chottokun/time_series_anomaly_detection.ipynb

## time_series_anomaly_detection.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyP8V9F7IOv1K4whxizq7veX",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/chottokun/7c1137424a860bc8c60b08b6da06c7b0/time_series_anomaly_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "25ecbd3c"
      },
      "source": [
        "このセルでは、必要なライブラリのインストールを行っています。\n",
        "時系列データ処理、ディープラーニングモデル構築、データ可視化、評価に必要なライブラリが含まれています。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "822c098e"
      },
      "source": [
        "%pip install -q tensorflow matplotlib scikit-learn pandas numpy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ab7ac7a8"
      },
      "source": [
        "このセルでは、分析に使用する合成時系列データを生成しています。\n",
        "サイン波にノイズを加えたデータを作成し、意図的に特定の期間（インデックス700から709）に異常値（+1）を加えています。\n",
        "Pandas DataFrameに変換し、タイムスタンプ列を追加しています。\n",
        "データの最初の数行を表示し、生成された時系列データをグラフで可視化しています。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 772
        },
        "id": "9453edb1",
        "outputId": "c94dd694-c1ff-4756-d92b-fa5e367f044a"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Generate synthetic time series data\n",
        "np.random.seed(42)\n",
        "time_steps = 1000\n",
        "data = np.sin(np.linspace(0, 100, time_steps)) + np.random.normal(0, 0.1, time_steps)\n",
        "data[700:710] += 1 # Introduce a small anomaly\n",
        "\n",
        "df = pd.DataFrame({'value': data})\n",
        "df['timestamp'] = pd.to_datetime(pd.Series(range(time_steps)), unit='s')\n",
        "\n",
        "# Display the first few rows and plot the data\n",
        "print(\"Sample Data:\")\n",
        "display(df.head())\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(df['timestamp'], df['value'])\n",
        "plt.title('Synthetic Time Series Data with Anomaly')\n",
        "plt.xlabel('Timestamp')\n",
        "plt.ylabel('Value')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sample Data:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      value           timestamp\n",
              "0  0.049671 1970-01-01 00:00:00\n",
              "1  0.086107 1970-01-01 00:00:01\n",
              "2  0.263634 1970-01-01 00:00:02\n",
              "3  0.448110 1970-01-01 00:00:03\n",
              "4  0.366372 1970-01-01 00:00:04"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-47ae48ec-70d0-4539-bc2c-cd8b425cbc7e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>value</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.049671</td>\n",
              "      <td>1970-01-01 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.086107</td>\n",
              "      <td>1970-01-01 00:00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.263634</td>\n",
              "      <td>1970-01-01 00:00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.448110</td>\n",
              "      <td>1970-01-01 00:00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.366372</td>\n",
              "      <td>1970-01-01 00:00:04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-47ae48ec-70d0-4539-bc2c-cd8b425cbc7e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-47ae48ec-70d0-4539-bc2c-cd8b425cbc7e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-47ae48ec-70d0-4539-bc2c-cd8b425cbc7e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-d5c15a29-ea51-4979-bf8f-5aeffe61d77d\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d5c15a29-ea51-4979-bf8f-5aeffe61d77d')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-d5c15a29-ea51-4979-bf8f-5aeffe61d77d button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"plt\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17299403953777462,\n        \"min\": 0.04967141530112327,\n        \"max\": 0.44811006680735643,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0861065860459208,\n          0.36637176680138717,\n          0.26363439014756784\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"timestamp\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00\",\n        \"max\": \"1970-01-01 00:00:04\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"1970-01-01 00:00:01\",\n          \"1970-01-01 00:00:04\",\n          \"1970-01-01 00:00:02\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6a748aea"
