3_anomaly-detection_autoencoder_ecg-5000.ipynb

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    "Author: Iman Irajian ∞\n",
    "\n",
    "Data: 18 Febuary, 2025\n",
    "\n",
    "Email: [`iman.irajian@gmail.com`](mailto:iman.irajian@gmail.com?subject=GithubAnomalyDetection)\n",
    "\n",
    "LinkedIn: [linkedin.com/in/imanirajian](https://linkedin.com/in/imanirajian)\n",
    "\n",
    "___\n"
   ],
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    "Problem: Anomaly Detection\n",
    "\n",
    "Model: Autoencoder\n",
    "\n",
    "Dataset: ECG5000 [X ~ (5000 x 140)]\n",
    "\n",
    "___"
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    "## Anomaly detection on ECG5000 dataset\n",
    "\n",
    "An autoencoder will be trained to identify anomalies in the simplified version of the [ECG5000 dataset](http://www.timeseriesclassification.com/description.php?Dataset=ECG5000)-([Electrocardiograms](https://en.wikipedia.org/wiki/Electrocardiography)), a collection of 5,000 electrocardiograms, each comprising 140 data points.\n",
    "\n",
    "For simplicity, the dataset has been modified such that each sample is assigned a binary label: `0` for abnormal heart rhythms and `1` for normal ones. The primary objective is to accurately detect irregular cardiac patterns. By focusing on anomaly detection, this approach offers a framework that can be extended to much larger datasets, where labeled data is scarce or unavailable. Such methodologies are particularly useful in real-world medical applications, where distinguishing between normal and abnormal physiological patterns is critical for early diagnosis and intervention.\n",
    "\n",
    "Although the dataset is labeled, making it suitable for supervised learning, this example is designed to illustrate unsupervised anomaly detection techniques. In many practical scenarios, vast amounts of normal data are available, while abnormal cases remain relatively rare. Therefore, training a model purely on normal rhythms allows it to learn their typical patterns, making deviations easier to spot. The insights gained from this demonstration can be extrapolated to broader use cases, including fraud detection, industrial monitoring, and predictive maintenance, where anomalies often indicate critical failures or malfunctions.\n",
    "\n",
    "The core principle behind this method lies in the functioning of autoencoders, which are trained to reconstruct input data with minimal error. Here, the autoencoder is exclusively trained on normal ECG patterns, enabling it to reconstruct them with high accuracy. When faced with abnormal rhythms, however, the model is expected to struggle, leading to a noticeably higher reconstruction error. By setting a predefined threshold, any instance exceeding this error margin is classified as an anomaly. This threshold-based approach ensures a systematic and quantifiable means of distinguishing normal and abnormal heart rhythms, reinforcing the efficacy of autoencoders in medical anomaly detection.\n",
    "\n",
    "___\n",
    "___"
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    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from Helpers.ecg_helper import load_ecg_data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, losses\n",
    "from tensorflow.keras.datasets import fashion_mnist\n",
    "\n",
    "from Models.AutoEncoders import AnomalyDetector"
   ]
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       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "variable_name": "df"
      }
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "# Load the dataset\n",
    "df, X, y, raw_data = load_ecg_data()\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "UmuCPVYKsKKx"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=21\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Normalize the data to `[0, 1]`."
