11_ml_attemt1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "# read data from csv\n",
    "df = pd.read_csv('all_data_541983310278725782.csv')\n",
    "\n",
    "# rename columns: timestamp, connected to Date, Value\n",
    "df.rename(columns={'timestamp': 'Date', 'connected': 'Value'}, inplace=True)\n",
    "\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df.set_index('Date', inplace=True)\n",
    "\n",
    "\n",
    "df['Value'] = df['Value'].astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_size = int(len(df) * 0.7)\n",
    "val_size = int(len(df) * 0.2)\n",
    "test_size = len(df) - train_size - val_size\n",
    "\n",
    "train, val, test = df.iloc[0:train_size], df.iloc[train_size:train_size+val_size], df.iloc[train_size+val_size:len(df)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "train_scaled = scaler.fit_transform(train)\n",
    "val_scaled = scaler.transform(val)\n",
    "test_scaled = scaler.transform(test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def create_dataset(X, y, time_steps=1):\n",
    "    Xs, ys = [], []\n",
    "    for i in range(len(X) - time_steps):\n",
    "        v = X.iloc[i:(i + time_steps)].values\n",
    "        Xs.append(v)\n",
    "        ys.append(y.iloc[i + time_steps])\n",
    "    return np.array(Xs), np.array(ys)\n",
    "\n",
    "time_steps = 10\n",
    "\n",
    "X_train, y_train = create_dataset(train, train.Value, time_steps)\n",
    "X_val, y_val = create_dataset(val, val.Value, time_steps)\n",
    "X_test, y_test = create_dataset(test, test.Value, time_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/niaki/Workspace/personal/Elia-Hackathon-Steering/.venv/lib/python3.9/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9224 - loss: 0.2656 - val_accuracy: 0.9731 - val_loss: 0.0835\n",
      "Epoch 2/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9796 - loss: 0.0854 - val_accuracy: 0.9864 - val_loss: 0.0691\n",
      "Epoch 3/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0739 - val_accuracy: 0.9866 - val_loss: 0.0670\n",
      "Epoch 4/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0723 - val_accuracy: 0.9864 - val_loss: 0.0663\n",
      "Epoch 5/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0717 - val_accuracy: 0.9864 - val_loss: 0.0661\n",
      "Epoch 6/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0712 - val_accuracy: 0.9864 - val_loss: 0.0659\n",
      "Epoch 7/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9856 - loss: 0.0708 - val_accuracy: 0.9863 - val_loss: 0.0660\n",
      "Epoch 8/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9856 - loss: 0.0705 - val_accuracy: 0.9862 - val_loss: 0.0662\n",
      "Epoch 9/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0702 - val_accuracy: 0.9862 - val_loss: 0.0665\n",
      "Epoch 10/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0700 - val_accuracy: 0.9860 - val_loss: 0.0670\n",
      "Epoch 11/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0697 - val_accuracy: 0.9860 - val_loss: 0.0675\n",
      "Epoch 12/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0694 - val_accuracy: 0.9860 - val_loss: 0.0679\n",
      "Epoch 13/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9854 - loss: 0.0712 - val_accuracy: 0.9860 - val_loss: 0.0674\n",
      "Epoch 14/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0692 - val_accuracy: 0.9860 - val_loss: 0.0681\n",
      "Epoch 15/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0691 - val_accuracy: 0.9860 - val_loss: 0.0683\n",
      "Epoch 16/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0690 - val_accuracy: 0.9860 - val_loss: 0.0683\n",
      "Epoch 17/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0690 - val_accuracy: 0.9860 - val_loss: 0.0684\n",
      "Epoch 18/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0690 - val_accuracy: 0.9860 - val_loss: 0.0685\n",
      "Epoch 19/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9856 - loss: 0.0689 - val_accuracy: 0.9860 - val_loss: 0.0687\n",
      "Epoch 20/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9856 - loss: 0.0689 - val_accuracy: 0.9860 - val_loss: 0.0689\n",
      "Epoch 21/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0691 - val_accuracy: 0.9860 - val_loss: 0.0677\n",
      "Epoch 22/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0689 - val_accuracy: 0.9860 - val_loss: 0.0682\n",
      "Epoch 23/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0688 - val_accuracy: 0.9860 - val_loss: 0.0685\n",
      "Epoch 24/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0688 - val_accuracy: 0.9860 - val_loss: 0.0685\n",
      "Epoch 25/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0689 - val_accuracy: 0.9864 - val_loss: 0.0659\n",
      "Epoch 26/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9856 - loss: 0.0693 - val_accuracy: 0.9863 - val_loss: 0.0671\n",
      "Epoch 27/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0690 - val_accuracy: 0.9863 - val_loss: 0.0673\n",
      "Epoch 28/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0688 - val_accuracy: 0.9863 - val_loss: 0.0674\n",
      "Epoch 29/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0688 - val_accuracy: 0.9863 - val_loss: 0.0674\n",
      "Epoch 30/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0688 - val_accuracy: 0.9862 - val_loss: 0.0676\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(128, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(\n",
    "    X_train, y_train,\n",
    "    epochs=30,\n",
    "    batch_size=32,\n",
    "    validation_data=(X_val, y_val),\n",
    "    shuffle=False\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m119/119\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m119/119\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9866 - loss: 0.0651\n",
      "Test loss: 0.0663071945309639\n",
      "Test accuracy: 0.9862869381904602\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "print('Test loss:', loss)\n",
    "print('Test accuracy:', accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history.history['loss'], label='Train Loss')\n",
    "plt.plot(history.history['val_loss'], label='Val Loss')\n",
    "plt.title('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='Val Accuracy')\n",
    "plt.title('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m  1/119\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m119/119\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, roc_auc_score\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# print(classification_report(y_test, y_pred))\n",
    "\n",
    "# roc_auc = roc_auc_score(y_test, y_pred)\n",
    "# print('AUC-ROC:', roc_auc)\n",
    "\n",
    "\n",
    "# plot y_test and y_pred\n",
    "plt.figure(figsize=(12, 3))\n",
    "plt.plot(y_test, label='True')\n",
    "plt.plot(y_pred, label='Predicted')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model\n",
    "model.save('models/model_541983310278725782.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}