      },
      "source": [
        "このセルでは、時系列データをオートエンコーダーモデルで扱えるように前処理を行っています。\n",
        "まず、データをMinMaxScalerで正規化（スケーリング）しています。\n",
        "次に、`create_sequences`関数を定義し、元の時系列データから指定された長さ（`seq_length`）のシーケンスを作成しています。\n",
        "ここでは`seq_length`を50としています。\n",
        "データを訓練用（正常データ部分）とテスト用（異常を含む可能性のある部分）に分割しています。\n",
        "最後に、LSTMモデルの入力形式に合わせてデータをリシェイプしています。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5348ac89",
        "outputId": "4141430b-7b00-4c01-b96a-65ed810c52da"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# Prepare data for the Autoencoder\n",
        "# Autoencoders typically work best with scaled data\n",
        "scaler = MinMaxScaler()\n",
        "scaled_data = scaler.fit_transform(df['value'].values.reshape(-1, 1))\n",
        "\n",
        "# Create sequences for time series data\n",
        "def create_sequences(data, seq_length):\n",
        "    sequences = []\n",
        "    for i in range(len(data) - seq_length):\n",
        "        seq = data[i:(i + seq_length)]\n",
        "        sequences.append(seq)\n",
        "    return np.array(sequences)\n",
        "\n",
        "# Define sequence length (hyperparameter, can be tuned)\n",
        "seq_length = 50\n",
        "sequences = create_sequences(scaled_data, seq_length)\n",
        "\n",
        "# Split data into training and testing sets\n",
        "# Autoencoders are typically trained on normal data to learn its patterns\n",
        "# So, we'll split the *normal* part of the data for training\n",
        "# For simplicity, let's assume the first 700 data points are normal based on our synthetic data generation\n",
        "normal_data_indices = range(700 - seq_length)\n",
        "train_data = sequences[normal_data_indices]\n",
        "\n",
        "# We'll use the rest of the data for validation/testing, including the anomaly\n",
        "test_data = sequences[700 - seq_length:]\n",
        "\n",
        "# Reshape data for LSTM input (samples, time_steps, features)\n",
        "train_data = train_data.reshape(train_data.shape[0], train_data.shape[1], 1)\n",
        "test_data = test_data.reshape(test_data.shape[0], test_data.shape[1], 1)\n",
        "\n",
        "print(\"Training data shape:\", train_data.shape)\n",
        "print(\"Testing data shape:\", test_data.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training data shape: (650, 50, 1)\n",
            "Testing data shape: (300, 50, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "69fe6e5c"
      },
      "source": [
        "このセルでは、LSTMベースのオートエンコーダーモデルを定義し、構築しています。\n",
        "`create_autoencoder_model`関数でモデルのアーキテクチャを定義しています。エンコーダー部とデコーダー部からなり、時系列シーケンスを入力として、元のシーケンスを再構築することを目指します。\n",
        "モデルをAdamオプティマイザと平均二乗誤差（MSE）損失関数でコンパイルしています。\n",
        "モデルのサマリー（層の情報、パラメータ数など）を表示しています。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "2d804bbd",
        "outputId": "26563329-a00b-456a-bbf6-9dad43e9b683"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Dense, LSTM, RepeatVector, TimeDistributed\n",
        "\n",
        "# Define the Autoencoder model (as a starting point)\n",
        "def create_autoencoder_model(input_shape):\n",
        "    inputs = Input(shape=input_shape)\n",
        "    encoded = LSTM(128, activation='relu', return_sequences=True)(inputs)\n",
        "    encoded = LSTM(64, activation='relu', return_sequences=False)(encoded)\n",
        "    decoded = RepeatVector(input_shape[0])(encoded)\n",
        "    decoded = LSTM(64, activation='relu', return_sequences=True)(decoded)\n",
        "    decoded = LSTM(128, activation='relu', return_sequences=True)(decoded)\n",
        "    output = TimeDistributed(Dense(input_shape[1]))(decoded)\n",
        "\n",
        "    model = Model(inputs, output)\n",
        "    return model\n",
        "\n",
        "# Create and compile the model\n",
        "# The input shape should match the shape of a single sequence\n",
        "input_shape = (seq_length, 1)\n",
        "model = create_autoencoder_model(input_shape)\n",
