task_anomaly_detection.ipynb

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   "source": [
    "# Anomaly Detection - ECG\n",
    "- Finding conspicuous electrocardiograms (ECG).\n",
    "- We use the ECG5000 dataset found [here](http://timeseriesclassification.com/description.php?Dataset=ECG5000).\n",
    "- Model: LSTM Autoencoder which is used in [1].\n",
    "    - [1] Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, Gautam Shroff: LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection. CoRR abs/1607.00148 (2016)\n",
    "    - <sup>Disclaimer: Large parts of the code are reproduced from [Venelin Valkov](https://curiousily.com/posts/time-series-anomaly-detection-using-lstm-autoencoder-with-pytorch-in-python/)</sup>"
   ]
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      "userId": "08051752269221227298"
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import seaborn as sns\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8c905a01-f9f9-4d79-9a32-8790e0a8f036",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023/06/28 19:14:12 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
      "2023/06/28 19:14:12 INFO mlflow.store.db.utils: Updating database tables\n",
      "INFO  [alembic.runtime.migration] Context impl SQLiteImpl.\n",
      "INFO  [alembic.runtime.migration] Will assume non-transactional DDL.\n",
      "INFO  [alembic.runtime.migration] Context impl SQLiteImpl.\n",
      "INFO  [alembic.runtime.migration] Will assume non-transactional DDL.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Experiment: artifact_location='file:///D:/Personal/SAL_Task/anomaly_detection/anomaly_detection/mlruns/1', creation_time=1687970766039, experiment_id='1', last_update_time=1687970766039, lifecycle_stage='active', name='Anomaly_Detection_ECG', tags={}>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mlflow tracking\n",
    "import mlflow\n",
    "\n",
    "mlflow.set_tracking_uri(\"sqlite:///mlflow.db\")\n",
    "mlflow.set_experiment(\"Anomaly_Detection_ECG\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eca8458",
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    "id": "4eca8458",
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   "source": [
    "## 1. Data Loading and Preprocessing"
   ]
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     "execution_count": 3,
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   "source": [
    "ecg_train_df = pd.read_csv('./data/ECG5000_TRAIN.txt', delimiter=\"\\t\", header=None)\n",
    "ecg_train_df.head(3)"
   ]
  },
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    "ecg_test_df = pd.read_csv('./data/ECG5000_TEST.txt', delimiter=\"\\t\", header=None)\n",
    "ecg_test_df.head(3)"
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       "      <td>1</td>\n",
       "      <td>-0.567088</td>\n",
       "      <td>-2.593450</td>\n",
       "      <td>-3.874230</td>\n",
       "      <td>-4.584095</td>\n",
       "      <td>-4.187449</td>\n",
       "      <td>-3.151462</td>\n",
       "      <td>-1.742940</td>\n",
       "      <td>-1.490659</td>\n",
       "      <td>-1.183580</td>\n",
       "      <td>...</td>\n",
       "      <td>1.284825</td>\n",
       "      <td>0.886073</td>\n",
       "      <td>0.531452</td>\n",
       "      <td>0.311377</td>\n",
       "      <td>-0.021919</td>\n",
       "      <td>-0.713683</td>\n",
       "      <td>-0.532197</td>\n",
       "      <td>0.321097</td>\n",
       "      <td>0.904227</td>\n",
       "      <td>-0.421797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 141 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label         1         2         3         4         5         6  \\\n",
       "0      1 -0.112522 -2.827204 -3.773897 -4.349751 -4.376041 -3.474986   \n",
       "1      1 -1.100878 -3.996840 -4.285843 -4.506579 -4.022377 -3.234368   \n",
       "2      1 -0.567088 -2.593450 -3.874230 -4.584095 -4.187449 -3.151462   \n",
       "\n",
       "          7         8         9  ...       131       132       133       134  \\\n",
       "0 -2.181408 -1.818286 -1.250522  ...  0.160348  0.792168  0.933541  0.796958   \n",
       "1 -1.566126 -0.992258 -0.754680  ...  0.560327  0.538356  0.656881  0.787490   \n",
       "2 -1.742940 -1.490659 -1.183580  ...  1.284825  0.886073  0.531452  0.311377   \n",
       "\n",
       "        135       136       137       138       139       140  \n",
       "0  0.578621  0.257740  0.228077  0.123431  0.925286  0.193137  \n",
       "1  0.724046  0.555784  0.476333  0.773820  1.119621 -1.436250  \n",
       "2 -0.021919 -0.713683 -0.532197  0.321097  0.904227 -0.421797  \n",
       "\n",
       "[3 rows x 141 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concatenate training and test data to a single dataframe\n",
    "ecg_all_df = pd.concat([ecg_train_df, ecg_test_df])\n",
    "column_names = list(ecg_all_df.columns)\n",
    "column_names[0] = \"label\"\n",
    "ecg_all_df.columns = column_names\n",
    "ecg_all_df[\"label\"] = ecg_all_df[\"label\"].astype('int32')\n",
    "\n",
    "# Shuffling the rows in the dataframe to mix test and training data\n",
    "ecg_all_df.sample(frac=1)\n",
    "\n",
    "ecg_all_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae653adc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1683585118185,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "ae653adc",
    "outputId": "3c3cfac4-a636-4c9e-fa9b-bf6c5fae5bbb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    2919\n",
      "2    1767\n",
      "4     194\n",
      "3      96\n",
      "5      24\n",
      "Name: label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# unique label counts\n",
    "print(ecg_all_df[\"label\"].value_counts())\n",
    "unique_labels = ecg_all_df[\"label\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15037054",
   "metadata": {
    "id": "15037054"
   },
   "source": [
    "It seems that there are 2919 normal samples and 1767+194+96+24 anomaly samples in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ef4ecec2",
   "metadata": {
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1683585118186,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "ef4ecec2"
   },
   "outputs": [],
   "source": [
    "def plot_time_series_sample(data_df, color):\n",
    "    tmp_df = data_df.copy()\n",
    "    tmp_df = tmp_df.drop(columns=[\"label\"])\n",
    "    tmp_df = tmp_df.iloc[1].transpose()\n",
    "    tmp_df = tmp_df.reset_index()    \n",
    "        \n",
    "    column_names = list(tmp_df.columns)\n",
    "    column_names[-1] = \"y\"\n",
    "    column_names[0] = \"x\"\n",
    "    tmp_df.columns = column_names\n",
    "    fig, ax = plt.subplots(figsize=(5, 3))\n",
    "    sns.lineplot(data=tmp_df, x=\"x\",y=\"y\", ax=ax, color=color)\n",
    "    fig.tight_layout()\n",
    "    plt.title(\"ECG of a heartbeat\", fontsize=14)\n",
    "    ax.set_ylabel(\"voltage (amplitude)\")\n",
    "    ax.set_xlabel(\"timesteps\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9b29e23e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 510
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1683585118187,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "9b29e23e",
    "outputId": "94e4d049-281f-4be8-c252-c284f1bfd6dc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_time_series_sample(ecg_all_df, \"b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "545cc78a",
   "metadata": {
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1683585118188,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "545cc78a"
   },
   "outputs": [],
   "source": [
    "def plot_time_series(label, data_df, ax, color):\n",
    "    tmp_df = data_df.copy()\n",
    "    tmp_df = tmp_df[tmp_df[\"label\"] == label]\n",
    "    tmp_df = tmp_df.drop(columns=[\"label\"])\n",
    "    tmp_df = tmp_df.transpose()\n",
    "    tmp_df['std'] = tmp_df.std(axis=1)\n",
    "    tmp_df['mean'] = tmp_df.mean(axis=1)\n",
    "    tmp_df['ci_lower'] = tmp_df['mean'] - tmp_df['std']\n",
    "    tmp_df['ci_upper'] = tmp_df['mean'] + tmp_df['std']\n",
