배터리 이상탐지_랜덤포레스트.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "A100"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j1b6YRlTnp9X",
        "outputId": "45dc38d6-8ebb-4909-d981-dd3019e244f3"
      },
      "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"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import glob\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "WBBsV3gtnr_c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#train_data 불러오기"
      ],
      "metadata": {
        "id": "Wb_YizN65qbA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def unify_96_to_98(data):\n",
        "    \"\"\"\n",
        "    (n, 96) 형태의 데이터를 (n, 98)로 변환.\n",
        "    각 행(row)에 대해 평균값 2개를 추가.\n",
        "    data: (n, 96) 또는 (n, 98)\n",
        "    return: (n, 98)\n",
        "    \"\"\"\n",
        "    if data.shape[1] == 96:\n",
        "        # 각 행의 평균값 계산\n",
        "        row_mean = np.mean(data, axis=1, keepdims=True)  # (n, 1)\n",
        "        # 평균값을 2개로 확장\n",
        "        two_means = np.hstack([row_mean, row_mean])       # (n, 2)\n",
        "        # 기존 데이터 + 평균값 2개\n",
        "        data_98 = np.hstack([data, two_means])           # (n, 98)\n",
        "        return data_98\n",
        "    elif data.shape[1] == 98:\n",
        "        # 이미 98이면 그대로 반환\n",
        "        return data\n"
      ],
      "metadata": {
        "id": "i5zlxRsbntFC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tqdm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XFOwAMAdAWru",
        "outputId": "7a065099-0dff-40a2-a644-580aacc663e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import glob\n",
        "import numpy as np\n",
        "import random\n",
        "from tqdm import tqdm  # tqdm 임포트\n",
        "\n",
        "# 📌 Google Drive 내 최상위 폴더 경로\n",
        "base_dir = \"/content/drive/MyDrive/DATA/training/01.원천데이터\"\n",
        "# 폴더 구조 정의\n",
        "folders = {\n",
        "    \"Dev_96/NPY/IONIQ\": [\"NORMAL\", \"CAUTION\", \"DEFECT\"],\n",
        "    \"Dev_98/NPY/KONA\":  [\"NORMAL\", \"CAUTION\", \"DEFECT\"],\n",
        "    \"Dev_98/NPY/NIRO\":  [\"NORMAL\", \"CAUTION\", \"DEFECT\"]\n",
        "}\n",
        "\n",
        "# 라벨별 데이터 저장용 (모두 (n, 98) 형태로 통일)\n",
        "normal_data  = []\n",
        "caution_data = []\n",
        "defect_data  = []\n",
        "\n",
        "label_mapping = {\"normal\":0, \"caution\":1, \"defect\":2}\n",
        "\n",
        "for main_folder, sub_folders in folders.items():\n",
        "    for sub_folder in sub_folders:\n",
        "        folder_path = os.path.join(base_dir, main_folder, sub_folder)\n",
        "        file_list = sorted(glob.glob(os.path.join(folder_path, \"**\", \"*.npy\"), recursive=True))\n",
        "\n",
        "        print(f\"[{main_folder}/{sub_folder}] 파일 개수:\", len(file_list))\n",
        "\n",
        "        # tqdm으로 파일 처리 상황 표시\n",
        "        for file_path in tqdm(file_list, desc=f\"Processing {main_folder}/{sub_folder}\"):\n",
        "            data = np.load(file_path)  # (n, 96) or (n, 98)\n",
        "            if data.size == 0:\n",
        "                print(f\"⚠️ {file_path} 파일이 비어있습니다. 스킵.\")\n",
        "                continue\n",
        "\n",
        "            # 96→98 변환\n",
        "            data_98 = unify_96_to_98(data)  # (n, 98)\n",
        "\n",
        "            # 라벨별 리스트에 쌓기\n",
        "            label = sub_folder.lower()  # normal, caution, defect\n",
        "            if label == \"normal\":\n",
        "                normal_data.append(data_98)\n",
        "            elif label == \"caution\":\n",
        "                caution_data.append(data_98)\n",
        "            elif label == \"defect\":\n",
        "                defect_data.append(data_98)\n",
        "            else:\n",
        "                print(f\"⚠️ 알 수 없는 라벨: {sub_folder}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ui_nNBN_nug3",