        "model.compile(optimizer='adam', loss='mse')\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"functional\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m1\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m)        │        \u001b[38;5;34m66,560\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │        \u001b[38;5;34m49,408\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ repeat_vector (\u001b[38;5;33mRepeatVector\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m64\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m64\u001b[0m)         │        \u001b[38;5;34m33,024\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m128\u001b[0m)        │        \u001b[38;5;34m98,816\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ time_distributed                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m1\u001b[0m)          │           \u001b[38;5;34m129\u001b[0m │\n",
              "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)               │                        │               │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">66,560</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">49,408</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ repeat_vector (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">RepeatVector</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">33,024</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">98,816</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ time_distributed                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)               │                        │               │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m247,937\u001b[0m (968.50 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">247,937</span> (968.50 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m247,937\u001b[0m (968.50 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">247,937</span> (968.50 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e3b7a598"
      },
      "source": [
        "このセルでは、定義したオートエンコーダーモデルを訓練データ（正常データ）で学習させています。\n",
        "モデルは正常データのパターンを学習し、それを効率的に符号化・復号化できるようになります。\n",
        "学習中の損失（訓練損失と検証損失）を記録し、学習の進行状況を確認できるようにグラフで可視化しています。\n",
        "EarlyStoppingコールバックを使用し、検証損失の改善が見られない場合に学習を早期に終了させるように設定しています。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "c353c35d",
        "outputId": "9a9863d7-7f88-4d15-befa-af7a89336333"
      },
      "source": [
        "# Train the model\n",
        "# Train on the normal data\n",
        "history = model.fit(train_data, train_data,\n",
        "                    epochs=50, # You can adjust the number of epochs\n",
        "                    batch_size=32, # You can adjust the batch size\n",
        "                    validation_split=0.1, # Use a portion of training data for validation during training\n",
        "                    callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)],\n",
        "                    verbose=1)\n",
        "\n",
        "# Plot training history\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Model Loss during Training')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss (MSE)')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 616ms/step - loss: 0.1250 - val_loss: 0.0537\n",
            "Epoch 2/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0520 - val_loss: 0.0470\n",
            "Epoch 3/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0474 - val_loss: 0.0427\n",
            "Epoch 4/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0409 - val_loss: 0.0314\n",
            "Epoch 5/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.0296 - val_loss: 0.0285\n",
            "Epoch 6/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.0248 - val_loss: 0.0217\n",
            "Epoch 7/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.0197 - val_loss: 0.0159\n",
            "Epoch 8/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.0167 - val_loss: 0.0147\n",
            "Epoch 9/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.0124 - val_loss: 0.0071\n",
            "Epoch 10/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.0071 - val_loss: 0.0060\n",