   ],
   "metadata": {
    "id": "HtfIpkzZ6idt"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "tgMZVWRKsPx6"
   },
   "outputs": [],
   "source": [
    "min_val = tf.reduce_min(X_train)\n",
    "max_val = tf.reduce_max(X_train)\n",
    "\n",
    "X_train = (X_train - min_val) / (max_val - min_val)\n",
    "X_test = (X_test - min_val) / (max_val - min_val)\n",
    "\n",
    "X_train = tf.cast(X_train, tf.float32)\n",
    "X_test = tf.cast(X_test, tf.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BdSYr2IPsTiz"
   },
   "source": [
    "The autoencoder will be trained exclusively on normal rhythms, labeled as `1` in this dataset, ensuring a clear distinction between normal and abnormal heart patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "VvK4NRe8sVhE"
   },
   "outputs": [],
   "source": [
    "y_train = y_train.astype(bool)\n",
    "y_test = y_test.astype(bool)\n",
    "\n",
    "X_train_normal = X_train[y_train]\n",
    "X_test_normal = X_test[y_test]\n",
    "\n",
    "X_train_anomalous = X_train[~y_train]\n",
    "X_test_anomalous = X_test[~y_test]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wVcTBDo-CqFS"
   },
   "source": [
    "Plot ECG (normal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "ZTlMIrpmseYe",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "outputId": "ab7ca83b-e0f9-4df0-da93-8c601e478d6d"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.grid()\n",
    "plt.plot(np.arange(140), X_train_normal[0])\n",
    "plt.title(\"A Normal ECG\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QpI9by2ZA0NN"
   },
   "source": [
    "Plot ECG (anomalous)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "zrpXREF2siBr",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "outputId": "ecce06cb-e27b-4e5d-fc6d-a085d006f148"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.grid()\n",
    "plt.plot(np.arange(140), X_train_anomalous[0])\n",
    "plt.title(\"An Anomalous ECG\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0DS6QKZJslZz"
   },
   "source": [
    "### The model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "bf6owZQDsp9y"
   },
   "outputs": [],
   "source": [
    "autoencoder = AnomalyDetector()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "gwRpBBbg463S"
   },
   "outputs": [],
   "source": [
    "autoencoder.compile(optimizer='adam', loss='mae')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zuTy60STBEy4"
   },
   "source": [
    "It is important to note that while the autoencoder is trained solely on normal ECG patterns, its performance is evaluated on the entire test dataset, including both normal and abnormal rhythms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "V6NFSs-jsty2",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "ec987688-8bcb-4cab-8e7b-482af088ba8f"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m6s\u001B[0m 501ms/step - loss: 0.0574 - val_loss: 0.0529\n",
      "Epoch 2/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 27ms/step - loss: 0.0553 - val_loss: 0.0514\n",
      "Epoch 3/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - loss: 0.0526 - val_loss: 0.0497\n",
      "Epoch 4/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - loss: 0.0497 - val_loss: 0.0476\n",
      "Epoch 5/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - loss: 0.0462 - val_loss: 0.0455\n",
      "Epoch 6/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - loss: 0.0424 - val_loss: 0.0433\n",
      "Epoch 7/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - loss: 0.0384 - val_loss: 0.0413\n",
      "Epoch 8/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - loss: 0.0349 - val_loss: 0.0397\n",
      "Epoch 9/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - loss: 0.0318 - val_loss: 0.0379\n",
      "Epoch 10/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - loss: 0.0293 - val_loss: 0.0369\n",
      "Epoch 11/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 17ms/step - loss: 0.0278 - val_loss: 0.0361\n",
      "Epoch 12/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - loss: 0.0259 - val_loss: 0.0354\n",
      "Epoch 13/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 32ms/step - loss: 0.0249 - val_loss: 0.0349\n",
      "Epoch 14/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step - loss: 0.0241 - val_loss: 0.0341\n",
      "Epoch 15/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 33ms/step - loss: 0.0232 - val_loss: 0.0335\n",
      "Epoch 16/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 37ms/step - loss: 0.0227 - val_loss: 0.0328\n",
      "Epoch 17/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 23ms/step - loss: 0.0222 - val_loss: 0.0325\n",
      "Epoch 18/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step - loss: 0.0217 - val_loss: 0.0318\n",