        "outputId": "b24686a2-bf7d-488e-ab69-f74b1325052c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_96/NPY/IONIQ/NORMAL] 파일 개수: 9732\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_96/NPY/IONIQ/NORMAL: 100%|██████████| 9732/9732 [00:25<00:00, 379.17it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_96/NPY/IONIQ/CAUTION] 파일 개수: 9800\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_96/NPY/IONIQ/CAUTION: 100%|██████████| 9800/9800 [37:44<00:00,  4.33it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_96/NPY/IONIQ/DEFECT] 파일 개수: 9844\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_96/NPY/IONIQ/DEFECT: 100%|██████████| 9844/9844 [33:31<00:00,  4.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_98/NPY/KONA/NORMAL] 파일 개수: 9745\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_98/NPY/KONA/NORMAL: 100%|██████████| 9745/9745 [32:35<00:00,  4.98it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_98/NPY/KONA/CAUTION] 파일 개수: 9886\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_98/NPY/KONA/CAUTION: 100%|██████████| 9886/9886 [39:02<00:00,  4.22it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_98/NPY/KONA/DEFECT] 파일 개수: 9790\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_98/NPY/KONA/DEFECT: 100%|██████████| 9790/9790 [39:23<00:00,  4.14it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_98/NPY/NIRO/NORMAL] 파일 개수: 9770\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_98/NPY/NIRO/NORMAL: 100%|██████████| 9770/9770 [29:45<00:00,  5.47it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_98/NPY/NIRO/CAUTION] 파일 개수: 9732\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_98/NPY/NIRO/CAUTION: 100%|██████████| 9732/9732 [37:16<00:00,  4.35it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_98/NPY/NIRO/DEFECT] 파일 개수: 9829\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_98/NPY/NIRO/DEFECT: 100%|██████████| 9829/9829 [35:37<00:00,  4.60it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def concat_label_data(data_list):\n",
        "    \"\"\"\n",
        "    data_list: [ (n1, 98), (n2, 98), ... ]\n",
        "    return: (N, 98) 하나의 배열\n",
        "    \"\"\"\n",
        "    if len(data_list) == 0:\n",
        "        return np.empty((0, 98))  # 빈 배열\n",
        "    return np.concatenate(data_list, axis=0)  # (N, 98)\n",
        "\n",
        "normal_arr  = concat_label_data(normal_data)   # (N_normal,  98)\n",
        "caution_arr = concat_label_data(caution_data)  # (N_caution, 98)\n",
        "defect_arr  = concat_label_data(defect_data)   # (N_defect,  98)\n",
        "\n",
        "print(\"normal_arr shape:\",  normal_arr.shape)\n",
        "print(\"caution_arr shape:\", caution_arr.shape)\n",
        "print(\"defect_arr shape:\",  defect_arr.shape)\n"
      ],
      "metadata": {
        "id": "5zYFtH9fn0QQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a27ae8c8-aa5f-4f89-8f0a-640313a74ff9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "normal_arr shape: (770977, 98)\n",
            "caution_arr shape: (584051, 98)\n",
            "defect_arr shape: (584498, 98)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.save(\"/content/drive/MyDrive/list/train_normal_arr.npy\", normal_arr)\n",
        "np.save(\"/content/drive/MyDrive/list/train_caution_arr.npy\", caution_arr)\n",
        "np.save(\"/content/drive/MyDrive/list/train_defect_arr.npy\", defect_arr)"
      ],
      "metadata": {
        "id": "_EAIl2QP_koU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "normal_arr = np.load(\"/content/drive/MyDrive/list/train_normal_arr.npy\")\n",
        "caution_arr = np.load(\"/content/drive/MyDrive/list/train_caution_arr.npy\")\n",
        "defect_arr = np.load(\"/content/drive/MyDrive/list/train_defect_arr.npy\")"
      ],
      "metadata": {
        "id": "PRKvTX3we_f2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#모델구축"
      ],
      "metadata": {
        "id": "j6eHsrUIWT7z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# 슬라이딩 윈도우 생성 함수\n",