            "Epoch 11/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.0053 - val_loss: 0.0035\n",
            "Epoch 12/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0031 - val_loss: 0.0027\n",
            "Epoch 13/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0023 - val_loss: 0.0023\n",
            "Epoch 14/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0019 - val_loss: 0.0017\n",
            "Epoch 15/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0014 - val_loss: 0.0015\n",
            "Epoch 16/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0013 - val_loss: 0.0013\n",
            "Epoch 17/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0011 - val_loss: 0.0013\n",
            "Epoch 18/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - loss: 0.0010 - val_loss: 0.0012\n",
            "Epoch 19/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 9.6660e-04 - val_loss: 0.0012\n",
            "Epoch 20/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - loss: 9.4086e-04 - val_loss: 0.0012\n",
            "Epoch 21/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 9.3734e-04 - val_loss: 0.0012\n",
            "Epoch 22/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - loss: 9.4328e-04 - val_loss: 0.0011\n",
            "Epoch 23/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 9.0268e-04 - val_loss: 0.0011\n",
            "Epoch 24/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 9.1333e-04 - val_loss: 0.0012\n",
            "Epoch 25/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 8.9816e-04 - val_loss: 0.0011\n",
            "Epoch 26/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 8.8498e-04 - val_loss: 0.0012\n",
            "Epoch 27/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 8.8415e-04 - val_loss: 0.0011\n",
            "Epoch 28/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 8.6730e-04 - val_loss: 0.0011\n",
            "Epoch 29/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - loss: 8.6108e-04 - val_loss: 0.0011\n",
            "Epoch 30/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 8.7382e-04 - val_loss: 0.0011\n",
            "Epoch 31/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 8.9875e-04 - val_loss: 0.0011\n",
            "Epoch 32/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 8.6676e-04 - val_loss: 0.0011\n",
            "Epoch 33/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 25ms/step - loss: 9.2711e-04 - val_loss: 0.0011\n",
            "Epoch 34/50\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 8.9080e-04 - val_loss: 0.0012\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2a094fec"
      },
      "source": [
        "このセルでは、異常検知のための再構築誤差を計算し、異常箇所を特定しています。\n",
        "テストデータに対してモデルの予測（再構築）を行い、元のテストデータと再構築されたデータの間の平均二乗誤差（MSE）を計算しています。これが異常スコアとなります。\n",
        "学習データでの再構築誤差の分布に基づき、異常と判定するための閾値を設定しています（ここでは95パーセンタイルを使用）。\n",
        "異常スコアが閾値を超えたデータポイントを異常として特定し、そのインデックスを表示しています。\n",
        "元のデータと検出された異常箇所、および時系列に沿った異常スコアをグラフで可視化しています。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1518f86a",
        "outputId": "84a13ca3-ba99-4d36-fe8e-f373a6070a20"
      },
      "source": [
        "# Calculate reconstruction error\n",
        "# Predict on the test data to get the reconstructed sequences\n",
        "reconstructed_test_data = model.predict(test_data)\n",
        "\n",
        "# Calculate the Mean Squared Error (MSE) for each time step in each sequence\n",
        "# This gives us an error for each point in the original time series,\n",
        "# considering its context within the sequence\n",
        "mse_errors = np.mean(np.square(test_data - reconstructed_test_data), axis=1)\n",
        "\n",
        "# Let's create a new array for anomaly scores that matches the length of the original data\n",
        "full_anomaly_scores = np.zeros(len(scaled_data)) * np.nan\n",
        "\n",
        "# The mse_errors array has length len(test_data)\n",
        "# The first error in mse_errors corresponds to the sequence starting at index 700 - seq_length = 650 in the original data.\n",
