ML/training-keras.ipynb

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   "source": [
    "# ML Model Example\n",
    "\n",
    "This example provides a simply query tutorial for InfluxDB Cloud Dedicated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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     "name": "stderr",
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     "text": [
      "2023-11-02 18:36:23.264879: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "/Users/jayclifford/Documents/repos/Apache-Arrow-Tutorial/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import influxdb_client_3 as InfluxDBClient3\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from keras.layers import Input, Dense, LSTM, RepeatVector, TimeDistributed, BatchNormalization, Normalization\n",
    "from keras.models import Model\n",
    "from keras.callbacks import TensorBoard\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Model, load_model\n",
    "from keras.layers import Input, Dense, LSTM, RepeatVector, TimeDistributed,  Conv1D, Bidirectional\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "\n",
    "import plotly.io as pio\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from huggingface_hub import push_to_hub_keras\n",
    "pio.renderers.default = \"vscode\"\n",
    "import os, datetime\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "token = ''\n",
    "host = 'eu-central-1-1.aws.cloud2.influxdata.com'    \n",
    "org = 'Jay-IOx'\n",
    "db = 'factory'\n",
    "\n",
    "client = InfluxDBClient3.InfluxDBClient3(token=token,\n",
    "                         host=host,\n",
    "                         org=org,\n",
    "                         database=db) \n",
    "    \n",
    "    \n"
   ]
  },
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   "execution_count": 3,
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      "                            vibration\n",
      "time                                 \n",
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      "2023-11-02 18:06:34.978106         89\n",
      "2023-11-02 18:06:35.504956         89\n",
      "...                               ...\n",
      "2023-11-02 18:27:31.614922         87\n",
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      "2023-11-02 18:27:32.610040         86\n",
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      "2023-11-02 18:27:33.610810         87\n",
      "\n",
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           "bgcolor": "#E5ECF6",
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          "title": {
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          "xaxis": {
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   ],
   "source": [
    "query = '''SELECT vibration FROM machine_data WHERE \"machineID\" = 'machine2' AND time >= now() - 30m'''\n",
    "table = client.query(query=query, language=\"influxql\")\n",
    "normal_data = table.to_pandas().drop(columns=['iox::measurement']).set_index('time')\n",
    "print(normal_data)\n",
    "fig = px.line(normal_data, y=[\"vibration\"], title='raw vibration')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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          "2023-11-02T18:06:39.998913",
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            "gridwidth": 2,
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           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
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          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
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           "zerolinewidth": 2
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          "yaxis": {
           "automargin": true,
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        },
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         "text": "raw vibration"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
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         "title": {
          "text": "time"
         }
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "query = '''SELECT vibration FROM machine_data WHERE \"machineID\" = 'machine1' AND time >= now() - 30m'''\n",
    "table = client.query(query=query, language=\"influxql\")\n",
    "anom_data = table.to_pandas().drop(columns=['iox::measurement']).set_index('time')\n",
    "fig2 = px.line(anom_data, y=[\"vibration\"], title='raw vibration')\n",
    "fig2.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "95/95 [==============================] - 7s 24ms/step - loss: 0.1891 - val_loss: 0.1326\n",
      "Epoch 2/100\n",