        "def create_sliding_windows(arr, window_size=100, step=30):\n",
        "    n_rows = arr.shape[0]\n",
        "    windows = []\n",
        "    for i in range(0, n_rows - window_size + 1, step):\n",
        "        windows.append(arr[i:i+window_size])\n",
        "    return np.array(windows)\n",
        "\n",
        "# 각 배열에 대해 슬라이딩 윈도우 적용\n",
        "normal_windows = create_sliding_windows(normal_arr, window_size=100, step=30)   # shape: (192, 100, 98)\n",
        "caution_windows = create_sliding_windows(caution_arr, window_size=100, step=30)   # shape: (197, 100, 98)\n",
        "defect_windows = create_sliding_windows(defect_arr, window_size=100, step=30)     # shape: (198, 100, 98)\n",
        "\n",
        "# 각 데이터별 라벨 생성\n",
        "normal_labels = np.zeros(len(normal_windows))         # 정상: 0, 길이: 192\n",
        "caution_labels = np.ones(len(caution_windows))          # 주의: 1, 길이: 197\n",
        "defect_labels = np.full(len(defect_windows), 2)           # 결함: 2, 길이: 198\n",
        "\n",
        "# 모든 윈도우 데이터를 concat (axis=0)하여 하나의 X 배열 생성\n",
        "X_train = np.concatenate([normal_windows, caution_windows, defect_windows], axis=0)\n",
        "# 모든 라벨을 concat하여 하나의 y 배열 생성\n",
        "y_train = np.concatenate([normal_labels, caution_labels, defect_labels], axis=0)\n",
        "\n",
        "print(\"최종 X shape:\", X_train.shape)\n",
        "print(\"최종 y shape:\", y_train.shape)\n",
        "\n"
      ],
      "metadata": {
        "id": "Ip85c7won3kl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6853739d-b650-4d5f-8a41-c86f4156e000"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "최종 X shape: (64642, 100, 98)\n",
            "최종 y shape: (64642,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# X의 shape: (전체 윈도우 수, 100, 98)\n",
        "n_samples, win_size, num_features = X_train.shape\n",
        "# 2차원 입력으로 변환: (전체 윈도우 수, 100*98)\n",
        "X_train = X_train.reshape(n_samples, win_size * num_features)"
      ],
      "metadata": {
        "id": "142OvgC-9QOr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 랜덤 포레스트 모델의 파라미터를 개별 변수로 지정\n",
        "n_estimators = 50\n",
        "max_depth = 10\n",
        "min_samples_split = 2\n",
        "min_samples_leaf = 1\n",
        "criterion = \"gini\"\n",
        "random_state = 42\n",
        "\n",
        "# 랜덤 포레스트 분류 모델 생성\n",
        "clf = RandomForestClassifier(\n",
        "    n_estimators=n_estimators,\n",
        "    max_depth=max_depth,\n",
        "    min_samples_split=min_samples_split,\n",
        "    min_samples_leaf=min_samples_leaf,\n",
        "    criterion=criterion,\n",
        "    random_state=random_state\n",
        ")\n",
        "\n",
        "# 전체 데이터를 이용하여 모델 학습\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "print(\"모델 학습 완료!\")\n"
      ],
      "metadata": {
        "id": "1TAlBb65pVii",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72d2dde4-9011-41e6-e0e5-106e0399d9b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "모델 학습 완료!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#XAI"
      ],
      "metadata": {
        "id": "zALJm-bVWbDV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# shap_values.shape == (64642, 9800, 3)\n",
        "# X_train_df.shape  == (64642, 9800)\n",
        "\n",
        "class_index = 2  # 보고 싶은 클래스(0,1,2 중 선택)\n",
        "\n",
        "# 세 번째 축(axis=2)에서 class_index를 선택 -> (64642, 9800)\n",
        "shap_values_for_class = shap_values[:, :, class_index]\n",
        "\n",
        "print(\"shap_values_for_class shape:\", shap_values_for_class.shape)\n",
        "# 결과: (64642, 9800) -> X_train_df와 동일\n",
        "\n",
        "# summary_plot 실행\n",
        "shap.summary_plot(shap_values_for_class, X_train_df, feature_names=feature_names, max_display=20)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 976
        },
        "id": "x9A_stMQYdh9",