        "# This sequence ends at index 650 + seq_length - 1 = 650 + 50 - 1 = 699 in the original data.\n",
        "# So, mse_errors[0] is the anomaly score for original data point at index 699.\n",
        "# mse_errors[1] is the anomaly score for original data point at index 700.\n",
        "# ...\n",
        "# mse_errors[i] is the anomaly score for original data point at index 699 + i.\n",
        "\n",
        "# The indices in the original data where we have anomaly scores are from 699 to 699 + len(mse_errors) - 1\n",
        "# len(mse_errors) = len(test_data) = 300\n",
        "# So, the indices are from 699 to 699 + 300 - 1 = 998.\n",
        "\n",
        "# Assign the calculated MSE errors to the corresponding original data indices\n",
        "# The indices in the original data are from 699 to 998.\n",
        "start_index_for_scores = seq_length - 1 + (700 - seq_length)\n",
        "end_index_for_scores = seq_length - 1 + (700 - seq_length) + len(mse_errors) - 1\n",
        "\n",
        "full_anomaly_scores[start_index_for_scores : end_index_for_scores + 1] = mse_errors.flatten()\n",
        "\n",
        "# Now, let's identify potential anomalies based on a threshold\n",
        "# A simple approach is to use a threshold based on the distribution of errors on the training data\n",
        "# We'll calculate the errors on the training data as well to set a threshold\n",
        "reconstructed_train_data = model.predict(train_data)\n",
        "train_mse_errors = np.mean(np.square(train_data - reconstructed_train_data), axis=1)\n",
        "\n",
        "# Set a threshold (e.g., based on a percentile of training errors)\n",
        "threshold = np.percentile(train_mse_errors, 95) # Example: 95th percentile\n",
        "\n",
        "# Identify points with anomaly scores above the threshold\n",
        "anomalous_indices = np.where(full_anomaly_scores > threshold)[0]\n",
        "\n",
        "print(f\"Threshold for anomaly detection: {threshold}\")\n",
        "print(f\"Number of detected anomalous points: {len(anomalous_indices)}\")\n",
        "print(\"Detected anomalous indices (in original data):\", anomalous_indices)\n",
        "\n",
        "# Plot the original data and highlight the detected anomalies and the anomaly scores\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(df['timestamp'], df['value'], label='Original Data')\n",
        "plt.plot(df['timestamp'][anomalous_indices], df['value'][anomalous_indices], 'ro', markersize=5, label='Detected Anomalies')\n",
        "\n",
        "# Plot the anomaly scores (aligned with the original data timestamps)\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(df['timestamp'], full_anomaly_scores, label='Anomaly Score (MSE)')\n",
        "plt.axhline(y=threshold, color='r', linestyle='--', label=f'Threshold ({threshold:.4f})')\n",
        "plt.title('Anomaly Detection Results')\n",
        "plt.xlabel('Timestamp')\n",
        "plt.ylabel('Value / Anomaly Score')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 282ms/step\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 82ms/step\n",
            "Threshold for anomaly detection: 0.0014793736704077568\n",
            "Number of detected anomalous points: 70\n",
            "Detected anomalous indices (in original data): [700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717\n",
            " 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735\n",
            " 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753\n",
            " 754 755 756 757 758 884 885 886 887 888 889 890 892 894 895 896]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "91d81b37"
      },
      "source": [
        "このセルでは、モデルの再構築能力を視覚的に比較することで、異常検知の解釈を試みています。\n",
        "正常な時系列シーケンスと異常を含む時系列シーケンスをいくつかランダムに選択しています。\n",
        "それぞれのシーケンスに対してオートエンコーダーが再構築した結果を元のシーケンスとともにプロットしています。\n",
        "正常なシーケンスでは元の波形と再構築された波形が近いですが、異常なシーケンスでは大きな差（再構築誤差）が生じていることが視覚的に確認できます。これは、モデルが正常パターンを学習し、そこから逸脱したパターンを異常として捉えていることを示しています。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 770
        },
        "id": "c2343858",
        "outputId": "23a8d81a-791a-497c-cc7f-a649e6f2185f"
      },