    "    tmp_df = tmp_df.reset_index()\n",
    "    sns.lineplot(data=tmp_df, x=\"index\",y=\"mean\", ax=ax, color=color)\n",
    "    ax.fill_between(tmp_df['index'], tmp_df['ci_lower'], tmp_df['ci_upper'], color=color, alpha=.15)\n",
    "    \n",
    "    class_dict = dict({1: \"Normal\", 2: \"Anomaly 1\", 3: \"Anomaly 2\", 4: \"Anomaly 3\", 5: \"Anomaly 4\"})\n",
    "    ax.set_title(\"Label: {}\".format(class_dict[label]))\n",
    "    ax.set_ylabel(\"voltage (amplitude)\")\n",
    "    ax.set_xlabel(\"timesteps\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7457ee44",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 795
    },
    "executionInfo": {
     "elapsed": 1953,
     "status": "ok",
     "timestamp": 1683585120132,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "7457ee44",
    "outputId": "a226f1cb-b559-43bd-cb1f-cfc8e55f2752"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.02, 'ECG analysis of normal and anomalous heartbeats')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=len(unique_labels) // 2 + 1,\n",
    "                         ncols=2,\n",
    "                         sharey=True,\n",
    "                         figsize=(10,7))\n",
    "colors = ['r', 'g', 'b' ,'c', 'm', 'y']\n",
    "for ax_index, label in enumerate(unique_labels):\n",
    "    ax = axes.flat[ax_index]\n",
    "    plot_time_series(label, ecg_all_df, ax, colors[ax_index])\n",
    "fig.delaxes(axes.flat[-1])\n",
    "fig.tight_layout()\n",
    "fig.suptitle(\"ECG analysis of normal and anomalous heartbeats\", fontsize=11, y=1.02)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31c5c4e2",
   "metadata": {
    "id": "31c5c4e2"
   },
   "source": [
    "Based on the plots (where the mean values and the corresponding confidence intervals are plotted), you can already see the first differences. The recordings of normal heartbeats look clearly different from all others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5676daa9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1683585120133,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "5676daa9",
    "outputId": "c35112fb-1cb7-4ee5-938b-8b6e11d720eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2919, 140)\n"
     ]
    }
   ],
   "source": [
    "normal_data_df = ecg_all_df[ecg_all_df[\"label\"]==1].drop(labels=\"label\", axis=1)\n",
    "normal_data_df = normal_data_df.reset_index(drop=True)\n",
    "normal_data_df.head()\n",
    "print(normal_data_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "56e97577",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1683585120133,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "56e97577",
    "outputId": "cde962f6-f53a-47f6-d9b8-80faf82c74c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2081, 140)\n"
     ]
    }
   ],
   "source": [
    "anomaly_data_df = ecg_all_df[ecg_all_df[\"label\"]>1].drop(labels=\"label\", axis=1)\n",
    "anomaly_data_df = anomaly_data_df.reset_index(drop=True)\n",
    "anomaly_data_df.head()\n",
    "print(anomaly_data_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4af4263",
   "metadata": {
    "id": "b4af4263"
   },
   "source": [
    "As you can see, we have 2919 heartbeat recordings which are completly inconspicuous and 2081 heartbeat recordings with anomalies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2656314d",
   "metadata": {
    "id": "2656314d"
   },
   "source": [
    "## 2. Data Split - Training, Validation and Test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0f88a46f",
   "metadata": {
    "executionInfo": {
     "elapsed": 478,
     "status": "ok",
     "timestamp": 1683586099078,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "0f88a46f"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "def create_datasets(dataframe_df):\n",
    "    sequences = dataframe_df.astype(np.float32).to_numpy().tolist()\n",
    "    dataset = [torch.tensor(s).unsqueeze(1).float() for s in sequences]\n",
    "    n_seq, seq_len, n_features = torch.stack(dataset).shape\n",
    "    return dataset, seq_len, n_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7d65e793",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 700,
     "status": "ok",
     "timestamp": 1683586099774,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "7d65e793",
    "outputId": "62ecb6e2-b12a-494d-cf7b-4b3044a83c18"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample dimension: 140 1\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "training_df, validation_df = train_test_split(normal_data_df, test_size=0.2, random_state=42)\n",
    "validation_df, test_df = train_test_split(validation_df, test_size=0.4, random_state=42)\n",
    "\n",
    "dataset_training, _, _ = create_datasets(training_df)\n",
    "dataset_validation, _, _ = create_datasets(validation_df)\n",
    "dataset_test, sequence_length, n_features = create_datasets(test_df)\n",
    "dataset_anomaly, _, _ = create_datasets(anomaly_data_df)\n",
    "\n",
    "print(\"Sample dimension:\",sequence_length, n_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44d9c28c",
   "metadata": {
    "id": "44d9c28c"
   },
   "source": [
    "## 3. Compute Statistics - Normalize/Standardize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "FrZkzq76fIOk",
   "metadata": {
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1683586099775,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "FrZkzq76fIOk"
   },
   "outputs": [],
   "source": [
    "# you might add some code here and modify the datasets\n",
    "from scipy.stats import shapiro\n",
    "from sklearn.preprocessing import StandardScaler,normalize\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ADbqtz15QNpQ",
   "metadata": {
    "id": "ADbqtz15QNpQ"
   },
   "source": [
    "### 3.1 Normality of the Feature Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "Eq9TzMedg9Oa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1683586099776,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "Eq9TzMedg9Oa",
    "outputId": "5d0d870a-6841-472c-b309-6c3ab26b1bae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null hypothesis: sample comes from a normal distribution\n",
      "\n",
      "Number of p-values less than alpha of 0.05: 140 \n",
      "Null-hypothesis rejected!\n",
      "Feature values are not normally ditributed.\n"
     ]
    }
   ],
   "source": [
    "# checking whether the features are normally distributed - Shapiro–Wilk test\n",
    "p_values = [shapiro(list(training_df.iloc[:,i-1]))[1] for i in training_df.columns]\n",
    "print('Null hypothesis: sample comes from a normal distribution')\n",
    "print(f\"\\nNumber of p-values less than alpha of 0.05: {np.sum(np.array(p_values) < 0.05)} \\nNull-hypothesis rejected!\\nFeature values are not normally ditributed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "XrHLVG8rQxcw",
   "metadata": {
    "id": "XrHLVG8rQxcw"
   },
   "source": [
    "### 3.1 Standardization/Normalization of the features\n",
    "- Normality of the distribution is desirable but not strictly necessary for standardization.\n",
    "- Shapiro–Wilk test indicates that the feature distributions are not normally distributed.\n",
    "- Plotting the feature distributions shows that they are approximately bell-shaped, suggesting that standardization may be appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3U4yyAX8gBSI",
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1683586100320,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "3U4yyAX8gBSI"
   },
   "outputs": [],
   "source": [
    "# applying scaling\n",
    "std = False\n",
    "norm = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "sWILGlJYgUrv",
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1683586100320,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "sWILGlJYgUrv"
   },
   "outputs": [],
   "source": [
    "# standardization function\n",
    "def standardization_dist(data):\n",
    "    \"\"\" Function to standardize the data\n",
    "    Args:\n",
    "        data (list): list of torch.tensors\n",
    "    \"\"\"\n",
    "    df = pd.DataFrame(torch.stack(data).squeeze(2))\n",