      "95/95 [==============================] - 2s 17ms/step - loss: 0.1287 - val_loss: 0.1246\n",
      "Epoch 3/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1241 - val_loss: 0.1218\n",
      "Epoch 4/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1218 - val_loss: 0.1201\n",
      "Epoch 5/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1204 - val_loss: 0.1194\n",
      "Epoch 6/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1200 - val_loss: 0.1178\n",
      "Epoch 7/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1189 - val_loss: 0.1170\n",
      "Epoch 8/100\n",
      "95/95 [==============================] - 1s 16ms/step - loss: 0.1187 - val_loss: 0.1167\n",
      "Epoch 9/100\n",
      "95/95 [==============================] - 1s 16ms/step - loss: 0.1184 - val_loss: 0.1169\n",
      "Epoch 10/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1175 - val_loss: 0.1163\n",
      "Epoch 11/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1171 - val_loss: 0.1155\n",
      "Epoch 12/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1170 - val_loss: 0.1154\n",
      "Epoch 13/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1171 - val_loss: 0.1156\n",
      "Epoch 14/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1166 - val_loss: 0.1158\n",
      "Epoch 15/100\n",
      "95/95 [==============================] - 2s 16ms/step - loss: 0.1163 - val_loss: 0.1150\n",
      "Epoch 16/100\n",
      "95/95 [==============================] - 2s 16ms/step - loss: 0.1164 - val_loss: 0.1147\n",
      "Epoch 17/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1162 - val_loss: 0.1162\n",
      "Epoch 18/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1167 - val_loss: 0.1152\n",
      "Epoch 19/100\n",
      "95/95 [==============================] - 1s 16ms/step - loss: 0.1162 - val_loss: 0.1145\n",
      "Epoch 20/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1165 - val_loss: 0.1145\n",
      "Epoch 21/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1160 - val_loss: 0.1146\n",
      "Epoch 22/100\n",
      "95/95 [==============================] - 2s 16ms/step - loss: 0.1159 - val_loss: 0.1143\n",
      "Epoch 23/100\n",
      "95/95 [==============================] - 2s 16ms/step - loss: 0.1159 - val_loss: 0.1146\n",
      "Epoch 24/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1160 - val_loss: 0.1146\n",
      "Epoch 25/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1159 - val_loss: 0.1148\n",
      "Epoch 26/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1160 - val_loss: 0.1142\n",
      "Epoch 27/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1157 - val_loss: 0.1145\n",
      "Epoch 28/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1160 - val_loss: 0.1145\n",
      "Epoch 29/100\n",
      "95/95 [==============================] - 2s 16ms/step - loss: 0.1157 - val_loss: 0.1141\n",
      "Epoch 30/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1154 - val_loss: 0.1153\n",
      "Epoch 31/100\n",
      "95/95 [==============================] - 1s 16ms/step - loss: 0.1159 - val_loss: 0.1146\n",
      "Epoch 32/100\n",
      "95/95 [==============================] - 2s 16ms/step - loss: 0.1159 - val_loss: 0.1140\n",
      "Epoch 33/100\n",
      "95/95 [==============================] - 2s 16ms/step - loss: 0.1158 - val_loss: 0.1145\n",
      "Epoch 34/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1157 - val_loss: 0.1141\n",
      "Epoch 35/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1156 - val_loss: 0.1154\n",
      "Epoch 36/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1160 - val_loss: 0.1142\n",
      "Epoch 37/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1155 - val_loss: 0.1143\n",
      "Epoch 38/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1154 - val_loss: 0.1149\n",
      "Epoch 39/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1155 - val_loss: 0.1141\n",
      "Epoch 40/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1157 - val_loss: 0.1141\n",
      "Epoch 41/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1159 - val_loss: 0.1145\n",
      "Epoch 42/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1156 - val_loss: 0.1146\n",
      "Epoch 43/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1154 - val_loss: 0.1139\n",
      "Epoch 44/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1156 - val_loss: 0.1139\n",
      "Epoch 45/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1154 - val_loss: 0.1142\n",
      "Epoch 46/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1153 - val_loss: 0.1143\n",
      "Epoch 47/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1152 - val_loss: 0.1146\n",