      "Epoch 19/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 33ms/step - loss: 0.0214 - val_loss: 0.0313\n",
      "Epoch 20/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - loss: 0.0208 - val_loss: 0.0311\n",
      "Epoch 21/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - loss: 0.0205 - val_loss: 0.0307\n",
      "Epoch 22/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 18ms/step - loss: 0.0200 - val_loss: 0.0303\n",
      "Epoch 23/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 15ms/step - loss: 0.0198 - val_loss: 0.0301\n",
      "Epoch 24/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - loss: 0.0195 - val_loss: 0.0298\n",
      "Epoch 25/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - loss: 0.0193 - val_loss: 0.0294\n",
      "Epoch 26/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - loss: 0.0189 - val_loss: 0.0291\n",
      "Epoch 27/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 15ms/step - loss: 0.0187 - val_loss: 0.0292\n",
      "Epoch 28/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - loss: 0.0188 - val_loss: 0.0288\n",
      "Epoch 29/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - loss: 0.0185 - val_loss: 0.0286\n",
      "Epoch 30/30\n",
      "\u001B[1m5/5\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 16ms/step - loss: 0.0182 - val_loss: 0.0285\n"
     ]
    }
   ],
   "source": [
    "history = autoencoder.fit(\n",
    "    X_train_normal, X_train_normal,\n",
    "    epochs=30,\n",
    "    batch_size=512,\n",
    "    validation_data=(X_test, X_test),\n",
    "    shuffle=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "OEexphFwwTQS",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "outputId": "dd0be819-461a-464c-b244-450fc9ea97ef"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "plt.title(\"Training and validation losses\")\n",
    "plt.plot(history.history[\"loss\"], label=\"Training Loss\")\n",
    "plt.plot(history.history[\"val_loss\"], label=\"Validation Loss\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ceI5lKv1BT-A"
   },
   "source": [
    "An ECG will be classified as anomalous if its reconstruction error exceeds one standard deviation from the normal training samples; to illustrate this, we will first visualize a normal ECG from the training set, its reconstructed version after passing through the autoencoder, and the corresponding reconstruction error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "hmsk4DuktxJ2",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "outputId": "5506bb98-6015-4784-d94a-69281cee6422"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "X_data_encoded = autoencoder.encoder(X_test_normal).numpy()\n",
    "X_data_decoded = autoencoder.decoder(X_data_encoded).numpy()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "ix = 0\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.plot(X_test_normal[ix], 'blue')\n",
    "plt.plot(X_data_decoded[ix], 'red')\n",
    "\n",
    "plt.fill_between(np.arange(140), X_data_decoded[ix], X_test_normal[ix], color='lightcoral')\n",
    "\n",
    "plt.legend(labels=[\"Input\", \"Reconstruction\", \"Error\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ocA_q9ufB_aF"
   },
   "source": [
    "Create a similar plot, this time for an anomalous test example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "vNFTuPhLwTBn",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "outputId": "1c86177f-8dad-4513-c3c7-5706fda31bb8"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "X_data_encoded = autoencoder.encoder(X_test_anomalous).numpy()\n",
    "X_data_decoded = autoencoder.decoder(X_data_encoded).numpy()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "ix = 0\n",
    "\n",
    "plt.plot(X_test_anomalous[ix], 'blue')\n",
    "plt.plot(X_data_decoded[ix], 'red')\n",
    "\n",
    "plt.fill_between(np.arange(140), X_data_decoded[ix], X_test_anomalous[ix], color='lightcoral')\n",
    "\n",
    "plt.legend(labels=[\"Input\", \"Reconstruction\", \"Error\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ocimg3MBswdS"
   },
   "source": [
    "### Detect anomalies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xnh8wmkDsypN"
   },
   "source": [
    "Anomalies will be detected by determining whether the reconstruction loss exceeds a predefined threshold; the mean average error of normal training examples will be computed, and any future instance with a reconstruction error surpassing one standard deviation from this baseline will be classified as anomalous.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TeuT8uTA5Y_w"
   },
   "source": [
    "Plot the reconstruction error on normal ECGs from the training set"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def show_and_get_loss(X, set_name, threshold=None):\n",