    "    scaler = StandardScaler().fit(df)\n",
    "    df = torch.tensor(scaler.transform(df)).unsqueeze(2)\n",
    "    return df\n",
    "\n",
    "# normalization function\n",
    "def normalization_dist(data):\n",
    "    \"\"\" Function to normalize the data\n",
    "    Args:\n",
    "        data (list): list of torch.tensors\n",
    "    \"\"\"    \n",
    "    df = pd.DataFrame(torch.stack(data).squeeze(2))\n",
    "    df = torch.tensor(normalize(df, norm='l2')).unsqueeze(2)\n",
    "    return df\n",
    "\n",
    "if std == True:\n",
    "  # standardization - training set\n",
    "  dataset_training = standardization_dist(dataset_training)\n",
    "  # standardization - validation set\n",
    "  dataset_validation = standardization_dist(dataset_validation)\n",
    "  # standardization - test set\n",
    "  dataset_test = standardization_dist(dataset_test)\n",
    "  # standardization - anomaly set\n",
    "  dataset_anomaly = standardization_dist(dataset_anomaly)\n",
    "\n",
    "if norm == True:  \n",
    "  # normalize - training set\n",
    "  dataset_training = normalization_dist(dataset_training)\n",
    "  # normalize - validation set\n",
    "  dataset_validation = normalization_dist(dataset_validation)\n",
    "  # normalize - test set\n",
    "  dataset_test = normalization_dist(dataset_test)\n",
    "  # normalize - anomaly set\n",
    "  dataset_anomaly = normalization_dist(dataset_anomaly)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d020bea2",
   "metadata": {
    "id": "d020bea2",
    "tags": []
   },
   "source": [
    "## 4. Implementation of the LSTM Autoencoder Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "62db14a5",
   "metadata": {
    "executionInfo": {
     "elapsed": 350,
     "status": "ok",
     "timestamp": 1683586101939,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "62db14a5"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "class Encoder(nn.Module):\n",
    "    def __init__(self, sequence_length, n_features, n_layers=1, embedding_dim=64):\n",
    "        super(Encoder, self).__init__()\n",
    "        \n",
    "        self.sequence_length, self.n_features = sequence_length, n_features\n",
    "        self.embedding_dim, self.hidden_dim = embedding_dim, 2 * embedding_dim\n",
    "        \n",
    "        self.rnn1 = nn.LSTM(input_size=self.n_features, \n",
    "                            hidden_size=self.hidden_dim, \n",
    "                            num_layers=n_layers, \n",
    "                            batch_first=True)\n",
    "        self.rnn2 = nn.LSTM(input_size=self.hidden_dim,                     \n",
    "                            hidden_size=self.embedding_dim, \n",
    "                            num_layers=n_layers, \n",
    "                            batch_first=True)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = x.reshape((1, self.sequence_length, self.n_features))\n",
    "        x, (_, _) = self.rnn1(x)\n",
    "        x, (hidden_n, _) = self.rnn2(x)\n",
    "        return hidden_n.reshape((self.n_features, self.embedding_dim))\n",
    "        \n",
    "class Decoder(nn.Module):\n",
    "    def __init__(self, sequence_length, n_features, n_layers=1, input_dim=64):\n",
    "        super(Decoder, self).__init__()\n",
    "        self.sequence_length, self.input_dim = sequence_length, input_dim\n",
    "        self.hidden_dim, self.n_features = 2 * input_dim, n_features\n",
    "        \n",
    "        self.rnn1 = nn.LSTM(input_size=self.input_dim, \n",
    "                            hidden_size=self.input_dim, \n",
    "                            num_layers=n_layers, \n",
    "                            batch_first=True)\n",
    "        self.rnn2 = nn.LSTM(input_size=self.input_dim,                     \n",
    "                            hidden_size=self.hidden_dim, \n",
    "                            num_layers=n_layers, \n",
    "                            batch_first=True)\n",
    "        self.output_layer = nn.Linear(self.hidden_dim, self.n_features)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = x.repeat(self.sequence_length, self.n_features)\n",
    "        x = x.reshape((self.n_features, self.sequence_length, self.input_dim))\n",
    "        x, (hidden_n, cell_n) = self.rnn1(x)\n",
    "        x, (hidden_n, cell_n) = self.rnn2(x)\n",
    "        x = x.reshape((self.sequence_length, self.hidden_dim))\n",
    "        return self.output_layer(x)\n",
    "        \n",
    "class LSTMAE(nn.Module):\n",
    "    def __init__(self, sequence_length, n_features, n_layers=1, embedding_dim=64):\n",
    "        super(LSTMAE, self).__init__()\n",
    "                \n",
    "        self.encoder = Encoder(sequence_length=sequence_length, n_features=n_features, n_layers=1,\n",
    "                               embedding_dim=embedding_dim)\n",
    "        self.decoder = Decoder(sequence_length=sequence_length, n_features=n_features, n_layers=1,\n",
    "                               input_dim=embedding_dim)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.encoder(x)\n",
    "        x = self.decoder(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "61163a85",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1683585850465,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "61163a85",
    "outputId": "1dcf7739-1139-4dce-cc1e-f10f968efe95"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90b8ab1c",
   "metadata": {
    "id": "90b8ab1c",
    "tags": []
   },
   "source": [
    "## 5.1 Hyperparameters Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4b5be3a5",
   "metadata": {
    "executionInfo": {
     "elapsed": 367,
     "status": "ok",
     "timestamp": 1683585852162,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "4b5be3a5"
   },
   "outputs": [],
   "source": [
    "# hyperparameters = dict()\n",
    "# hyperparameters[\"lr\"] = 1e-3             # learning rate\n",
    "# hyperparameters[\"n_epochs\"] = 150         # number of epochs \n",
    "# hyperparameters[\"n_layers\"] = 1          # number of lstm layers\n",
    "# hyperparameters[\"embedding_dim\"] = 128   # lstm embedding dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f8ad4e55",
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1683585852163,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "f8ad4e55"
   },
   "outputs": [],
   "source": [
    "# model = LSTMAE(sequence_length=sequence_length, n_features=n_features, n_layers=hyperparameters[\"n_layers\"], \n",
    "#                embedding_dim=hyperparameters[\"embedding_dim\"])\n",
    "# model = model.to(device)\n",
    "# model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a432c43f",
   "metadata": {
    "id": "a432c43f",
    "tags": []
   },
   "source": [
    "## 5.2 Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2fac7ab8",
   "metadata": {
    "executionInfo": {
     "elapsed": 551,
     "status": "ok",
     "timestamp": 1683582438726,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "2fac7ab8"
   },
   "outputs": [],
   "source": [
    "import copy \n",
    "from tqdm import tqdm\n",
    "def train_model(model, train_dataset, val_dataset, hyperparameters: dict):\n",
    "    \"\"\"\n",
    "    In this function the model is trained using the training set and evaluated on the validation set.\n",
    "    \"\"\"\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=hyperparameters[\"lr\"])\n",
    "    criterion = nn.L1Loss(reduction='sum').to(device)\n",
    "    history = dict(train=[], val=[])\n",
    "    \n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_loss = float('inf')\n",
    "    \n",
    "    for epoch in tqdm(range(1, hyperparameters[\"n_epochs\"] + 1)):\n",
    "        model = model.train()\n",
    "        train_losses = []\n",
    "        for seq_true in train_dataset:\n",
    "            optimizer.zero_grad()\n",
    "            \n",
    "            seq_true = seq_true.to(device)\n",
    "            seq_pred = model(seq_true)\n",
    "            loss = criterion(seq_pred, seq_true)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            train_losses.append(loss.item())\n",
