n",
        "\n",
        "    ######### TODO 1 : Step 2 #########\n",
        "    # 결측값은 측정된 마지막 값으로 대체\n",
        "    vals = #### INSERT CODE ####\n",
        "\n",
        "    ######### TODO 1 : Step 3 #########\n",
        "    # 마지막 남은 맨 앞 쪽 결측값은 0으로 대체\n",
        "    vals = #### INSERT CODE ####\n",
        "\n",
        "    # drug 주입을 하지 않은 case 혹은 bis를 사용하지 않은 case는 제외\n",
        "    if (np.max(vals, axis=0) <= 1).any():\n",
        "        print('no drug infusion or bis')\n",
        "        continue\n",
        "\n",
        "    # drug infusion 시작 시간을 구하고 그 이전을 삭제\n",
        "    first_ppf_idx = np.where(vals[:, PPF_DOSE] > 1)[0][0]\n",
        "    first_rft_idx = np.where(vals[:, RFT_DOSE] > 1)[0][0]\n",
        "    first_drug_idx = min(first_ppf_idx, first_rft_idx)\n",
        "    vals = vals[first_drug_idx:, :]\n",
        "\n",
        "    # volume 을 rate로 변경\n",
        "    vals[1:, PPF_DOSE] -= vals[:-1, PPF_DOSE]\n",
        "    vals[1:, RFT_DOSE] -= vals[:-1, RFT_DOSE]\n",
        "    vals[0, PPF_DOSE] = 0\n",
        "    vals[0, RFT_DOSE] = 0\n",
        "\n",
        "    ######### TODO 1 : Step 4 #########\n",
        "    # 음수 값을 0으로 대체\n",
        "    vals[#### INSERT CODE #### ] = 0\n",
        "\n",
        "    # bis 값의 첫 값이 80 이하이거나 마지막 값이 70 이하인 case는 사용하지 않음\n",
        "    valid_bis_idx = np.where(vals[:, BIS] > 0)[0]\n",
        "    first_bis_idx = valid_bis_idx[0]\n",
        "    last_bis_idx = valid_bis_idx[-1]\n",
        "    if vals[first_bis_idx, BIS] < 80:\n",
        "        print('first bis < 80')\n",
        "        continue\n",
        "    if vals[last_bis_idx, BIS] < 70:\n",
        "        print('last bis < 70')\n",
        "        continue\n",
        "\n",
        "    # infusion 시작 전 LSTM_TIMEPOINTS 동안의 dose와 bis를 모두 0으로 세팅\n",
        "    vals = np.vstack((np.zeros((LSTM_TIMEPOINTS - 1, vals.shape[1])), vals))\n",
        "\n",
        "    ######### TODO 1 : Step 5 #########\n",
        "    # 현 case의 나이, 성별, 키, 몸무게를 가져옴\n",
        "    aswh = #### INSERT CODE ####\n",
        "    aswh = aswh.values.astype(float).flatten()\n",
        "\n",
        "    # case 시작 부터 종료까지 dataset 에 넣음\n",
        "    for irow in range(1, vals.shape[0] - LSTM_TIMEPOINTS - 1):\n",
        "        bis = vals[irow + LSTM_TIMEPOINTS, BIS]\n",
        "        if bis == 0:\n",
        "            continue\n",
        "\n",
        "        # 데이터셋에 입력값을 넣음\n",
        "        x_ppf_dose.append(vals[irow:irow + LSTM_TIMEPOINTS, PPF_DOSE])\n",
        "        x_rft_dose.append(vals[irow:irow + LSTM_TIMEPOINTS, RFT_DOSE])\n",
        "\n",
        "        x_aswh.append(aswh)\n",
        "        x_caseid.append(caseid)\n",
        "        y.append(bis / 100)\n",
        "\n",
        "        ppf_ce = vals[irow + LSTM_TIMEPOINTS, PPF_CE]\n",
        "        rft_ce = vals[irow + LSTM_TIMEPOINTS, RFT_CE]\n",
        "         \n",
        "        ug = (ppf_ce / 4.47 + rft_ce / 19.3)**1.43\n",
        "        y_old.append(0.98 - 0.98 * (ug / (1 + ug)))\n",
        "\n",
        "\n",
        "    # 사용할 case\n",
        "    print('done')\n",
        "    icase += 1\n",
        "    if icase >= ncase:\n",
        "        break\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NwwLDouezDLc"
      },
      "source": [
        "## 데이터셋 포맷 및 차원 변환"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHa2fte0zGEv"
      },
      "source": [
        "# 입력 데이터셋을 numpy array로 변경\n",
        "x_ppf_dose = np.array(x_ppf_dose)[..., None]  # LSTM 에 넣기 위해서는 3차원이어야 한다. 마지막 차원을 추가\n",
        "x_rft_dose = np.array(x_rft_dose)[..., None]\n",
        "x_aswh = np.array(x_aswh)\n",