        "outputId": "bdbec5fd-67c0-496e-8587-894111f4f94e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shap_values_for_class shape: (64642, 9800)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x950 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shap\n",
        "\n",
        "# 1. 다중 클래스 중에서 보고 싶은 클래스 선택 (예: 클래스 1)\n",
        "class_index = 2  # (0, 1, 2 중에서 골라주세요)\n",
        "\n",
        "# 2. (n_samples, n_features, n_classes)에서 세 번째 축(axis=2)으로 클래스 추출\n",
        "#    → (n_samples, n_features) 형태\n",
        "shap_values_for_class = shap_values[:, :, class_index]\n",
        "print(\"shap_values_for_class shape:\", shap_values_for_class.shape)\n",
        "# 결과: (64642, 9800)  -> X_train_df.shape와 동일\n",
        "\n",
        "# 3. Waterfall Plot을 위해 특정 샘플 선택 (예: sample_index=0)\n",
        "sample_index = 0\n",
        "sample_shap_values = shap_values_for_class[sample_index]  # (9800,)\n",
        "\n",
        "# 4. base_values도 해당 클래스에 맞춰 선택\n",
        "#    explainer.expected_value가 (3,) 형태라면, class_index=1에 해당하는 값만 사용\n",
        "base_value = explainer.expected_value[class_index]\n",
        "\n",
        "# 5. Waterfall Plot 실행\n",
        "shap.waterfall_plot(\n",
        "    shap.Explanation(\n",
        "        values=sample_shap_values,           # (9800,) → 선택된 샘플의 SHAP 값\n",
        "        base_values=base_value,              # 해당 클래스의 base_value\n",
        "        data=X_train_df.iloc[sample_index],  # 샘플의 원본 Feature (Series, shape=(9800,))\n",
        "        feature_names=feature_names          # Feature 이름 (9800개)\n",
        "    )\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636
        },
        "id": "Y_QNXZVQe0YI",
        "outputId": "782c6676-6cce-401f-d4ea-f662bef1ad69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shap_values_for_class shape: (64642, 9800)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x650 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#test_data 불러오기"
      ],
      "metadata": {
        "id": "Prf27xIY9HYV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import glob\n",
        "import numpy as np\n",
        "from tqdm import tqdm  # tqdm 임포트\n",
        "\n",
        "# 테스트 데이터가 저장된 폴더와 서브 폴더 지정\n",
        "base_dir = \"/content/drive/MyDrive/DATA/Validation/01.원천데이터\"\n",
        "# 폴더 구조 정의\n",
        "folders = {\n",
        "    \"Dev_96/NPY/IONIQ\": [\"NORMAL\", \"CAUTION\", \"DEFECT\"],\n",
        "    \"Dev_98/NPY/KONA\":  [\"NORMAL\", \"CAUTION\", \"DEFECT\"],\n",
        "    \"Dev_98/NPY/NIRO\":  [\"NORMAL\", \"CAUTION\", \"DEFECT\"]\n",
        "}\n",
        "\n",
        "# 라벨별 데이터 저장용 (모두 (n, 98) 형태로 통일)\n",
        "normal_data  = []\n",
        "caution_data = []\n",
        "defect_data  = []\n",
        "\n",
        "label_mapping = {\"normal\":0, \"caution\":1, \"defect\":2}\n",
        "\n",
        "for main_folder, sub_folders in folders.items():\n",
        "    for sub_folder in sub_folders:\n",
        "        folder_path = os.path.join(base_dir, main_folder, sub_folder)\n",
        "        file_list = sorted(glob.glob(os.path.join(folder_path, \"**\", \"*.npy\"), recursive=True))\n",
        "\n",
        "        print(f\"[{main_folder}/{sub_folder}] 파일 개수:\", len(file_list))\n",
        "\n",
        "        # tqdm으로 진행률 표시\n",
        "        for file_path in tqdm(file_list, desc=f\"Processing {main_folder}/{sub_folder}\"):\n",
        "            data = np.load(file_path)  # (n, 96) or (n, 98)\n",
        "            if data.size == 0:\n",
        "                print(f\"⚠️ {file_path} 파일이 비어있습니다. 스킵.\")\n",
        "                continue\n",
        "\n",
        "            # 96→98 변환 (구현에 맞게 수정)\n",
        "            data_98 = unify_96_to_98(data)  # (n, 98)\n",
        "\n",
        "            # 라벨별 리스트에 쌓기\n",
        "            label = sub_folder.lower()  # normal, caution, defect\n",
        "            if label == \"normal\":\n",
        "                normal_data.append(data_98)\n",
        "            elif label == \"caution\":\n",
        "                caution_data.append(data_98)\n",