    "    \n",
    "        val_losses = []\n",
    "        model = model.eval()\n",
    "\n",
    "        with torch.no_grad():\n",
    "            for seq_true in val_dataset:\n",
    "                seq_true = seq_true.to(device)\n",
    "                seq_pred = model(seq_true)\n",
    "                loss = criterion(seq_pred, seq_true)\n",
    "                val_losses.append(loss.item())\n",
    "        train_loss = np.mean(train_losses)\n",
    "        val_loss = np.mean(val_losses)\n",
    "\n",
    "        history['train'].append(train_loss)\n",
    "        history['val'].append(val_loss)\n",
    "\n",
    "        if val_loss < best_loss:\n",
    "            best_loss = val_loss\n",
    "            best_model_wts = copy.deepcopy(model.state_dict())\n",
    "\n",
    "        print(f'Epoch {epoch}: train loss {train_loss} val loss {val_loss}')\n",
    "\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model.eval(), history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dd3e6853",
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1683582438726,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "dd3e6853"
   },
   "outputs": [],
   "source": [
    "# model, history = train_model(model, dataset_training, dataset_validation, hyperparameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "80f96519",
   "metadata": {
    "executionInfo": {
     "elapsed": 201,
     "status": "ok",
     "timestamp": 1683582441152,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "80f96519"
   },
   "outputs": [],
   "source": [
    "# from pathlib import Path\n",
    "# torch.save(model, Path(\"./model_lr_{}_layers_{}_embedding_{}.pth\".format(hyperparameters[\"lr\"], \n",
    "#                                                                          hyperparameters[\"n_layers\"],\n",
    "#                                                                          hyperparameters[\"embedding_dim\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "000bdb11",
   "metadata": {
    "id": "000bdb11"
   },
   "source": [
    "## 6. Validation of the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "KfGxuGEHMdmJ",
   "metadata": {
    "id": "KfGxuGEHMdmJ"
   },
   "source": [
    "### 6.1 Loading the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "zteIKTfiUUOq",
   "metadata": {
    "executionInfo": {
     "elapsed": 402,
     "status": "ok",
     "timestamp": 1683586106219,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "zteIKTfiUUOq"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mlflow.models.model.ModelInfo at 0x1d2a4677fd0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hyperparameters = {\"lr\":1e-4,  # learning rate\n",
    "                   \"n_layers\":1, # number of lstm layers\n",
    "                   \"embedding_dim\":128, # lstm embedding dimension\n",
    "                   \"device\":device,\n",
    "                   \"scaling\":{\"standardization\":std, \"normalization\":norm}\n",
    "                  }\n",
    "# load the model to evaluate\n",
    "path_to_the_model = Path(\"trained_models/model_lr_{}_layers_{}_embedding_{}.pth\".format(hyperparameters[\"lr\"], \n",
    "                                                                           hyperparameters[\"n_layers\"],\n",
    "                                                                           hyperparameters[\"embedding_dim\"]))\n",
    "# loading std/norm models\n",
    "# path_to_the_model = Path(\"norm_model_lr_{}_layers_{}_embedding_{}.pth\".format(hyperparameters[\"lr\"], \n",
    "#                                                                            hyperparameters[\"n_layers\"],\n",
    "#                                                                            hyperparameters[\"embedding_dim\"]))\n",
    "\n",
    "mlflow.start_run(run_name=str(path_to_the_model).split('\\\\')[-1].split('.pth')[0])\n",
    "mlflow.log_params(hyperparameters)\n",
    "\n",
    "model = torch.load(path_to_the_model, map_location=torch.device(device))\n",
    "model = model.to(device)\n",
    "model.eval()\n",
    "\n",
    "mlflow.pytorch.log_model(model, 'pytorch_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "_uFfvtqZKw4w",
   "metadata": {
    "id": "_uFfvtqZKw4w"
   },
   "source": [
    "### 6.2 Data set - Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "N89wBzy1LacY",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 385
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1683586107174,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "N89wBzy1LacY",
    "outputId": "428bddb9-72fc-4f16-d21a-226cad746683"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data size:\n",
      "test_df = 234\n",
      "anomaly_data_df = 2081\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data size\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.bar(['training_df', 'validation_df', 'test_df', 'anomaly_data_df'], \n",
    "        [len(training_df), len(validation_df), len(test_df), len(anomaly_data_df)])\n",
    "\n",
    "plt.xlabel('Data sets')\n",
    "plt.ylabel('Sample count')\n",
    "plt.title('Data size')\n",
    "\n",
    "print(f'Data size:\\ntest_df = {len(test_df)}\\nanomaly_data_df = {len(anomaly_data_df)}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "JZxVTkC2Mphi",
   "metadata": {
    "id": "JZxVTkC2Mphi"
   },
   "source": [
    "- Sample count:\n",
    "  - test_df = 234 (negative samples)\n",
    "  - anomaly_data_df = 2081 (positive samples)\n",
    "- Classes are unbalanced and hence accuracy is not a suitable to assess model performance.\n",
    "- Approaches:\n",
    "    - Under/Over Sampling\n",
    "      - Undersampling the majority data or oversampling the minority can be performed.\n",
    "      - But, oversampling the negative samples results in significant amount of repetitions.\n",
    "      - Undersampling the positive class results in overall fewer number of samples.\n",
    "    - Hence metrics like F1 Score which are unaffected by class imbalances can be used.\n",
    "    - Precision and Recall Curve can be generated and the threshold that maximizes F1 score can be computed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e777711-b98d-44b4-9c88-baa734da9cd9",
   "metadata": {},
   "source": [
    "### 6.3 Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7aebfdce",
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1683586107506,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "7aebfdce"
   },
   "outputs": [],
   "source": [
    "# Compute the accuracy of the model - you need to maximize it. \n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from tqdm import tqdm\n",
    "\n",
    "def compute_metrics(model, dataset_normal, dataset_anomaly, device):\n",
    "    \"\"\" This function computes the model KPIs\n",
    "    Args:\n",
    "        model (torch.nn.Module): the model\n",
    "        dataset_normal (torch.tensor): data set with normal samples\n",
    "        dataset_anomaly (torch.tensor): data set with anomaly samples\n",
    "        device (string): device to use - cpu/cuda:0\n",
    "        \n",
    "    Returns:\n",
    "        df_samples (pandas.DataFrame): dataframe with actual label, prediction, loss and features\n",
    "    \"\"\"\n",
    "    criterion = nn.L1Loss(reduction='sum').to(device)\n",
    "    # loss list for normal (n) and anomaly (a) samples\n",
    "    loss_n, loss_a = [],[]\n",
    "\n",
    "    for sequence in tqdm(dataset_normal, desc='Normal samples'):\n",
    "        sequence_true = sequence.to(device)\n",
    "        sequence_pred = model(sequence_true)\n",
    "        loss = criterion(sequence_pred, sequence_true)\n",
    "        loss_n.append( sequence.squeeze(1).tolist() + [loss.to('cpu').item()] )\n",
    "            \n",
    "    for sequence in tqdm(dataset_anomaly, desc='Anomaly samples'):\n",
    "        sequence_true = sequence.to(device)\n",
    "        sequence_pred = model(sequence_true)\n",
    "        loss = criterion(sequence_pred, sequence_true)\n",
    "        loss_a.append( sequence.squeeze(1).tolist() + [loss.to('cpu').item()] )\n",