        "y = np.array(y)\n",
        "y_old = np.array(y_old)\n",
        "x_caseid = np.array(x_caseid)\n",
        "\n",
        "# 최종적으로 로딩 된 caseid\n",
        "caseids = np.unique(x_caseid)\n",
        "\n",
        "# normalize data\n",
        "x_aswh = (x_aswh - np.mean(x_aswh, axis=0)) / np.std(x_aswh, axis=0)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VpMowosc0Bzf"
      },
      "source": [
        "## 데이터를 학습(train)과 테스트(test)로 나누기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AjdvjsJ10JAe"
      },
      "source": [
        "# train, test case로 나눔\n",
        "ntest = int(ncase * 0.1)\n",
        "ntrain = ncase - ntest\n",
        "train_caseids = caseids[:ntrain]\n",
        "test_caseids = caseids[ntrain:ncase]\n",
        "\n",
        "# train set과 test set 으로 나눔\n",
        "train_mask = np.array([caseid in train_caseids for caseid in x_caseid])\n",
        "test_mask = np.array([caseid in test_caseids for caseid in x_caseid])\n",
        "x_train = [x_ppf_dose[train_mask], x_rft_dose[train_mask], x_aswh[train_mask]]\n",
        "y_train = y[train_mask]\n",
        "x_test = [x_ppf_dose[test_mask], x_rft_dose[test_mask], x_aswh[test_mask]]\n",
        "y_test = y[test_mask]\n",
        "y_old = y_old[test_mask]\n",
        "\n",
        "print('train: {} cases {} samples, testing: {} cases {} samples'.format(len(train_caseids), np.sum(train_mask), len(test_caseids), np.sum(test_mask)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iPi6FIbB0NkF"
      },
      "source": [
        "# Model building\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Model, load_model\n",
        "from keras.layers import Dense, Dropout, LSTM, Input, concatenate\n",
        "from keras.callbacks import EarlyStopping\n",
        "import tensorflow as tf\n",
        "\n",
        "# 모델 설계\n",
        "\n",
        "def build_model(LSTM_NODES, FNN_NODES, DROP_RATE):\n",
        "    \n",
        "    input_cov = Input(batch_shape=(None, 4))\n",
        "    input_ppf = Input(batch_shape=(None, LSTM_TIMEPOINTS, 1))\n",
        "    input_rft = Input(batch_shape=(None, LSTM_TIMEPOINTS, 1))\n",
        "    output_ppf = LSTM(LSTM_NODES, input_shape=(LSTM_TIMEPOINTS, 1), activation='tanh')(input_ppf)\n",
        "    output_rft = LSTM(LSTM_NODES, input_shape=(LSTM_TIMEPOINTS, 1), activation='tanh')(input_rft)\n",
        "    output = concatenate([output_ppf, output_rft, input_cov])\n",
        "    output = Dense(FNN_NODES)(output)\n",
        "    output = Dropout(DROP_RATE)(output)\n",
        "    output = Dense(1, activation='sigmoid')(output)\n",
        "    model = Model(inputs=[input_ppf, input_rft, input_cov], outputs=[output])\n",
        "    \n",
        "    return model\n"
      ],
      "metadata": {
        "id": "nstrZVa3WIez"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델 생성 및 구조 확인\n",
        "model = build_model(32, 32, 0.5)\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "2QDgyGNwXBY3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uytYU2Fu0rPi"
      },
      "source": [
        "mse = tf.keras.losses.MeanSquaredError()\n",
        "\n",
        "model.compile(loss=mse, optimizer='sgd', metrics=[mse])\n",
        "hist = model.fit(x_train, y_train,\n",
        "                 batch_size=64,\n",
        "                 validation_split=0.2, \n",
        "                 epochs=1, \n",
        "                 callbacks=[EarlyStopping(monitor='val_loss', patience=3, verbose=0, mode='auto')])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SeVpuglt1W0k"
      },
      "source": [
        "# 결과 출력"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNA1cUY71cxs"