      "source": [
        "# Visualize reconstruction differences for normal and anomalous data\n",
        "\n",
        "# Get reconstructed data for a few normal and anomalous sequences\n",
        "num_examples_to_show = 5\n",
        "\n",
        "# Find indices of some normal sequences (from the training data range)\n",
        "# Ensure indices are within the valid range of sequences and training data\n",
        "normal_indices_in_sequences = np.where(np.logical_and(np.arange(len(sequences)) >= 0, np.arange(len(sequences)) < (700 - seq_length)))[0]\n",
        "selected_normal_indices_in_sequences = np.random.choice(normal_indices_in_sequences, num_examples_to_show, replace=False)\n",
        "selected_normal_sequences = sequences[selected_normal_indices_in_sequences]\n",
        "reconstructed_normal_sequences = model.predict(selected_normal_sequences)\n",
        "\n",
        "# Find indices of some anomalous sequences (from the test data range where we know there's an anomaly)\n",
        "# Note: The indices here refer to the index within the 'sequences' array, not the original data index\n",
        "# Adjusting the range to more reliably pick from the known anomaly period (indices 700-710 in original data)\n",
        "# The sequences covering this anomaly period start from index 700 - seq_length up to 710 - seq_length\n",
        "anomalous_sequence_start_in_original = 700\n",
        "anomalous_sequence_end_in_original = 710\n",
        "anomalous_indices_in_sequences_candidates = np.arange(anomalous_sequence_start_in_original - seq_length + 1, anomalous_sequence_end_in_original - seq_length + 1)\n",
        "# Ensure selected indices are within the actual range of sequences that cover the anomaly\n",
        "valid_anomalous_indices_in_sequences = anomalous_indices_in_sequences_candidates[np.logical_and(anomalous_indices_in_sequences_candidates >= (700 - seq_length), anomalous_indices_in_sequences_candidates < len(sequences))]\n",
        "\n",
        "# If there are enough sequences covering the anomaly, sample from them. Otherwise, sample from available test sequences.\n",
        "if len(valid_anomalous_indices_in_sequences) >= num_examples_to_show:\n",
        "    selected_anomalous_indices_in_sequences = np.random.choice(valid_anomalous_indices_in_sequences, num_examples_to_show, replace=False)\n",
        "elif len(test_data) >= num_examples_to_show:\n",
        "     # Fallback: If not enough specific anomaly sequences, sample from the test data sequences\n",
        "     # Need to map test_data indices back to original sequences indices\n",
        "     test_data_start_index_in_sequences = 700 - seq_length\n",
        "     selected_test_indices = np.random.choice(np.arange(len(test_data)), num_examples_to_show, replace=False)\n",
        "     selected_anomalous_indices_in_sequences = selected_test_indices + test_data_start_index_in_sequences\n",
        "else:\n",
        "    print(\"Warning: Not enough sequences in test data to show anomalous examples.\")\n",
        "    selected_anomalous_indices_in_sequences = [] # No anomalous examples to show\n",
        "\n",
        "\n",
        "if len(selected_anomalous_indices_in_sequences) > 0:\n",
        "    selected_anomalous_sequences = sequences[selected_anomalous_indices_in_sequences]\n",
        "    reconstructed_anomalous_sequences = model.predict(selected_anomalous_sequences)\n",
        "\n",
        "    plt.figure(figsize=(15, 10))\n",
        "\n",
        "    # Plot normal examples\n",
        "    for i in range(num_examples_to_show):\n",
        "        plt.subplot(2, num_examples_to_show, i + 1)\n",
        "        plt.plot(selected_normal_sequences[i].flatten(), label='Original')\n",
        "        plt.plot(reconstructed_normal_sequences[i].flatten(), label='Reconstructed')\n",
        "        plt.title(f'Normal Example {i+1}\\nReconstruction Error: {np.mean(np.square(selected_normal_sequences[i] - reconstructed_normal_sequences[i])):.4f}')\n",
        "        plt.ylim([-1.5, 2.5]) # Set consistent y-axis limits\n",
        "        plt.legend()\n",
        "\n",
        "    # Plot anomalous examples\n",