    "\n",
    "    # dataframe of samples with true_label and score (loss)\n",
    "    normal_samples = np.concatenate( ([[0]]*len(loss_n), np.array(loss_n)), axis=1)\n",
    "    anomaly_samples = np.concatenate( ([[1]]*len(loss_a), np.array(loss_a)), axis=1)\n",
    "    full_samples = np.concatenate( (normal_samples, anomaly_samples) )\n",
    "    df_samples = pd.DataFrame(full_samples).rename(columns={0:'label', 141:'loss'})\n",
    "\n",
    "    # # precision and recall\n",
    "    precision, recall, thresholds = precision_recall_curve(df_samples['label'], df_samples['loss'])\n",
    "\n",
    "    # computing the F1 score for each threshold value\n",
    "    pr, p_r = (precision * recall), (precision + recall)\n",
    "    f1_scores = [2*(pr/p_r) if p_r > 0 else 0 for pr, p_r in zip(pr, p_r)]\n",
    "\n",
    "    # find the index of the maximum F1-score\n",
    "    best_threshold_index = np.argmax(f1_scores)\n",
    "\n",
    "    # get the best threshold, Precision, Recall values\n",
    "    best_threshold = thresholds[best_threshold_index]\n",
    "    best_precision, best_recall = precision[best_threshold_index], recall[best_threshold_index]\n",
    "    \n",
    "    best_metrics = {'Best F1 Score':np.max(f1_scores),\n",
    "                    'Best threshold':best_threshold,\n",
    "                    'Best precision':best_precision,\n",
    "                    'Best recall':best_recall}\n",
    "    mlflow.log_metrics(best_metrics)\n",
    "\n",
    "    # dataframe update\n",
    "    df_samples['pred'] = np.nan\n",
    "    pred_normal_idx = df_samples[df_samples.loss < best_threshold].index\n",
    "    pred_anomaly_idx = df_samples[df_samples.loss >= best_threshold].index\n",
    "    df_samples.loc[pred_normal_idx, 'pred'] = 0\n",
    "    df_samples.loc[pred_anomaly_idx, 'pred'] = 1\n",
    "\n",
    "    # plot the PR curve\n",
    "    plt.figure(figsize=(4,3))\n",
    "    plt.plot(recall, precision, color= 'black', label='PR curve')\n",
    "    plt.scatter(best_recall, best_precision, color='tab:red', label='Best PR - Max F1 Score')\n",
    "\n",
    "    # setting the plot parms\n",
    "    plt.xlim([-0.05, 1.05])\n",
    "    plt.ylim([-0.05, 1.05])\n",
    "    plt.xlabel('Recall')\n",
    "    plt.ylabel('Precision')\n",
    "    plt.legend()\n",
    "    plt.title('Precision-Recall Curve')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "    return df_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "L2kEoo_3TMeo",
   "metadata": {
    "id": "L2kEoo_3TMeo"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normal samples: 100%|████████████████████████████████████████████████████████████████| 234/234 [00:03<00:00, 64.07it/s]\n",
      "Anomaly samples: 100%|█████████████████████████████████████████████████████████████| 2081/2081 [00:28<00:00, 74.05it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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iEglKzv+JwoOHUHL+TzCJpEG206Szd/z9/eHv7690/7Vr16J169b48ccfAVTf4/XixYv44Ycf6rwVHSGENGdFycl4tDgGlXl50jaBhQXMv46G4Wu3jNSEd2rKZmpqqsw9MwFgwIABiIuLQ0VFBbS1teWWKSsrk7lpc1FRkVrbXrZsGX7++We1lm1oBgYG2LRpE/T19VFZWSn3kEgkMs+rqqpQVVUFxpj06zef1+fr+iwPVH+6sEbN14ra3va6ppahbTfMeq5fvw5/f3/pNMM37+f0vj8HgEePHqGnjQ2wfz9chCI4i0TS96Py0SM8mBoJrPhRo8H/ToV+Xl4ezM3NZdrMzc1RWVmJgoICWFpayi0TExNT671YVZGbm4urV6/Wez0NpX379k1dAiEq++uvv5q6hCZ36I3nq62t0UffAGAM4PHwaHEMDHx9wePzNbK9dyr0AfkPH9T89aztQwnR0dGIioqSPq/55JqqPvvsMwwcOFDl5Rra4sWLcfz4cQCAQCB464PP54PP54PH40FLSwtaWloyX7/5vD5fq7oMj8eTfh9f/37W1fa21zW1DG1bs8tcvnwZYrEY/DeC7M3f4/f5+R9//IEWABJ+/VWmz8qCgurQBwDGUJmXh9KLaRB36wpNeKdC38LCAnmvHfMCgPz8fAgEApiYmChcRiQSQSQS1Xvbzs7OcHZ2rvd6NK1fv36oqqqSCUxCyLuh8OAhfPXf6v924p4+wbLHj/HXa4eja1Q+fqyxbb5T8/S9vb2RkpIi05acnAwvLy+Fx/O5omYvmRDybhG0aiX9ur1IR6l+9dWkof/ixQtkZGQgIyMDQPWUzIyMDOTk5ACoPjQTGhoq7T9x4kTcvXsXUVFRuH79OjZt2oS4uDjMmDGjKconhJB60fPyhMDCAuDxYPfajmv5/yY1gMeDwMICel6eGttmk4b+xYsX4e7uDnd3dwBAVFQU3N3dMXfuXADVJ09r/gAAgIODA5KSknDixAl07twZ3377LVauXEnTNQkh7yQenw/zr6MBANZCobT9RVUV8L//3s2/jtbYSVwA4DFF84jeY0VFRTAyMkJhYSFdZZMQ0izUzNN3PnkCAHDG0QlmNjZKz9NXJdco9AkhpBlgEgm0BNVza7J/Owy7/v2U3sNXJdfeqRO5hBDyvno94MWeHho9pPM6Cn1CCOEQCn1CCOEQCn1CCOEQCn1CCOEQCn1CCOEQCn1CCGlmcnNzG2zdFPqEENLMlJaWNti6KfQJIaSZaNOmTYNvg0KfEEI4hEKfEEI4hEKfEEKamcLCwgZbN4U+IYQ0E1lZWQCABw8eNNg2KPQJIaSZ+OCDDwBAI7d4rc07dY9cQgh5n82cORPZ2dno1KlTg22DQp8QQpqJsWPHNvg26PAOIYRwCIU+IYRwCIU+IYRwCIU+IYRwCIU+IYRwCIU+IYRwCIU+IYRwCIU+IYRwCIU+IYRwCIU+IYRwCIU+IYRwCIU+IYRwCIU+IYRwCIU+IYRwSJOH/po1a+Dg4AAdHR14enri9OnTdfbfunUr3NzcoKenB0tLS0RERODJkyeNVC0hhLzbmjT0ExMTERkZidmzZyM9PR29evWCv78/cnJyFPY/c+YMQkNDMW7cOFy7dg07d+7EhQsXMH78+EaunBBC3k1NGvrLly/HuHHjMH78eLi4uODHH3+Era0tYmNjFfb/448/YG9vjylTpsDBwQEffvghPv/8c1y8eLGRKyeEkHdTk4V+eXk50tLS4OfnJ9Pu5+eHc+fOKVymR48euH//PpKSksAYw6NHj7Br1y4MGjSo1u2UlZWhqKhI5kEIIVzVZKFfUFAAiUQCc3NzmXZzc3Pk5eUpXKZHjx7YunUrgoODIRQKYWFhAWNjY/z000+1bicmJgZGRkbSh62trUbHQQgh75ImP5HL4/FknjPG5NpqZGZmYsqUKZg7dy7S0tJw+PBhZGdnY+LEibWuPzo6GoWFhdLHvXv3NFo/IYS8S5rsxuimpqbg8/lye/X5+flye/81YmJi0LNnT8ycORMA0KlTJ4jFYvTq1QuLFi2CpaWl3DIikQgikUjzAyCEkHdQk+3pC4VCeHp6IiUlRaY9JSUFPXr0ULhMaWkptLRkS+bz+QCq/0MghBBStyY9vBMVFYWNGzdi06ZNuH79OqZNm4acnBzp4Zro6GiEhoZK+wcGBmLPnj2IjY1FVlYWzp49iylTpqBr166wsrJqqmEQQsg7o8kO7wBAcHAwnjx5goULFyI3Nxeurq5ISkqCnZ0dACA3N1dmzn54eDiKi4uxatUqTJ8+HcbGxujbty/++c9/NtUQCCHkncJjHDsuUlRUBCMjIxQWFsLQ0LCpyyGEkHpTJdeafPYOIYSQxkOhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHEKhTwghHCJQZ6GSkhIsWbIER48eRX5+PqqqqmRez8rK0khxhBBCNEut0B8/fjxOnjyJsWPHwtLSEjweT+0C1qxZg++//x65ubno2LEjfvzxR/Tq1avW/mVlZVi4cCG2bNmCvLw82NjYYPbs2fj000/VroEQQrhCrdD/7bffcOjQIfTs2bNeG09MTERkZCTWrFmDnj17Yt26dfD390dmZiZat26tcJlRo0bh0aNHiIuLg5OTE/Lz81FZWVmvOgghhCt4jDGm6kIODg5ISkqCi4tLvTberVs3eHh4IDY2Vtrm4uKCoUOHIiYmRq7/4cOHMXr0aGRlZaFly5ZqbbOoqAhGRkYoLCyEoaGh2rUTQkhzoUquqXUi99tvv8XcuXNRWlqqVoEAUF5ejrS0NPj5+cm0+/n54dy5cwqX2b9/P7y8vLB06VJYW1vD2dkZM2bMwMuXL2vdTllZGYqKimQehBDCVWod3lm2bBlu374Nc3Nz2NvbQ1tbW+b1S5cuvXUdBQUFkEgkMDc3l2k3NzdHXl6ewmWysrJw5swZ6OjoYO/evSgoKMDkyZPx9OlTbNq0SeEyMTExWLBggZIjI4SQ95taoT906FCNFfDmSWDGWK0nhquqqsDj8bB161YYGRkBAJYvX44RI0Zg9erV0NXVlVsmOjoaUVFR0udFRUWwtbXVWP2EEPIuUSv0582bV+8Nm5qags/ny+3V5+fny+3917C0tIS1tbU08IHqcwCMMdy/fx9t27aVW0YkEkEkEtW7XkIIeR/U68NZaWlp2LJlC7Zu3Yr09HSVlhUKhfD09ERKSopMe0pKCnr06KFwmZ49e+Lhw4d48eKFtO3GjRvQ0tKCjY2N6gMghBCuYWp49OgR69OnD+PxeKxFixbM2NiY8Xg81rdvX5afn6/0enbs2MG0tbVZXFwcy8zMZJGRkUwsFrM7d+4wxhibNWsWGzt2rLR/cXExs7GxYSNGjGDXrl1jJ0+eZG3btmXjx49XepuFhYUMACssLFR+wIQQ0oypkmtq7el/+eWXKCoqwrVr1/D06VM8e/YM//nPf1BUVIQpU6YovZ7g4GD8+OOPWLhwITp37oxTp04hKSkJdnZ2AIDc3Fzk5ORI++vr6yMlJQXPnz+Hl5cXQkJCEBgYiJUrV6ozDEII4Ry15ukbGRnh999/R5cuXWTa//zzT/j5+eH58+eaqk/jaJ4+IeR90+Dz9KuqquSmaQKAtra23HV4CCGENB9qhX7fvn0xdepUPHz4UNr24MEDTJs2Db6+vhorjhBCiGapFfqrVq1CcXEx7O3t4ejoCCcnJzg4OKC4uBg//fSTpmskhBCiIWrN07e1tcWlS5eQkpKC//73v2CMoUOHDujXr5+m6yOEEKJBap3IfZfRiVxCyPtGlVxTek9/5cqV+Oyzz6Cjo/PWKZKqTNskhBDSeJTe03dwcMDFixdhYmICBweH2lfI4zXrO2fRnj4h5H3TIHv62dnZCr8mhBDy7tDIjdElEgkyMjLw7NkzTayOEEJIA1Er9CMjIxEXFwegOvD/9re/wcPDA7a2tjhx4oQm6yOEEKJBaoX+rl274ObmBgA4cOAA7ty5g//+97+IjIzE7NmzNVogIYQQzVEr9AsKCmBhYQEASEpKwsiRI+Hs7Ixx48bh6tWrGi2QEEKI5qgV+ubm5sjMzIREIsHhw4elH8oqLS0Fn8/XaIGEEEI0R61P5EZERGDUqFGwtLQEj8dD//79AQDnz59H+/btNVogIYQQzVEr9OfPnw9XV1fcu3cPI0eOlN6OkM/nY9asWRotkBBCiObQZRgIIeQdR5dhIIQQohBdhoEQQt5xdBkGQgghCmnkMgyEEELeDWqF/ogRI7BkyRK59u+//x4jR46sd1GEEEIahlqhf/LkSQwaNEiufeDAgTh16lS9iyKEENIw1Ar9Fy9eQCgUyrVra2ujqKio3kURQghpGGqFvqurKxITE+Xad+zYgQ4dOtS7KEIIIQ1DrU/kfvPNNxg+fDhu376Nvn37AgCOHj2K7du3Y+fOnRotkBBCiOaoFfpDhgzBvn37sHjxYuzatQu6urro1KkTfv/9d/j4+Gi6RkIIIRpCl2EghJB3nCq5pvY8/efPn2Pjxo34+uuv8fTpUwDApUuX8ODBA3VXSQghpIGpdXjnypUr6NevH4yMjHDnzh2MHz8eLVu2xN69e3H37l1s3rxZ03USQgjRALX29KOiohAeHo6bN29CR0dH2u7v70/z9AkhpBlTK/QvXLiAzz//XK7d2toaeXl59S6KEEJIw1Ar9HV0dBR+COuvv/5Cq1atVFrXmjVr4ODgAB0dHXh6euL06dNKLXf27FkIBAJ07txZpe0RQgiXqRX6QUFBWLhwISoqKgBUX045JycHs2bNwvDhw5VeT2JiIiIjIzF79mykp6ejV69e8Pf3R05OTp3LFRYWIjQ0FL6+vuqUTwghnKXWlM2ioiIEBATg2rVrKC4uhpWVFfLy8uDt7Y2kpCSIxWKl1tOtWzd4eHggNjZW2ubi4oKhQ4ciJiam1uVGjx6Ntm3bgs/nY9++fcjIyFCpdpqySQh5nzTI9fRfZ2hoiDNnzuDYsWO4dOkSqqqq4OHhgX79+im9jvLycqSlpcndU9fPzw/nzp2rdbn4+Hjcvn0bW7ZswaJFi966nbKyMpSVlUmf07WBCCFcpnLoV1ZWQkdHBxkZGejbt6/0MgyqKigogEQigbm5uUy7ubl5rSeDb968iVmzZuH06dMQCJQrPSYmBgsWLFCrRkIIed+ofExfIBDAzs4OEolEIwXweDyZ54wxuTYAkEgkGDNmDBYsWABnZ2el1x8dHY3CwkLp4969e/WumRBC3lVqHd6ZM2cOoqOjsWXLFrRs2VKtDZuamoLP58vt1efn58vt/QNAcXExLl68iPT0dHzxxRcAgKqqKjDGIBAIkJycrPC/DpFIBJFIpFaNhBDyvlEr9FeuXIlbt27BysoKdnZ2ciduL1269NZ1CIVCeHp6IiUlBcOGDZO2p6SkICgoSK6/oaEhrl69KtO2Zs0aHDt2DLt27arzZu2EEEKqqRX6Q4cOBY/HQ32v1RYVFYWxY8fCy8sL3t7eWL9+PXJycjBx4kQA1YdmHjx4gM2bN0NLSwuurq4yy5uZmUFHR0eunRBCiGIqhX5paSlmzpyJffv2oaKiAr6+vvjpp59gamqq1saDg4Px5MkTLFy4ELm5uXB1dUVSUhLs7OwAALm5uW+ds08IIUR5Ks3TnzlzJtasWYOQkBDo6upi27Zt6N279zt14xSap08Ied802Dz9PXv2IC4uDqNHjwYAhISEoGfPnpBIJODz+epXTAghpFGoNGXz3r176NWrl/R5165dIRAI8PDhQ40XRgghRPNUCn2JRAKhUCjTJhAIUFlZqdGiCCGENAyVDu8wxhAeHi4z7/3Vq1eYOHGizLTNPXv2aK5CQgghGqNS6IeFhcm1ffLJJxorhhBCSMNSKfTj4+Mbqg5CCCGNQO0boxNCCHn3UOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHUOgTQgiHNHnor1mzBg4ODtDR0YGnpydOnz5da989e/agf//+aNWqFQwNDeHt7Y0jR440YrWEEPJua9LQT0xMRGRkJGbPno309HT06tUL/v7+yMnJUdj/1KlT6N+/P5KSkpCWloY+ffogMDAQ6enpjVw5IYS8m3iMMdZUG+/WrRs8PDwQGxsrbXNxccHQoUMRExOj1Do6duyI4OBgzJ07V6n+RUVFMDIyQmFhIQwNDdWqmxBCmhNVck3QSDXJKS8vR1paGmbNmiXT7ufnh3Pnzim1jqqqKhQXF6Nly5a19ikrK0NZWZn0eVFRkVLrlkgkqKioUKovIeT/aWtrg8/nN3UZpBZNFvoFBQWQSCQwNzeXaTc3N0deXp5S61i2bBlKSkowatSoWvvExMRgwYIFStfFGENeXh6eP3+u9DKEEFnGxsawsLAAj8dr6lLIG5os9Gu8+UPBGFPqB2X79u2YP38+fv31V5iZmdXaLzo6GlFRUdLnRUVFsLW1rbV/TeCbmZlBT0+PfmgJUQFjDKWlpcjPzwcAWFpaNnFF5E1NFvqmpqbg8/lye/X5+flye/9vSkxMxLhx47Bz507069evzr4ikQgikUipmiQSiTTwTUxMlFqGECJLV1cXQPXvspmZGR3qaWaabPaOUCiEp6cnUlJSZNpTUlLQo0ePWpfbvn07wsPDsW3bNgwaNEijNdUcw9fT09PoegnhmprfITov1vw06eGdqKgojB07Fl5eXvD29sb69euRk5ODiRMnAqg+NPPgwQNs3rwZQHXgh4aGYsWKFejevbv0vwRdXV0YGRlprC46pENI/dDvUPPVpKEfHByMJ0+eYOHChcjNzYWrqyuSkpJgZ2cHAMjNzZWZs79u3TpUVlbi73//O/7+979L28PCwpCQkNDY5RNCyDunSefpN4W65rO+evUK2dnZ0k8IE0LUQ79LjUuVefpNfhkGohnh4eHg8Xjg8XjQ1tZGmzZtMGPGDJSUlAAA7ty5I32dx+PByMgI3bt3x4EDB5q4ckJIY6LQf48MHDgQubm5yMrKwqJFi7BmzRrMmDFDps/vv/+O3NxcnD9/Hl27dsXw4cPxn//8p1HrLC8vb9TtEUL+H4X+WzDGUFJS0iQPVY+8iUQiWFhYwNbWFmPGjEFISAj27dsn08fExAQWFhZo3749vvvuO1RUVOD48eN1rvf+/fsYPXo0WrZsCbFYDC8vL5w/fx5A9X8YQ4cOlekfGRmJ3r17S5/37t0bX3zxBaKiomBqaor+/fvj448/xujRo2WWq6iogKmpKeLj46Xv/dKlS9GmTRvo6urCzc0Nu3btUuk9IYTIavIPZzV3paWl0NfXb5Jtv3jxAmKxWO3ldXV1a50yV1FRgQ0bNgCo/th8XTX4+PjA2toa+/fvh4WFBS5duoSqqiqVavn5558xadIknD17Fowx3Lp1C6NGjcKLFy+k7++RI0dQUlKC4cOHAwDmzJmDPXv2IDY2Fm3btsWpU6fwySefoFWrVvDx8VFp+4SQahT676k///wT27Ztg6+vr0x7jx49oKWlhZcvX6Kqqgr29vZ1XsZi27ZtePz4MS5cuCC9xpGTk5PK9Tg5OWHp0qXS546OjhCLxdi7dy/Gjh0r3VZgYCAMDQ1RUlKC5cuX49ixY/D29gYAtGnTBmfOnMG6deso9AlRE4X+W+jp6eHFixdNtm1VHDx4EPr6+qisrERFRQWCgoLw008/yfRJTExE+/btcePGDURGRmLt2rV1XrAuIyMD7u7udfZRhpeXl8xzbW1tjBw5Elu3bsXYsWNRUlKCX3/9Fdu2bQMAZGZm4tWrV+jfv7/McuXl5XB3d69XLYRwGYX+W/B4vHodYmlMffr0QWxsLLS1tWFlZaXwsI2trS3atm2Ltm3bQl9fH8OHD0dmZmat1y+q+Uh9bbS0tOTOPSg6pKToPQwJCYGPjw/y8/ORkpICHR0d+Pv7A4D08NGhQ4dgbW0ts5yyl9UghMijE7nvEbFYDCcnJ9jZ2dV5nL6Gj48PXF1d8d1339Xap1OnTsjIyMDTp08Vvt6qVSvk5ubKtGVkZChVb48ePWBra4vExERs3boVI0eOhFAoBAB06NABIpEIOTk5cHJyknnUdcE8QkjdKPQ5bvr06Vi3bh0ePHig8PWPP/4YFhYWGDp0KM6ePYusrCzs3r0bqampAIC+ffvi4sWL2Lx5M27evIl58+YpPQWUx+NhzJgxWLt2LVJSUvDJJ59IXzMwMMCMGTMwbdo0/Pzzz7h9+zbS09OxevVq/Pzzz/UfOCEcRaHPcYMHD4a9vX2te/tCoRDJyckwMzNDQEAAPvjgAyxZskR65cQBAwbgm2++wVdffYUuXbqguLgYoaGhSm8/JCQEmZmZsLa2Rs+ePWVe+/bbbzF37lzExMTAxcUFAwYMwIEDB+Dg4KD+gAnhOLoMw2voo+OEaAb9LjUuugwDIYQQhSj0CSGEQyj0CSGEQyj0CSGEQyj0CSGEQyj0CSGEQyj0CSGEQyj0CSGEQyj0CSGEQyj0CSGEQyj03xOv3xidx+PBxMQEAwcOxJUrVzS2jfnz56Nz585K9aupg8/nw9bWFuPHj8fjx4+lfV6vVV9fH25ubkhISNBInTXvxcSJE+Vemzx5Mng8HsLDwzWyrdokJCTIjLHmsXHjRgBAbm4uxowZg3bt2kFLSwuRkZFKrXf37t3o1q0bjIyMYGBggI4dO2L69OkNOBLyvqHQbyBMIkHJ+T9RePAQSs7/CSaRNPg2a26Mnpubi6NHj0IgEGDw4MENvl1FOnbsiNzcXOTk5CA2NhYHDhyQuxBbfHw8cnNzcfnyZQQHByMiIgJHjhzRyPZtbW2xY8cOvHz5Utr26tUrbN++Ha1bt9bINt7G0NBQ+v2oeYSEhAAAysrK0KpVK8yePRtubm5Kre/333/H6NGjMWLECPz5559IS0vDd99916A3mpdIJCrfGpM0bxT6DaAoORm3fPshJywMD2fMQE5YGG759kNRcnKDbrfmxugWFhbo3Lkz/vGPf+DevXsye9gPHjxAcHAwWrRoARMTEwQFBeHOnTvS10+cOIGuXbtCLBbD2NgYPXv2xN27d5GQkIAFCxbg8uXL0r3WuvbMBQIBLCwsYG1tjcGDB2PKlClITk6WCWFjY2NYWFjA0dERX3/9NVq2bIlkDb1HHh4eaN26Nfbs2SNt27NnD2xtbeXuvHX48GF8+OGHMDY2homJCQYPHozbt29LX9+8eTP09fVx8+ZNaduXX34JZ2dnlJSU1FoDj8eTfj9qHjU3pbG3t8eKFSsQGhoKIyMjpcZ08OBBfPjhh5g5cybatWsHZ2dnDB06VO7uaPv374eXlxd0dHRgamqKjz76SPras2fPEBoaihYtWkBPTw/+/v4y40pISICxsTEOHjwovafB3bt3UV5ejq+++grW1tYQi8Xo1q0bTpw4oVTdpHmh0NewouRkPJgaicq8PJn2ykeP8GBqZIMHf40XL15g69atcHJygomJCYDqm7z36dMH+vr6OHXqFM6cOQN9fX0MHDgQ5eXlqKysxNChQ+Hj44MrV64gNTUVn332GXg8HoKDgzF9+nTpHnxubi6Cg4OVrkdXVxdVVVWorKyUe00ikeCXX37B06dPlbr5i7IiIiIQHx8vfb5p0yZ8+umncv1KSkoQFRWFCxcu4OjRo9DS0sKwYcOke7ihoaEICAhASEgIKisrcfjwYaxbtw5bt25t1LuqWVhY4Nq1a3Xer+DQoUP46KOPMGjQIKSnp+Po0aMyt6oMDw/HxYsXsX//fqSmpoIxhoCAAJm7nZWWliImJgYbN27EtWvXYGZmhoiICJw9exY7duzAlStXMHLkSAwcOFDmDwZ5RzCOKSwsZABYYWGh3GsvX75kmZmZ7OXLl2qtu6qykt3w6c0y27VX/Gjvwm749GZVlZX1HYacsLAwxufzmVgsZmKxmAFglpaWLC0tTdonLi6OtWvXjlVVVUnbysrKmK6uLjty5Ah78uQJA8BOnDihcBvz5s1jbm5ub63lzX7Xr19nTk5OrGvXrtI2AExHR4eJxWLG5/MZANayZUt28+ZN1Qf/hrCwMBYUFMQeP37MRCIRy87OZnfu3GE6Ojrs8ePHLCgoiIWFhdW6fH5+PgPArl69Km17+vQps7GxYZMmTWLm5uZs0aJFddYQHx/PAEi/H2KxmJmbmyvs6+Pjw6ZOnfrWcb148YIFBAQwAMzOzo4FBwezuLg49urVK2kfb29vFhISonD5GzduMADs7Nmz0raCggKmq6vLfvnlF5m6MzIypH1u3brFeDwee/Dggcz6fH19WXR0tMJt1fd3iaimrlx7E+3pa1DpxTS5PXwZjKEyLw+lF9MaZPt9+vRBRkYGMjIycP78efj5+cHf3x93794FAKSlpeHWrVswMDCAvr4+9PX10bJlS7x69Qq3b99Gy5YtER4ejgEDBiAwMBArVqyQuxWisq5evQp9fX3o6uqiQ4cOsLW1xdatW2X6/Otf/0JGRgZSUlLQuXNn/Otf/4KTk1Ot65w4caK0bn19/bfWYGpqikGDBuHnn39GfHw8Bg0aBFNTU7l+t2/fxpgxY9CmTRsYGhpKb9KSk5Mj7dOiRQvExcUhNjYWjo6OmDVr1lu3b2BgIP1+ZGRk4Ny5c29dpi5isRiHDh3CrVu3MGfOHOjr62P69Ono2rUrSktLAVTfqtLX11fh8tevX4dAIEC3bt2kbSYmJmjXrh2uX78ubRMKhejUqZP0+aVLl8AYg7Ozs8z7f/LkSZnDYOTdQDdG16DK146da6KfqmrukVvD09MTRkZG2LBhAxYtWoSqqip4enrKhS9Qfa9boPrk6pQpU3D48GEkJiZizpw5SElJQffu3VWqpV27dti/fz/4fD6srKwU3szcwsJCet/bnTt3wt3dHV5eXujQoYPCdS5cuBAzZsxQqY5PP/0UX3zxBQBg9erVCvsEBgbC1tYWGzZsgJWVFaqqquDq6ip3gvTUqVPg8/l4+PAhSkpK3n6zCi2tOv+IqcvR0RGOjo4YP348Zs+eDWdnZyQmJiIiIqLOG9mzWu6XxBgDj8eTPtfV1ZV5XlVVBT6fj7S0NOkd02oo88eXNC+0p69Bgv8Fp6b61RePx4OWlpb05KmHhwdu3rwJMzMzuZuNv34y0d3dHdHR0Th37hxcXV2xbds2ANV7gBIlZyEJhUI4OTnBwcFBYeC/ycnJCcOHD0d0dHStfd6sWxk15yvKy8sxYMAAudefPHmC69evY86cOfD19YWLiwuePXsm1+/cuXNYunQpDhw4AENDQ3z55ZdKbb+h2dvbQ09PT3pCuVOnTjh69KjCvh06dEBlZSXOnz8vbXvy5Alu3LgBFxeXWrfh7u4OiUSC/Px8uZ8bCwsLzQ6INDja09cgPS9PCCwsUPnoEaBor4rHg8DcHHpeng2y/bKyMuT97/DSs2fPsGrVKrx48QKBgYEAqu9H+/333yMoKAgLFy6EjY0NcnJysGfPHsycORMVFRVYv349hgwZAisrK/z111+4ceOGdKqlvb09srOzkZGRARsbGxgYGCgV6MqaPn063NzccPHiRZmTj/XB5/Olhy7e3EsFIJ3FtH79elhaWiInJ0fu0E1xcTHGjh2LL7/8Ev7+/mjdujW8vLwwePBgjBw5Uu3aMjIyAFSfdH/8+DEyMjIgFApr/U9n/vz5KC0tRUBAAOzs7PD8+XOsXLkSFRUV6N+/PwBg3rx58PX1haOjI0aPHo3Kykr89ttv+Oqrr9C2bVsEBQVhwoQJWLduHQwMDDBr1ixYW1sjKCio1jqdnZ0REhKC0NBQLFu2DO7u7igoKMCxY8fwwQcfICAgQO33gDSBhj7B0Nw05IlcxhgrPHKEZbZ3qX68cRI3s70LKzxypD7l1yosLIwBkD4MDAxYly5d2K5du2T65ebmstDQUGZqaspEIhFr06YNmzBhAissLGR5eXls6NChzNLSkgmFQmZnZ8fmzp3LJBIJY4yxV69eseHDhzNjY2MGgMXHxyusRZkTvgDY3r175dr79+/P/P391XkLpGpO5NbmzRO5KSkpzMXFhYlEItapUyd24sQJmfoiIiLYBx98IHPCdMWKFaxly5bs/v37CrcRHx/PjIyM6qzz9e9XzcPOzq7W/seOHWPDhw9ntra2TCgUMnNzczZw4EB2+vRpmX67d+9mnTt3ZkKhkJmamrKPPvpI+trTp0/Z2LFjmZGREdPV1WUDBgxgN27ceGvd5eXlbO7cucze3p5pa2szCwsLNmzYMHblyhWFtdKJ3MalyoncJg/91atXM3t7eyYSiZiHhwc7depUnf1PnDjBPDw8mEgkYg4ODiw2Nlal7TV06DNWHfxvzuK54dO7wQKfkOaGQr9xqRL6TXp4JzExEZGRkVizZg169uyJdevWwd/fH5mZmQo/NZmdnY2AgABMmDABW7ZswdmzZzF58mS0atUKw4cPb4IRKGbo5wcDX9/q2TyPH0PQqhX0vDzBU3B4gRBCGhOPsVpO6TeCbt26wcPDA7GxsdI2FxcXDB06FDExMXL9//GPf2D//v0y08smTpyIy5cvIzU1ValtFhUVwcjICIWFhXKzL169eoXs7Gw4ODhAR0dHzVERQuh3qXHVlWtvarLZO+Xl5UhLS4Ofn59Mu5+fX63zmVNTU+X6DxgwABcvXpT5ROHrysrKUFRUJPMghBCuarLQLygogEQigbm5uUy7ubm5dAbKm/Ly8hT2r6ysREFBgcJlYmJiYGRkJH3Y2tpqZgCEEPIOavJ5+q9/CASQ/6CIMv0VtdeIjo5GYWGh9HHv3r231tSER7wIeS/Q71Dz1WQnck1NTcHn8+X26vPz8+X25mtYWFgo7C8QCKQXFXuTSCRSei55zcW+SktL6/xkIyGkbjWXhdDkBfSIZjRZ6AuFQnh6eiIlJQXDhg2TtqekpNT6QRFvb28cOHBApi05ORleXl4a+eHi8/kwNjZGfn4+AEBPT6/O/zoIIbIYYygtLUV+fj6MjY0VfiCONK0mnbIZFRWFsWPHwsvLC97e3li/fj1ycnKkdzyKjo7GgwcPsHnzZgDVM3VWrVqFqKgoTJgwAampqYiLi8P27ds1VlPNx8prgp8QorqaeyWQ5qdJQz84OBhPnjzBwoULkZubC1dXVyQlJcHOzg4ApHdequHg4ICkpCRMmzYNq1evhpWVFVauXKnROfo8Hg+WlpYwMzOrdUYQIaR22tratIffjDXpPP2moMp8VkIIeRe8E/P0CSGEND4KfUII4RAKfUII4RDOXU+/5hQGXY6BEPK+qMkzZU7Rci70i4uLAYAux0AIee8UFxfL3AVPEc7