        "            elif label == \"defect\":\n",
        "                defect_data.append(data_98)\n",
        "            else:\n",
        "                print(f\"⚠️ 알 수 없는 라벨: {sub_folder}\")\n"
      ],
      "metadata": {
        "id": "eiBPBXfWp4_N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c9c487e7-d01a-4e05-dce3-d1b475fb7b6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_96/NPY/IONIQ/NORMAL] 파일 개수: 1303\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_96/NPY/IONIQ/NORMAL: 100%|██████████| 1303/1303 [04:49<00:00,  4.50it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_96/NPY/IONIQ/CAUTION] 파일 개수: 1199\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_96/NPY/IONIQ/CAUTION: 100%|██████████| 1199/1199 [04:59<00:00,  4.00it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_96/NPY/IONIQ/DEFECT] 파일 개수: 1170\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_96/NPY/IONIQ/DEFECT: 100%|██████████| 1170/1170 [04:01<00:00,  4.84it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_98/NPY/KONA/NORMAL] 파일 개수: 1231\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_98/NPY/KONA/NORMAL: 100%|██████████| 1231/1231 [04:20<00:00,  4.73it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_98/NPY/KONA/CAUTION] 파일 개수: 1161\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_98/NPY/KONA/CAUTION: 100%|██████████| 1161/1161 [04:26<00:00,  4.36it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_98/NPY/KONA/DEFECT] 파일 개수: 1251\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_98/NPY/KONA/DEFECT: 100%|██████████| 1251/1251 [05:01<00:00,  4.15it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_98/NPY/NIRO/NORMAL] 파일 개수: 1235\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_98/NPY/NIRO/NORMAL: 100%|██████████| 1235/1235 [04:17<00:00,  4.79it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_98/NPY/NIRO/CAUTION] 파일 개수: 1288\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_98/NPY/NIRO/CAUTION: 100%|██████████| 1288/1288 [04:39<00:00,  4.61it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Dev_98/NPY/NIRO/DEFECT] 파일 개수: 1178\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Dev_98/NPY/NIRO/DEFECT: 100%|██████████| 1178/1178 [04:21<00:00,  4.50it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.save(\"/content/drive/MyDrive/list/test_val_normal_arr.npy\", test_normal_arr)\n",
        "np.save(\"/content/drive/MyDrive/list/test_val_caution_arr.npy\", test_caution_arr)\n",
        "np.save(\"/content/drive/MyDrive/list/test_val_defect_arr.npy\", test_defect_arr)"
      ],
      "metadata": {
        "id": "mld_zgpnVNuZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "normal_arr = np.load(\"/content/drive/MyDrive/list/test_normal_arr.npy\")\n",
        "caution_arr = np.load(\"/content/drive/MyDrive/list/test_caution_arr.npy\")\n",
        "defect_arr = np.load(\"/content/drive/MyDrive/list/test_defect_arr.npy\")"
      ],
      "metadata": {
        "id": "TBGPOK3cd36s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# 슬라이딩 윈도우 생성 함수\n",
        "def create_sliding_windows(arr, window_size=100, step=30):\n",
        "    n_rows = arr.shape[0]\n",
        "    windows = []\n",
        "    for i in range(0, n_rows - window_size + 1, step):\n",
        "        windows.append(arr[i:i+window_size])\n",
        "    return np.array(windows)\n",
        "\n",
        "# 각 배열에 대해 슬라이딩 윈도우 적용\n",
        "normal_windows = create_sliding_windows(normal_arr, window_size=100, step=30)   # shape: (192, 100, 98)\n",
        "caution_windows = create_sliding_windows(caution_arr, window_size=100, step=30)   # shape: (197, 100, 98)\n",
        "defect_windows = create_sliding_windows(defect_arr, window_size=100, step=30)     # shape: (198, 100, 98)\n",
        "\n",
        "# 각 데이터별 라벨 생성\n",
        "normal_labels = np.zeros(len(normal_windows))         # 정상: 0, 길이: 192\n",
        "caution_labels = np.ones(len(caution_windows))          # 주의: 1, 길이: 197\n",