    "  reconstructions = autoencoder.predict(X)\n",
    "  loss = tf.keras.losses.mae(reconstructions, X)\n",
    "\n",
    "  plt.figure()\n",
    "\n",
    "  plt.hist(loss[None, :], bins=50)\n",
    "\n",
    "  if (threshold):\n",
    "    plt.axvline(threshold, c='r')\n",
    "\n",
    "  plt.xlabel(f\"{set_name} loss: Mean Squared Error\")\n",
    "  plt.ylabel(\"Frequency: Number of examples\")\n",
    "  plt.title(f\"Loss of {set_name} set\")\n",
    "\n",
    "  plt.show()\n",
    "\n",
    "  return loss"
   ],
   "metadata": {
    "id": "tjvAc9zj3wlc"
   },
   "execution_count": 14,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "N7FltOnHu4-l",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 490
    },
    "outputId": "87200525-dd51-44f4-8c6b-bad30d08ed85"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001B[1m74/74\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 6ms/step\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "train_loss = show_and_get_loss(X_train_normal, \"Train\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mh-3ChEF5hog"
   },
   "source": [
    "Choose a threshold value that is one standard deviations above the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "82hkl0Chs3P_",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "30f5bcbe-40ff-4616-da57-aec9ce649302"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Threshold:  0.02760632\n"
     ]
    }
   ],
   "source": [
    "threshold = np.mean(train_loss) + np.std(train_loss)\n",
    "print(\"Threshold: \", threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uEGlA1Be50Nj"
   },
   "source": [
    "There are alternative methods for determining a threshold value beyond which test samples are identified as anomalies. The optimal approach will vary based on your dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zpLSDAeb51D_"
   },
   "source": [
    "If you analyze the reconstruction error of anomalous instances in the test set, you'll observe that most exhibit a higher reconstruction error than the threshold. By adjusting the threshold, you can fine-tune the precision and recall of your classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "sKVwjQK955Wy",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 490
    },
    "outputId": "c7606355-89e9-4ffa-ee37-2fe201999182"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001B[1m14/14\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 24ms/step\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "test_loss = show_and_get_loss(X_test_anomalous, \"Test\", threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PFVk_XGE6AX2"
   },
   "source": [
    "An ECG should be identified as an anomaly if its reconstruction error exceeds the threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "mkgJZfhh6CHr"
   },
   "outputs": [],
   "source": [
    "def predict(model, data, threshold):\n",
    "  reconstructions = model(data)\n",
    "  loss = tf.keras.losses.mae(reconstructions, data)\n",
    "  return tf.math.less(loss, threshold)\n",
    "\n",
    "def print_stats(predictions, labels):\n",
    "  print(\"Accuracy = {}\".format(accuracy_score(labels, predictions)))\n",
    "  print(\"Precision = {}\".format(precision_score(labels, predictions)))\n",
    "  print(\"Recall = {}\".format(recall_score(labels, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "sOcfXfXq6FBd",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "75b52dd6-7b7a-4549-9340-364b05327d8f"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Accuracy = 0.936\n",
      "Precision = 0.9940239043824701\n",
      "Recall = 0.8910714285714286\n"
     ]
    }
   ],
   "source": [
    "preds = predict(autoencoder, X_test, threshold)\n",
    "print_stats(preds, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
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    "∎\n",
    "___\n",
    "___\n",
    "\n",
    "### Refrences:\n",
    "\n",
    "1- Machine learning [Coursera] by Andrew Ng: https://www.coursera.org/learn/machine-learning\n",
    "\n",
    "2- Pattern Recognition and Machine Learning by Christopher Bishop: https://www.springer.com/gp/book/9780387310732\n",
    "\n",
    "3- [Deep Learning Book](https://www.deeplearningbook.org/) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.\n",
    "\n",
    "4- [Building Autoencoders in Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n",
    "\n",
    "___\n",
    "\n",
    "Author: Iman Irajian ∞\n",
    "\n",
    "Data: 18 Febuary, 2025\n",
    "\n",
    "Email: [`iman.irajian@gmail.com`](mailto:iman.irajian@gmail.com?subject=GithubAnomalyDetection)\n",
    "\n",
    "LinkedIn: [linkedin.com/in/imanirajian](https://linkedin.com/in/imanirajian)\n"
   ]
  }
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