N3qmqqsLDhw9hYGCg0gevioqKYGtri3v37r23s37e9zG+7+MD3v8x0vgUY4yhuLgYVlZW0NKq+6g95/b0tbS0YGNjo/byhoaG7+UP2+ve9zG+7+MD3v8x0vjkvW0PvwadyCWEEA6h0CeEEA6h0FeSSCTCvHnzlL5i57vofR/j+z4+4P0fI42v/jh3IpcQQriM9vQJIYRDKPQJIYRDKPQJIYRDKPQJIYRDKPRfs2bNGjg4OEBHRweenp44ffp0nf1PnjwJT09P6OjooE2bNli7dm0jVao+Vca4Z88e9O/fH61atYKhoSG8vb1x5MiRRqxWdap+D2ucPXsWAoEAnTt3btgC60nV8ZWVlWH27Nmws7ODSCSCo6MjNm3a1EjVqkfVMW7duhVubm7Q09ODpaUlIiIi8OTJk0aqVjWnTp1CYGAgrKyswOPxsG/fvrcuo/GcYYQxxtiOHTuYtrY227BhA8vMzGRTp05lYrGY3b17V2H/rKwspqenx6ZOncoyMzPZhg0bmLa2Ntu1a1cjV648Vcc4depU9s9//pP9+eef7MaNGyw6Opppa2uzS5cuNXLlylF1fDWeP3/O2rRpw/z8/Jibm1vjFKsGdcY3ZMgQ1q1bN5aSksKys7PZ+fPn2dmzZxuxatWoOsbTp08zLS0ttmLFCpaVlcVOnz7NOnbsyIYOHdrIlSsnKSmJzZ49m+3evZsBYHv37q2zf0PkDIX+/3Tt2pVNnDhRpq19+/Zs1qxZCvt/9dVXrH379jJtn3/+OevevXuD1Vhfqo5RkQ4dOrAFCxZoujSNUHd8wcHBbM6cOWzevHnNOvRVHd9vv/3GjIyM2JMnTxqjPI1QdYzff/89a9OmjUzbypUrmY2NTYPVqCnKhH5D5Awd3gFQXl6OtLQ0+Pn5ybT7+fnh3LlzCpdJTU2V6z9gwABcvHixWd5bV50xvqmqqgrFxcVo2bJlQ5RYL+qOLz4+Hrdv38a8efMausR6UWd8+/fvh5eXF5YuXQpra2s4OztjxowZePnyZWOUrDJ1xtijRw/cv38fSUlJYIzh0aNH2LVrFwYNGtQYJTe4hsgZzl1wTZGCggJIJBKYm5vLtJubmyMvL0/hMnl5eQr7V1ZWoqCgAJaWlg1WrzrUGeObli1bhpKSEowaNaohSqwXdcZ38+ZNzJo1C6dPn4ZA0Lx/FdQZX1ZWFs6cOQMdHR3s3bsXBQUFmDx5Mp4+fdosj+urM8YePXpg69atCA4OxqtXr1BZWYkhQ4bgp59+aoySG1xD5Azt6b/mzUstM8bqvPyyov6K2psTVcdYY/v27Zg/fz4SExNhZmbWUOXVm7Ljk0gkGDNmDBYsWABnZ+fGKq/eVPn+VVVVgcfjYevWrejatSsCAgKwfPlyJCQkNNu9fUC1MWZmZmLKlCmYO3cu0tLScPjwYWRnZ2PixImNUWqj0HTONO/dm0ZiamoKPp8vtzeRn58v91e2hoWFhcL+AoEAJiYmDVarutQZY43ExESMGzcOO3fuRL9+/RqyTLWpOr7i4mJcvHgR6enp+OKLLwBUhyRjDAKBAMnJyejbt2+j1K4Mdb5/lpaWsLa2lrnkrouLCxhjuH//Ptq2bdugNatKnTHGxMSgZ8+emDlzJgCgU6dOEIvF6NWrFxYtWtTs/uNWVUPkDO3pAxAKhfD09ERKSopMe0pKCnr06KFwGW9vb7n+ycnJ8PLygra2doPVqi51xghU7+GHh4dj27Ztzfo4qarjMzQ0xNWrV5GRkSF9TJw4Ee3atUNGRga6devWWKUrRZ3vX8+ePfHw4UO8ePFC2nbjxo1631OioagzxtLSUrmbhvD5fADK3TqwuWuQnFH7FPB7pmaqWFxcHMvMzGSRkZFMLBazO3fuMMYYmzVrFhs7dqy0f81UqmnTprHMzEwWFxf3zkzZVHaM27ZtYwKBgK1evZrl5uZKH8+fP2+qIdRJ1fG9qbnP3lF1fMXFxczGxoaNGDGCXbt2jZ08eZK1bduWjR8/vqmG8FaqjjE+Pp4JBAK2Zs0advv2bXbmzBnm5eXFunbt2lRDqFNxcTFLT09n6enpDABbvnw5S09Pl05JbYycodB/zerVq5mdnR0TCoXMw8ODnTx5UvpaWFgY8/Hxkel/4sQJ5u7uzoRCIbO3t2exsbGNXLHqVBmjj48PAyD3CAsLa/zClaTq9/B1zT30GVN9fNevX2f9+vVjurq6zMbGhkVFRbHS0tJGrlo1qo5x5cqVrEOHDkxXV5dZWlqykJAQdv/+/UauWjnHjx+v83eqMXKGLq1MCCEcQsf0CSGEQyj0CSGEQyj0CSGEQyj0CSGEQyj0CSGEQyj0CSGEQyj0CSGEQyj0CSGEQyj0CWmG7O3t8eOPP0qfK3trPULehkKfkDeEh4eDx+OBx+NBIBCgdevWmDRpEp49e9bUpRFSbxT6hCgwcOBA5Obm4s6dO9i4cSMOHDiAyZMnN3VZhNQbhT4hCohEIlhYWMDGxgZ+fn4IDg5GcnKy9PX4+Hi4uLhAR0cH7du3x5o1a2SWv3//PkaPHo2WLVtCLBbDy8sL58+fBwDcvn0bQUFBMDc3h76+Prp06YLff/+9UcdHuItuokLIW2RlZeHw4cPS65dv2LAB8+bNw6pVq+Du7o709HRMmDABYrEYYWFhePHiBXx8fGBtbY39+/fDwsICly5dQlVVFQDgxYsXCAgIwKJFi6Cjo4Off/4ZgYGB+Ouvv9C6deumHCrhAAp9QhQ4ePAg9PX1IZFI8OrVKwDA8uXLAQDffvstli1bho8++ggA4ODggMzMTKxbtw5hYWHYtm0bHj9+jAsXLkhvIu/k5CRdt5ubG9zc3KTPFy1ahL1792L//v3Su3gR0lAo9AlRoE+fPoiNjUVpaSk2btyIGzdu4Msvv8Tjx49x7949jBs3DhMmTJD2r6yslN6WMCMjA+7u7tLAf1NJSQkWLFiAgwcP4uHDh6isrMTLly+Rk5PTKGMj3EahT4gCYrFYune+cuVK9OnTBwsWLJDuiW/YsEHuloo1t+nT1dWtc90zZ87EkSNH8MMPP8DJyQm6uroYMWIEysvLG2AkhMii0CdECfPmzYO/vz8mTZoEa2trZGVlISQkRGHfTp06YePGjXj69KnCvf3Tp08jPDwcw4YNA1B9jP/OnTsNWT4hUjR7hxAl9O7dGx07dsTixYsxf/58xMTEYMWKFbhx4wauXr2K+Ph46TH/jz/+GBYWFhg6dCjOnj2LrKws7N69G6mpqQCqj+/v2bMHGRkZuHz5MsaMGSM9yUtIQ6PQJ0RJUVFR2LBhAwYMGICNGzciISEBH3zwAXx8fJCQkAAHBwcAgFAoRHJyMszMzBAQEIAPPvgAS5YskR7++de//oUWLVqgR48eCAwMxIABA+Dh4dGUQyMcQvfIJYQQDqE9fUII4RAKfUII4RAKfUII4RAKfUII4RAKfUII4RAKfUII4RAKfUII4RAKfUII4RAKfUII4RAKfUII4RAKfUII4ZD/AzdebA2DNN5JAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_samples = compute_metrics(model = model,\n",
    "                            dataset_normal = dataset_test, \n",
    "                            dataset_anomaly = dataset_anomaly, \n",
    "                            device = device)\n",
    "\n",
    "mlflow.end_run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bc35331-c6a2-4a35-880b-aeede905c8f1",
   "metadata": {},
   "source": [
    "### 6.4 Loading the best model and registering using MLflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "97b3fce7-7426-46aa-bb0c-19fae2d402a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model_pth = 'runs:/1c1886f90edb444291ab5033b37c539e/pytorch_model'\n",
    "best_model = mlflow.pytorch.load_model(best_model_pth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "44c6645b-13f7-4f69-982a-fc04ea6ee209",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(8.8250, grad_fn=<SumBackward0>)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "criterion = nn.L1Loss(reduction='sum').to(device)\n",
    "sample = dataset_test[1]  # dataset_anomaly[0]\n",
    "criterion(best_model(sample), sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f30494ea-192e-4bd6-91ea-4771c98a3a4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Successfully registered model 'anomaly-detect-reg'.\n",
      "2023/06/28 21:05:20 INFO mlflow.tracking._model_registry.client: Waiting up to 300 seconds for model version to finish creation. Model name: anomaly-detect-reg, version 1\n",
      "Created version '1' of model 'anomaly-detect-reg'.\n"
     ]
    }
   ],
   "source": [
    "# registering the model\n",
    "result = mlflow.register_model(best_model_pth, \"anomaly-detect-reg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "a726696f-2ea9-4e2a-a71a-cf9d743a3d1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ModelVersion: aliases=[], creation_timestamp=1687979120508, current_stage='Production', description='', last_updated_timestamp=1687979122577, name='anomaly-detect-reg', run_id='1c1886f90edb444291ab5033b37c539e', run_link='', source='file:///D:/Personal/SAL_Task/anomaly_detection/anomaly_detection/mlruns/1/1c1886f90edb444291ab5033b37c539e/artifacts/pytorch_model', status='READY', status_message='', tags={}, user_id='', version='1'>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stage changing\n",
    "client = mlflow.tracking.MlflowClient()\n",
    "client.transition_model_version_stage(\n",
    "    name = \"anomaly-detect-reg\",\n",
    "    version = 1,\n",
    "    stage = \"Production\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f874b4b-59fd-446f-b576-6b92b0e5b91a",
   "metadata": {},
   "source": [
    "#### Serving the model & Inference\n",
    "- env variable : MLFLOW_TRACKING_URI = 'http://localhost:5000'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "620c1669-4e41-4692-8f28-9748bae0267c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set tracking uri\n",
    "mlflow.set_tracking_uri('http://localhost:5000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "c8ac832f-0bca-4fc5-a92b-634560c04bf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import requests\n",
    "# import json\n",
    "\n",
    "# sample = dataset_test[1]\n",
    "# payload = json.dumps({'inputs': [{'data':sample.tolist(), \"datatype\": \"FP64\"}]})\n",
    "# headers = {'content-type': 'application/json'}\n",
    "\n",
    "# endpoint = \"http://localhost:1255/invocations\"\n",
    "\n",
    "# response = requests.post(endpoint, data = payload, headers = headers)\n",
    "# print(response.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ce3f5a9-e7b1-4968-9c60-86cd46b743c4",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 6.5 Comparison of the PR curve for different feature scaling approaches\n",
    "- Achieved a better F1 score without performing any feature scaling.\n",
    "- Each models are trained for 150 epochs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf945008-272c-4033-a7eb-5000c7c117b6",
   "metadata": {},
   "source": [
    "![image](./plots/pr_curves.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fa2e576-046d-41f0-8ef1-bdda2f80d5e2",
   "metadata": {},
   "source": [
    "### 6.6 Visualizing Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "fFP6L4CklVpv",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 488
    },
    "executionInfo": {
     "elapsed": 320,
     "status": "ok",
     "timestamp": 1683586823352,
     "user": {
      "displayName": "Vivek Cherangil Ramankutty",
      "userId": "08051752269221227298"
     },
     "user_tz": -120
    },
    "id": "fFP6L4CklVpv",