        "defect_labels = np.full(len(defect_windows), 2)           # 결함: 2, 길이: 198\n",
        "\n",
        "# 모든 윈도우 데이터를 concat (axis=0)하여 하나의 X 배열 생성\n",
        "X_test = np.concatenate([normal_windows, caution_windows, defect_windows], axis=0)\n",
        "# 모든 라벨을 concat하여 하나의 y 배열 생성\n",
        "y_test = np.concatenate([normal_labels, caution_labels, defect_labels], axis=0)\n",
        "\n",
        "print(\"최종 X shape:\", X_test.shape)\n",
        "print(\"최종 y shape:\", y_test.shape)\n"
      ],
      "metadata": {
        "id": "xHUk9t2M8lbK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3bc770dd-ff64-4c1b-f38e-732797eb9d6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "최종 X shape: (8101, 100, 98)\n",
            "최종 y shape: (8101,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# X_test는 (n_windows, 100, 98)의 shape를 가지므로, 모델 입력에 맞게 flatten 처리\n",
        "X_test_flat = X_test.reshape(X_test.shape[0], -1)\n",
        "\n",
        "# 모델 예측\n",
        "y_pred = clf.predict(X_test_flat)\n",
        "\n",
        "# 성능 평가\n",
        "print(\"Test Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "id": "LZqb2We5u_0R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed650af7-b359-43a6-b546-4ba8ed7ee372"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy: 0.8456980619676583\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.73      1.00      0.85      3317\n",
            "         1.0       0.98      0.69      0.81      2408\n",
            "         2.0       1.00      0.78      0.88      2376\n",
            "\n",
            "    accuracy                           0.85      8101\n",
            "   macro avg       0.90      0.83      0.85      8101\n",
            "weighted avg       0.88      0.85      0.85      8101\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 랜덤 포레스트 모델의 파라미터를 개별 변수로 지정\n",
        "n_estimators = 500         # 트리 개수 (default: 100)\n",
        "max_depth = 100           # 트리의 최대 깊이 (default: None, 즉 제한 없음)\n",
        "min_samples_split = 200      # 내부 노드를 분할하기 위한 최소 샘플 수 (default: 2)\n",
        "min_samples_leaf = 100       # 리프 노드에 있어야 할 최소 샘플 수 (default: 1)\n",
        "criterion = \"gini\"      # 분할 기준 (\"gini\" 또는 \"entropy\")\n",
        "random_state = 42          # 랜덤 시드 (재현 가능성 확보)\n",
        "\n",
        "# 랜덤 포레스트 분류 모델 생성\n",
        "clf = RandomForestClassifier(\n",
        "    n_estimators=n_estimators,\n",
        "    max_depth=max_depth,\n",
        "    min_samples_split=min_samples_split,\n",
        "    min_samples_leaf=min_samples_leaf,\n",
        "    criterion=criterion,\n",
        "    random_state=random_state\n",
        ")\n",
        "\n",
        "# 전체 데이터를 이용하여 모델 학습\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "print(\"모델 학습 완료!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vPA949zOMRoF",
        "outputId": "5981489f-dbbb-485e-b88c-7a595ed7fe54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "모델 학습 완료!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# X_test는 (n_windows, 100, 98)의 shape를 가지므로, 모델 입력에 맞게 flatten 처리\n",
        "X_test_flat = X_test.reshape(X_test.shape[0], -1)\n",
        "\n",
        "# 모델 예측\n",
        "y_pred = clf.predict(X_test_flat)\n",
        "\n",
        "# 성능 평가\n",
        "print(\"Test Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nf1DcE-IMbul",
        "outputId": "98d6e618-d57c-46e5-82cf-4a25104b8137"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy: 0.9875324034069868\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.99      1.00      0.99      3317\n",
            "         1.0       0.98      0.99      0.99      2408\n",
            "         2.0       0.99      0.97      0.98      2376\n",
            "\n",
            "    accuracy                           0.99      8101\n",
            "   macro avg       0.99      0.99      0.99      8101\n",
            "weighted avg       0.99      0.99      0.99      8101\n",
            "\n"
          ]
        }
      ]
    }
  ]
}