        "    for i in range(num_examples_to_show):\n",
        "        if i < len(selected_anomalous_sequences): # Ensure we don't go out of bounds if less than num_examples_to_show anomalous sequences\n",
        "            plt.subplot(2, num_examples_to_show, num_examples_to_show + i + 1)\n",
        "            plt.plot(selected_anomalous_sequences[i].flatten(), label='Original')\n",
        "            plt.plot(reconstructed_anomalous_sequences[i].flatten(), label='Reconstructed')\n",
        "            plt.title(f'Anomalous Example {i+1}\\nReconstruction Error: {np.mean(np.square(selected_anomalous_sequences[i] - reconstructed_anomalous_sequences[i])):.4f}')\n",
        "            plt.ylim([-1.5, 2.5]) # Set consistent y-axis limits\n",
        "            plt.legend()\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.suptitle('Reconstruction Comparison: Normal vs. Anomalous Sequences', y=1.02, fontsize=16)\n",
        "    plt.show()\n",
        "else:\n",
        "    print(\"Skipping anomalous example visualization due to insufficient data.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7094a19c"
      },
      "source": [
        "このセルでは、異常検知モデルの性能を評価するための主要なメトリクスを計算し、表示しています。\n",
        "混同行列（Confusion Matrix）を計算し、真陽性（TP）、真陰性（TN）、偽陽性（FP）、偽陰性（FN）の数を示しています。\n",
        "Classification Reportを生成し、異常クラスに対するPrecision（精度）、Recall（再現率）、F1-scoreを表示しています。\n",
        "また、不均衡データセットの評価に適しているPrecision-Recall AUC（Area Under the Curve）を計算し、表示しています。\n",
        "最後に、Precision-Recallカーブをグラフで可視化しています。\n",
        "これらのメトリクスは、モデルが異常をどれだけ正確に、そして漏れなく検出できているかを示します。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 894
        },
        "id": "9771777c",
        "outputId": "cde423aa-f0ed-45ac-9c8b-3cef2ea051dd"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report, precision_recall_curve, auc\n",
        "\n",
        "# Assuming you have ground truth labels for anomalies in your test data\n",
        "# For this synthetic data, we know the anomaly is between index 700 and 709\n",
        "# Let's create a ground truth array for the entire time series\n",
        "ground_truth = np.zeros(len(scaled_data))\n",
        "ground_truth[700:710] = 1 # Mark the known anomaly period as 1\n",
        "\n",
        "# We only have anomaly scores for indices from 699 to 998\n",
        "# We need to align the ground truth with the anomaly scores\n",
        "ground_truth_aligned = ground_truth[start_index_for_scores : end_index_for_scores + 1]\n",
        "\n",
        "# Convert anomaly scores to binary predictions based on the threshold\n",
        "predictions = (full_anomaly_scores[start_index_for_scores : end_index_for_scores + 1] > threshold).astype(int)\n",
        "\n",
        "# Remove NaN values from predictions and ground truth where there are no anomaly scores\n",
        "valid_indices = ~np.isnan(full_anomaly_scores[start_index_for_scores : end_index_for_scores + 1])\n",
        "ground_truth_valid = ground_truth_aligned[valid_indices]\n",
        "predictions_valid = predictions[valid_indices]\n",
        "anomaly_scores_valid = full_anomaly_scores[start_index_for_scores : end_index_for_scores + 1][valid_indices]\n",
        "\n",
        "\n",
        "# Calculate evaluation metrics\n",
        "print(\"\\nEvaluation Metrics:\")\n",
        "# Confusion Matrix\n",
        "cm = confusion_matrix(ground_truth_valid, predictions_valid)\n",
        "print(\"Confusion Matrix:\\n\", cm)\n",
        "\n",
        "# Classification Report (includes Precision, Recall, F1-score)\n",
        "# Handle potential zero division if no anomalies are predicted or present\n",
        "report = classification_report(ground_truth_valid, predictions_valid, target_names=['Normal', 'Anomaly'], zero_division=1)\n",
        "print(\"\\nClassification Report:\\n\", report)\n",
        "\n",
        "# Precision-Recall AUC is often more informative for imbalanced datasets like anomaly detection\n",
        "# Calculate Precision-Recall curve\n",
        "precision, recall, _ = precision_recall_curve(ground_truth_valid, anomaly_scores_valid)\n",
        "\n",
        "# Calculate PR-AUC\n",