      },
      "source": [
        "# 예측\n",
        "y_pred = model.predict(x_test).flatten()\n",
        "print(f\"Test MAPE (LSTM): {mape(y_test, y_pred):.1f}%\")\n",
        "print(f\"Test MAPE (Short): {mape(y_old, y_pred):.1f}%\")\n",
        "print()\n",
        "\n",
        "# 각 case에서 예측 결과를 그림으로 확인\n",
        "import matplotlib.pyplot as plt\n",
        "for caseid in test_caseids:\n",
        "    case_mask = (x_caseid[test_mask] == caseid)\n",
        "    case_len = np.sum(case_mask)\n",
        "    if case_len == 0:\n",
        "        continue\n",
        "\n",
        "    print('CaseID {}, LSTM MAPE={}, Short MAPE={}'.format(caseid, \n",
        "      mape(y_test[case_mask], y_pred[case_mask]), \n",
        "      mape(y_test[case_mask], y_old[case_mask])\n",
        "      ))\n",
        "\n",
        "    t = np.arange(0, case_len)\n",
        "    plt.figure(figsize=(20, 5))\n",
        "    plt.plot(t, y_test[case_mask], label='BIS')  # 측정 결과\n",
        "    plt.plot(t, y_pred[case_mask], label='LSTM')  # LSTM model\n",
        "    plt.plot(t, y_old[case_mask], label='Short')  # Short model\n",
        "    plt.legend()\n",
        "    plt.xlim([0, case_len])\n",
        "    plt.ylim([0, 1])\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 모델 학습 및 추론 (TODO)"
      ],
      "metadata": {
        "id": "zsjvmOFNcJ3r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "######### TODO 2 #########\n",
        "# 모델 설계 및 학습\n",
        "\n",
        "######### TODO 2 : Step 1 #########\n",
        "# LSTM의 node 수 8, FNN의 node 수 16, dropout rate는 0.2인 모델을 만들어주세요.\n",
        "model = #### INSERT CODE ####\n",
        "\n",
        "######### TODO 2 : Step 2 #########\n",
        "# 손실함수는 mean absolute error, 옵티마이저는 Adam, 평가방법은 mean absolute percentage error로 모델을 컴파일 해주세요.\n",
        "mae = #### INSERT CODE ####\n",
        "mape = #### INSERT CODE ####\n",
        "\n",
        "model.compile(#### INSERT CODE ####)\n",
        "\n",
        "######### TODO 2 : Step 3 #########\n",
        "# 모델 검증을 위해 10%로 split해서 미니배치 크기는 256으로 모델을 epoch은 10회로 학습해주세요.\n",
        "hist = model.fit(x_train, y_train,\n",
        "                 #### INSERT CODE ####\n",
        "                 )\n"
      ],
      "metadata": {
        "id": "oMruV58jYDh-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 예측\n",
        "y_pred = model.predict(x_test).flatten()\n",
        "print(f\"Test MAPE (LSTM): {mape(y_test, y_pred):.1f}%\")\n",
        "print(f\"Test MAPE (Short): {mape(y_old, y_pred):.1f}%\")\n",
        "print()\n",
        "\n",
        "# 각 case에서 예측 결과를 그림으로 확인\n",
        "import matplotlib.pyplot as plt\n",
        "for caseid in test_caseids:\n",
        "    case_mask = (x_caseid[test_mask] == caseid)\n",
        "    case_len = np.sum(case_mask)\n",
        "    if case_len == 0:\n",
        "        continue\n",
        "\n",
        "    print('CaseID {}, LSTM MAPE={}, Short MAPE={}'.format(caseid, \n",
        "      mape(y_test[case_mask], y_pred[case_mask]), \n",
        "      mape(y_test[case_mask], y_old[case_mask])\n",
        "      ))\n",
        "\n",
        "    t = np.arange(0, case_len)\n",
        "    plt.figure(figsize=(20, 5))\n",
        "    plt.plot(t, y_test[case_mask], label='BIS')  # 측정 결과\n",
        "    plt.plot(t, y_pred[case_mask], label='LSTM')  # LSTM model\n",
        "    plt.plot(t, y_old[case_mask], label='Short')  # Short model\n",
        "    plt.legend()\n",
        "    plt.xlim([0, case_len])\n",
        "    plt.ylim([0, 1])\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "B1wIesXEI3oa"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}