      "Epoch 48/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1156 - val_loss: 0.1154\n",
      "Epoch 49/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1149 - val_loss: 0.1147\n",
      "Epoch 50/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1156 - val_loss: 0.1147\n",
      "Epoch 51/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1152 - val_loss: 0.1139\n",
      "Epoch 52/100\n",
      "95/95 [==============================] - 2s 17ms/step - loss: 0.1155 - val_loss: 0.1139\n",
      "Epoch 53/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1154 - val_loss: 0.1139\n",
      "Epoch 54/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1157 - val_loss: 0.1139\n",
      "Epoch 55/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1151 - val_loss: 0.1155\n",
      "Epoch 56/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1157 - val_loss: 0.1139\n",
      "Epoch 57/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1155 - val_loss: 0.1138\n",
      "Epoch 58/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1157 - val_loss: 0.1139\n",
      "Epoch 59/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1153 - val_loss: 0.1138\n",
      "Epoch 60/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1154 - val_loss: 0.1138\n",
      "Epoch 61/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1154 - val_loss: 0.1144\n",
      "Epoch 62/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1154 - val_loss: 0.1138\n",
      "Epoch 63/100\n",
      "95/95 [==============================] - 1s 16ms/step - loss: 0.1154 - val_loss: 0.1138\n",
      "Epoch 64/100\n",
      "95/95 [==============================] - 1s 16ms/step - loss: 0.1157 - val_loss: 0.1143\n",
      "Epoch 65/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1154 - val_loss: 0.1138\n",
      "Epoch 66/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1157 - val_loss: 0.1138\n",
      "Epoch 67/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1154 - val_loss: 0.1139\n",
      "Epoch 68/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1152 - val_loss: 0.1138\n",
      "Epoch 69/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1156 - val_loss: 0.1140\n",
      "Epoch 70/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1154 - val_loss: 0.1138\n",
      "Epoch 71/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1152 - val_loss: 0.1139\n",
      "Epoch 72/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1154 - val_loss: 0.1139\n",
      "Epoch 73/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1154 - val_loss: 0.1140\n",
      "Epoch 74/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1152 - val_loss: 0.1141\n",
      "Epoch 75/100\n",
      "95/95 [==============================] - 2s 16ms/step - loss: 0.1154 - val_loss: 0.1140\n",
      "Epoch 76/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1153 - val_loss: 0.1138\n",
      "Epoch 77/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1154 - val_loss: 0.1138\n",
      "Epoch 78/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1153 - val_loss: 0.1137\n",
      "Epoch 79/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1153 - val_loss: 0.1142\n",
      "Epoch 80/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1155 - val_loss: 0.1138\n",
      "Epoch 81/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1154 - val_loss: 0.1141\n",
      "Epoch 82/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1152 - val_loss: 0.1139\n",
      "Epoch 83/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1154 - val_loss: 0.1138\n",
      "Epoch 84/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1153 - val_loss: 0.1148\n",
      "Epoch 85/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1155 - val_loss: 0.1140\n",
      "Epoch 86/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1154 - val_loss: 0.1139\n",
      "Epoch 87/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1151 - val_loss: 0.1147\n",
      "Epoch 88/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1153 - val_loss: 0.1139\n",
      "Epoch 89/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1152 - val_loss: 0.1138\n",
      "Epoch 90/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1153 - val_loss: 0.1138\n",
      "Epoch 91/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1153 - val_loss: 0.1138\n",
      "Epoch 92/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1154 - val_loss: 0.1140\n",
      "Epoch 93/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1153 - val_loss: 0.1138\n",
      "Epoch 94/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1153 - val_loss: 0.1141\n",