        "pr_auc = auc(recall, precision)\n",
        "print(f'\\nPrecision-Recall AUC: {pr_auc:.4f}')\n",
        "\n",
        "# Plot Precision-Recall curve\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(recall, precision, marker='.')\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('Precision-Recall Curve')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Evaluation Metrics:\n",
            "Confusion Matrix:\n",
            " [[230  60]\n",
            " [  0  10]]\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "      Normal       1.00      0.79      0.88       290\n",
            "     Anomaly       0.14      1.00      0.25        10\n",
            "\n",
            "    accuracy                           0.80       300\n",
            "   macro avg       0.57      0.90      0.57       300\n",
            "weighted avg       0.97      0.80      0.86       300\n",
            "\n",
            "\n",
            "Precision-Recall AUC: 0.1005\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c1a47cf5"
      },
      "source": [
        "## 実装のまとめと結果\n",
        "\n",
        "このノートブックでは、要求定義書に基づき、時系列異常検知システムのプロトタイプを構築しました。\n",
        "\n",
        "1.  **環境構築**: 必要なライブラリをインストールしました。\n",
        "2.  **データ準備**: サンプルとして合成時系列データを生成し、前処理（スケーリング、シーケンス作成）を行いました。\n",
        "3.  **モデル実装**: LSTMベースのオートエンコーダーモデルを実装しました。\n",
        "4.  **学習と検証**: 正常データを用いてモデルを学習させ、学習中の損失を可視化しました。\n",
        "5.  **解釈メカニズム**: オートエンコーダーの再構築誤差を計算し、正常データと異常データの再構築の違いを可視化しました。これにより、モデルが異常をどのように捉えているかの手がかりを得られます。\n",
        "6.  **評価**: 再構築誤差に基づき異常を検出し、混同行列、Classification Report (Precision, Recall, F1-score)、Precision-Recall AUC を用いてモデルの異常検知性能を評価しました。\n",
        "\n",
        "**評価結果の概要:**\n",
        "\n",
        "*   **Confusion Matrix**:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6372435d"
      },
      "source": [
        "## 今後の課題と発展\n",
        "\n",
        "このプロトタイプは、時系列異常検知システムの実装の出発点となります。さらなる発展のために、以下の課題や検討事項があります。\n",
        "\n",
        "*   **モデルの改善**:\n",
        "    *   論文で提案されている具体的なモデルアーキテクチャ（LSTMベースのオートエンコーダー以外のモデルや、論文独自の構造など）があれば、それを正確に実装し、比較評価を行う必要があります。\n",
        "    *   ハイパーパラメータチューニング（例: `seq_length`, LSTM層のユニット数、学習率、バッチサイズなど）により、モデル性能を向上させられる可能性があります。\n",
        "    *   より複雑な時系列パターン（季節性、トレンドなど）に対応するために、他のモデル（Transformer, TCNなど）や、アンサンブル手法を検討することも有効です。\n",
        "*   **データ処理の高度化**:\n",
        "    *   実際のデータは欠損値やノイズが多く含まれる場合があるため、より頑健な前処理手法（補完、平滑化など）が必要です。\n",
        "    *   多様な時系列データの特性（周期性、非定常性など）に合わせた特徴量エンジニアリングやデータ拡張手法を検討します。\n",
        "*   **異常検知ロジックの洗練**:\n",
        "    *   異常閾値の設定方法を、静的なパーセンタイルだけでなく、データの特性や許容される誤検知率・見逃し率に応じて動的に調整する手法（例: 統計的手法、外れ値検知アルゴリズムとの組み合わせ）を検討します。\n",
        "    *   単一点の異常だけでなく、文脈的な異常（例: 特定のパターンからの逸脱）やグループ異常（例: 複数の関連する時系列データで同時に発生する異常）の検知に対応するためのロジックが必要です。\n",
        "*   **解釈性の深化**:\n",
        "    *   再構築誤差の可視化に加え、論文で提案されている、あるいは一般的な説明可能なAI (XAI) の手法（例: LIME, SHAP, Attention weightsの分析など）を用いて、なぜ特定のデータポイントが異常と判断されたのかをより詳細かつ定量的に説明するメカニズムを実装します。\n",
        "    *   異常の原因究明を支援するために、異常発生時の他の関連データの情報や、過去の類似異常の事例などを提示する機能を検討します。\n",
        "*   **評価体制の強化**:\n",
        "    *   合成データだけでなく、実際の様々な特性を持つ時系列データセットを用いてモデルの汎化性能を評価します。\n",
        "    *   異常検知の評価指標（Precision, Recall, F1-score, AUCなど）を、ビジネス要件やアプリケーションの目的に合わせて適切に選択し、報告します。\n",
        "    *   時系列データに特有の評価方法（例: Point-wise, Event-wise, Pattern-wiseの評価）を導入することを検討します。\n",
        "*   **スケーラビリティと効率性**:\n",
        "    *   大量の時系列データをリアルタイムまたはバッチで処理するための、効率的なデータパイプラインとモデル推論の仕組みを検討します。\n",
        "    *   リソース制約のある環境での運用を考慮し、モデルの軽量化や計算効率の最適化を図ります。\n",
        "*   **ユーザーインターフェース**:\n",
        "    *   分析者やエンドユーザーが容易に異常検知結果を確認し、解釈情報を取得できるようなインタフェース（ダッシュボードなど）の開発を検討します。\n",
        "\n",
        "これらの課題への取り組みを通じて、より実用的で信頼性の高い、そして説明可能な時系列異常検知システムを構築することが可能になります。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ba7d6cc5"
      },
      "source": [
        "## 参考にして得られたコンセプト\n",
        "\n",
        "このプロジェクトの主要なコンセプトは以下の通りです。\n",
        "\n",
        "*   **プロジェクト名**: Interpretable Time Series Anomaly Detection System (ITSADS)\n",
        "*   **目的**: 時系列データにおける異常を検出するだけでなく、その検出理由を明確かつ根拠に基づいて説明する、解釈可能なシステムの開発。"
      ]
    }
  ]
}
No results found