    "outputId": "d9a603b8-e2b5-42f7-e112-32551d3ed2a8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>label</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>133</th>\n",
       "      <th>134</th>\n",
       "      <th>135</th>\n",
       "      <th>136</th>\n",
       "      <th>137</th>\n",
       "      <th>138</th>\n",
       "      <th>139</th>\n",
       "      <th>140</th>\n",
       "      <th>loss</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.030195</td>\n",
       "      <td>-3.473507</td>\n",
       "      <td>-4.202599</td>\n",
       "      <td>-4.504882</td>\n",
       "      <td>-3.358768</td>\n",
       "      <td>-2.129900</td>\n",
       "      <td>-1.807276</td>\n",
       "      <td>-1.316215</td>\n",
       "      <td>-0.442324</td>\n",
       "      <td>...</td>\n",
       "      <td>1.405561</td>\n",
       "      <td>1.246552</td>\n",
       "      <td>1.098543</td>\n",
       "      <td>0.658596</td>\n",
       "      <td>0.806567</td>\n",
       "      <td>0.540006</td>\n",
       "      <td>1.419833</td>\n",
       "      <td>0.932755</td>\n",
       "      <td>8.110489</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.510468</td>\n",
       "      <td>-0.267296</td>\n",
       "      <td>-2.225971</td>\n",
       "      <td>-3.350706</td>\n",
       "      <td>-4.310554</td>\n",
       "      <td>-3.906900</td>\n",
       "      <td>-2.596504</td>\n",
       "      <td>-1.800355</td>\n",
       "      <td>-1.674052</td>\n",
       "      <td>...</td>\n",
       "      <td>0.443063</td>\n",
       "      <td>0.924243</td>\n",
       "      <td>1.070205</td>\n",
       "      <td>1.759431</td>\n",
       "      <td>2.198114</td>\n",
       "      <td>2.102171</td>\n",
       "      <td>1.228120</td>\n",
       "      <td>0.733822</td>\n",
       "      <td>7.621335</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.177978</td>\n",
       "      <td>-1.785999</td>\n",
       "      <td>-3.464569</td>\n",
       "      <td>-4.099926</td>\n",
       "      <td>-3.729192</td>\n",
       "      <td>-2.758933</td>\n",
       "      <td>-1.943406</td>\n",
       "      <td>-1.727890</td>\n",
       "      <td>-1.031499</td>\n",
       "      <td>...</td>\n",
       "      <td>1.181497</td>\n",
       "      <td>1.603270</td>\n",
       "      <td>1.857259</td>\n",
       "      <td>1.819548</td>\n",
       "      <td>1.535259</td>\n",
       "      <td>1.635084</td>\n",
       "      <td>2.126696</td>\n",
       "      <td>1.210875</td>\n",
       "      <td>7.085220</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.123075</td>\n",
       "      <td>-2.682445</td>\n",
       "      <td>-4.002048</td>\n",
       "      <td>-4.191883</td>\n",
       "      <td>-4.176793</td>\n",
       "      <td>-3.039296</td>\n",
       "      <td>-1.914833</td>\n",
       "      <td>-1.777830</td>\n",
       "      <td>-1.343534</td>\n",
       "      <td>...</td>\n",
       "      <td>1.485032</td>\n",
       "      <td>1.228445</td>\n",
       "      <td>0.913344</td>\n",
       "      <td>0.439609</td>\n",
       "      <td>0.214300</td>\n",
       "      <td>0.415868</td>\n",
       "      <td>1.259138</td>\n",
       "      <td>-0.726195</td>\n",
       "      <td>5.908807</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 143 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label         1         2         3         4         5         6  \\\n",
       "0    0.0 -1.030195 -3.473507 -4.202599 -4.504882 -3.358768 -2.129900   \n",
       "1    0.0  1.510468 -0.267296 -2.225971 -3.350706 -4.310554 -3.906900   \n",
       "2    0.0  0.177978 -1.785999 -3.464569 -4.099926 -3.729192 -2.758933   \n",
       "3    0.0  0.123075 -2.682445 -4.002048 -4.191883 -4.176793 -3.039296   \n",
       "\n",
       "          7         8         9  ...       133       134       135       136  \\\n",
       "0 -1.807276 -1.316215 -0.442324  ...  1.405561  1.246552  1.098543  0.658596   \n",
       "1 -2.596504 -1.800355 -1.674052  ...  0.443063  0.924243  1.070205  1.759431   \n",
       "2 -1.943406 -1.727890 -1.031499  ...  1.181497  1.603270  1.857259  1.819548   \n",
       "3 -1.914833 -1.777830 -1.343534  ...  1.485032  1.228445  0.913344  0.439609   \n",
       "\n",
       "        137       138       139       140      loss  pred  \n",
       "0  0.806567  0.540006  1.419833  0.932755  8.110489   0.0  \n",
       "1  2.198114  2.102171  1.228120  0.733822  7.621335   0.0  \n",
       "2  1.535259  1.635084  2.126696  1.210875  7.085220   0.0  \n",
       "3  0.214300  0.415868  1.259138 -0.726195  5.908807   0.0  \n",
       "\n",
       "[4 rows x 143 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_samples.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "ab6a2038",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sampling with fixed seed - normal/anomaly samples\n",
    "features = np.arange(1, 141)\n",
    "normal_samples = df_samples[df_samples.label==0].sample(8, random_state=42).astype(np.float32)\n",
    "normal_samples = torch.tensor(normal_samples[features].values).unsqueeze(2)\n",
    "\n",
    "anomaly_samples = df_samples[df_samples.label==1].sample(8, random_state=42).astype(np.float32)\n",
    "anomaly_samples = torch.tensor(anomaly_samples[features].values).unsqueeze(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "47625a00-51ee-4733-9745-3f04f1df329e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# func for plotting samples\n",
    "def plot_samples(samples, model, title, device='cpu'):\n",
    "    \"\"\" Function that plots the actual samples and the corresponding reconstructions\n",
    "    Args:\n",
    "        samples (torch.tensor): samples to plot\n",
    "        model (torch.nn.Module): model\n",
    "        title (string): suptitle of the plot\n",
    "        device (string): device to use - cpu/cuda:0\n",
    "    \"\"\"\n",
    "    samples_pred = []\n",
    "    criterion = nn.L1Loss(reduction='sum').to(device)\n",
    "    loss_pred = []\n",
    "    \n",
    "    for i in samples:\n",
    "        pred = model(i)\n",
    "        samples_pred.append(pred)\n",
    "        loss_pred.append(criterion(pred,i))\n",
    "        \n",
    "    samples_pred = torch.stack(samples_pred).squeeze(2).tolist()\n",
    "    samples_truth = samples.squeeze(2).tolist()\n",
    "    \n",
    "    fig, axs = plt.subplots(2, 4, figsize=(14,4.5))\n",
    "    for ax, p, t, l in zip(axs.flatten(), samples_pred, samples_truth, loss_pred):\n",
    "        ax.plot(np.arange(0, len(t)), t, color='tab:blue', label='Truth')\n",
    "        ax.plot(np.arange(0, len(p)), p, color='tab:red', label='Prediction')\n",
    "        ax.set_title(f'Loss: {round(l.item(), 3)}')\n",
    "        ax.set_xlabel('Timesteps')\n",
    "        ax.set_ylabel('Voltage (amplitudes)')\n",
    "        ax.legend()\n",
    "        \n",
    "    plt.suptitle(title)\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "fc400570",
   "metadata": {
    "id": "fc400570"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x450 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normal Samples\n",
    "plot_samples(normal_samples, model, title='Normal samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "fa404929-7c64-4155-89d9-46fe7ccceed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x450 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Anomaly Samples\n",
    "plot_samples(anomaly_samples, model, title='Anomaly samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f4c508-bdc1-45d1-b9a5-bc40319a5ede",
   "metadata": {},
   "source": [
    "- The lower reconstruction error for normal samples and the higher reconstruction error for anomaly samples indicate that the autoencoder model is able to differentiate between the two classes."
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [
    "d020bea2",
    "90b8ab1c",
    "a432c43f"
   ],
   "gpuType": "T4",
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python (interview)",
   "language": "python",
   "name": "interview"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}