      "Epoch 95/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1154 - val_loss: 0.1139\n",
      "Epoch 96/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1153 - val_loss: 0.1138\n",
      "Epoch 97/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1152 - val_loss: 0.1140\n",
      "Epoch 98/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1154 - val_loss: 0.1138\n",
      "Epoch 99/100\n",
      "95/95 [==============================] - 1s 15ms/step - loss: 0.1152 - val_loss: 0.1144\n",
      "Epoch 100/100\n",
      "95/95 [==============================] - 1s 14ms/step - loss: 0.1153 - val_loss: 0.1141\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Layer\n",
    "import tensorflow as tf\n",
    "\n",
    "class SequenceLayer(Layer):\n",
    "    def __init__(self, timesteps, **kwargs):\n",
    "        self.timesteps = timesteps\n",
    "        super(SequenceLayer, self).__init__(**kwargs)\n",
    "\n",
    "    def build(self, input_shape):\n",
    "        super(SequenceLayer, self).build(input_shape)\n",
    "\n",
    "    def call(self, x):\n",
    "        sequences = []\n",
    "        for i in range(self.timesteps):\n",
    "            sequences.append(x[i:-(self.timesteps-i-1)])\n",
    "        return tf.stack(sequences, axis=1)\n",
    "\n",
    "    def compute_output_shape(self, input_shape):\n",
    "        return (input_shape[0], self.timesteps, input_shape[1])\n",
    "\n",
    "# Preprocess the data\n",
    "scaler = MinMaxScaler()\n",
    "df_scaled = scaler.fit_transform(normal_data)\n",
    "\n",
    "# Build the autoencoder model\n",
    "input_dim = df_scaled.shape[1]  # number of features\n",
    "timesteps = 35  # you can change this based on your sequence length\n",
    "\n",
    "inputs = Input(shape=(input_dim,))\n",
    "sequences = SequenceLayer(timesteps)(inputs)\n",
    "inputs = Input(shape=(timesteps, input_dim))\n",
    "encoded = LSTM(16, activation='relu', return_sequences=True)(inputs)\n",
    "encoded = LSTM(4, activation='relu', return_sequences=False)(encoded)\n",
    "decoded = RepeatVector(timesteps)(encoded)\n",
    "decoded = LSTM(4, activation='relu', return_sequences=True)(decoded)\n",
    "decoded = LSTM(16, activation='relu', return_sequences=True)(decoded)\n",
    "decoded = TimeDistributed(Dense(input_dim))(decoded)\n",
    "\n",
    "autoencoder = Model(inputs, decoded)\n",
    "\n",
    "autoencoder.compile(optimizer='adam', loss='mse')\n",
    "\n",
    "# Train the model\n",
    "X_train, X_test = train_test_split(df_scaled, test_size=0.2, random_state=42)\n",
    "\n",
    "# Directory to store logs\n",
    "log_dir = os.path.join(\n",
    "    \"logs\",\n",
    "    \"fit\",\n",
    "    datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"),\n",
    ")\n",
    "\n",
    "# Creating the TensorBoard callback\n",
    "tensorboard_callback = TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
    "\n",
    "history = autoencoder.fit(X_train, X_train, epochs=100, batch_size=16, validation_data=(X_test, X_test), verbose=1, callbacks=[tensorboard_callback])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    " # Use the Autoencoder to predict on the anomalous data\n",
    "predictions = autoencoder.predict(anom_data)\n",
    "\n",
    "# Calculate reconstruction error for each sequence\n",
    "mse = np.mean(np.power(anom_data.values[-predictions.shape[0]:] - predictions[:, -1, :], 2), axis=1)\n",
    "\n",
    "# Scale the MSE to a percentage\n",
    "min_mse = np.min(mse)\n",
    "max_mse = np.max(mse)\n",
    "mse_percentage = ((mse - min_mse) / (max_mse - min_mse)) * 100\n",
    "\n",
    "# Detect anomalies by comparing the scaled reconstruction error to some threshold\n",
    "threshold = 82  # Define a threshold value (in percentage)\n",
    "\n",
    "# Add 'is_anomalous' column to the DataFrame\n",
    "anom_data = anom_data.iloc[-predictions.shape[0]:].copy()\n",
    "anom_data['is_anomalous'] = mse_percentage > threshold\n",
    "anom_data['mse_percentage'] = mse_percentage\n",
    "\n",
    "\n",
    "# Create a new figure\n",
    "fig = go.Figure()\n",
    "\n",
    "\n",
    "# Add a line for the actual load\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=anom_data.index,\n",
    "    y=anom_data[\"vibration\"],\n",
    "    mode='lines',\n",
    "    name='vibration',\n",
    "    line=dict(color='green'),  # specify line color here\n",
    "))\n",
    "\n",
    "# Add a line for the anomalous load\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=anom_data[anom_data['is_anomalous']].index,\n",
    "    y=anom_data[anom_data['is_anomalous']][\"vibration\"],\n",
    "    mode='markers',\n",
    "    name='Anomalous Vibration',\n",
    "    marker=dict(color='red', size=5),  # specify marker color and size here\n",
    "))\n",
    "\n",
    "# Show the plot\n",
    "fig.show()\n",
    "anom_data.to_csv('anomalous_data_result.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    " # Use the Autoencoder to predict on the anomalous data\n",
    "predictions = autoencoder.predict(normal_data)\n",
    "\n",
    "# Calculate reconstruction error for each sequence\n",
    "mse = np.mean(np.power(normal_data.values[-predictions.shape[0]:] - predictions[:, -1, :], 2), axis=1)\n",
    "\n",
    "# Scale the MSE to a percentage\n",
    "min_mse = np.min(mse)\n",
    "max_mse = np.max(mse)\n",
    "mse_percentage = ((mse - min_mse) / (max_mse - min_mse)) * 100\n",
    "\n",
    "# Detect anomalies by comparing the scaled reconstruction error to some threshold\n",
    "threshold = 82  # Define a threshold value (in percentage)\n",
    "\n",
    "# Add 'is_anomalous' column to the DataFrame\n",
    "normal_data = normal_data.iloc[-predictions.shape[0]:].copy()\n",
    "normal_data['is_anomalous'] = mse_percentage > threshold\n",
    "normal_data['mse_percentage'] = mse_percentage\n",
    "\n",
    "\n",
    "# Create a new figure\n",
    "fig = go.Figure()\n",
    "\n",
    "\n",
    "# Add a line for the actual load\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=normal_data.index,\n",
    "    y=normal_data[\"vibration\"],\n",
    "    mode='lines',\n",
    "    name='vibration',\n",
    "    line=dict(color='green'),  # specify line color here\n",
    "))\n",
    "\n",
    "# Add a line for the anomalous load\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=normal_data[normal_data['is_anomalous']].index,\n",
    "    y=normal_data[normal_data['is_anomalous']][\"vibration\"],\n",
    "    mode='markers',\n",
    "    name='Anomalous Vibration',\n",
    "    marker=dict(color='red', size=5),  # specify marker color and size here\n",
    "))\n",
    "\n",
    "# Show the plot\n",
    "fig.show()\n",
    "normal_data.to_csv('normal_data_result.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = '''SELECT vibration FROM machine_data WHERE \"machineID\" = 'machine3' AND time >= now() - 1m'''\n",
    "table = client.query(query=query, language=\"influxql\")\n",
    "test_data = table.to_pandas().drop(columns=['iox::measurement']).set_index('time')\n",
    "\n",
    "\n",
    " # Use the Autoencoder to predict on the anomalous data\n",
    "predictions = autoencoder.predict(test_data)\n",
    "\n",
    "# Calculate reconstruction error for each sequence\n",
    "mse = np.mean(np.power(test_data.values[-predictions.shape[0]:] - predictions[:, -1, :], 2), axis=1)\n",
    "\n",
    "# Scale the MSE to a percentage\n",
    "min_mse = np.min(mse)\n",
    "max_mse = np.max(mse)\n",
    "mse_percentage = ((mse - min_mse) / (max_mse - min_mse)) * 100\n",
    "\n",
    "# Detect anomalies by comparing the scaled reconstruction error to some threshold\n",
    "threshold = 83  # Define a threshold value (in percentage)\n",
    "\n",
    "# Add 'is_anomalous' column to the DataFrame\n",
    "test_data = test_data.iloc[-predictions.shape[0]:].copy()\n",
    "test_data['is_anomalous'] = mse_percentage > threshold\n",
    "test_data['mse_percentage'] = mse_percentage\n",
    "\n",
    "\n",
    "# Create a new figure\n",
    "fig = go.Figure()\n",
    "\n",
    "\n",
    "# Add a line for the actual load\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=test_data.index,\n",
    "    y=test_data[\"vibration\"],\n",
    "    mode='lines',\n",
    "    name='vibration',\n",
    "    line=dict(color='green'),  # specify line color here\n",
    "))\n",
    "\n",
    "# Add a line for the anomalous load\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=test_data[test_data['is_anomalous']].index,\n",
    "    y=test_data[test_data['is_anomalous']][\"vibration\"],\n",
    "    mode='markers',\n",
    "    name='Anomalous Vibration',\n",
    "    marker=dict(color='red', size=5),  # specify marker color and size here\n",
    "))\n",
    "\n",
    "# Show the plot\n",
    "fig.show()\n",
    "test_data.to_csv('test_data_result.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "push_to_hub_keras(model = autoencoder,\n",
    "    repo_id = \"jayclifford345/vibration-autoencoder\",\n",
    "    tags = [\"anomaly-detection\", \"vibration\", \"autoencoder\"],\n",
    "    plot_model=True,\n",
    "    log_dir=log_dir,\n",
    "    token='',\n",
    "    include_optimizer=True,\n",
    "    config=autoencoder.get_config()\n",
    ")"
   ]
  }
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