BAse_Lstm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a98d6431",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(76, 5, 4)\n",
      "(76,)\n",
      "Model: \"sequential_69\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_308 (LSTM)              (None, 5, 100)            42000     \n",
      "_________________________________________________________________\n",
      "dropout_308 (Dropout)        (None, 5, 100)            0         \n",
      "_________________________________________________________________\n",
      "lstm_309 (LSTM)              (None, 5, 100)            80400     \n",
      "_________________________________________________________________\n",
      "dropout_309 (Dropout)        (None, 5, 100)            0         \n",
      "_________________________________________________________________\n",
      "lstm_310 (LSTM)              (None, 5, 100)            80400     \n",
      "_________________________________________________________________\n",
      "dropout_310 (Dropout)        (None, 5, 100)            0         \n",
      "_________________________________________________________________\n",
      "lstm_311 (LSTM)              (None, 5, 100)            80400     \n",
      "_________________________________________________________________\n",
      "dropout_311 (Dropout)        (None, 5, 100)            0         \n",
      "_________________________________________________________________\n",
      "lstm_312 (LSTM)              (None, 5, 150)            150600    \n",
      "_________________________________________________________________\n",
      "dropout_312 (Dropout)        (None, 5, 150)            0         \n",
      "_________________________________________________________________\n",
      "lstm_313 (LSTM)              (None, 200)               280800    \n",
      "_________________________________________________________________\n",
      "dropout_313 (Dropout)        (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dense_69 (Dense)             (None, 1)                 201       \n",
      "=================================================================\n",
      "Total params: 714,801\n",
      "Trainable params: 714,801\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/80\n",
      "3/3 [==============================] - 19s 1s/step - loss: 0.4383 - val_loss: 0.4190\n",
      "Epoch 2/80\n",
      "3/3 [==============================] - 0s 73ms/step - loss: 0.3226 - val_loss: 0.6492\n",
      "Epoch 3/80\n",
      "3/3 [==============================] - 0s 83ms/step - loss: 0.1345 - val_loss: 1.3937\n",
      "Epoch 4/80\n",
      "3/3 [==============================] - 0s 70ms/step - loss: 0.1222 - val_loss: 1.2143\n",
      "Epoch 5/80\n",
      "3/3 [==============================] - 0s 72ms/step - loss: 0.0739 - val_loss: 0.9153\n",
      "Epoch 6/80\n",
      "3/3 [==============================] - 0s 67ms/step - loss: 0.0781 - val_loss: 0.8444\n",
      "Epoch 7/80\n",
      "3/3 [==============================] - 0s 66ms/step - loss: 0.0762 - val_loss: 0.8498\n",
      "Epoch 8/80\n",
      "3/3 [==============================] - 0s 64ms/step - loss: 0.0520 - val_loss: 0.8333\n",
      "Epoch 9/80\n",
      "3/3 [==============================] - 0s 64ms/step - loss: 0.0491 - val_loss: 0.6625\n",
      "Epoch 10/80\n",
      "3/3 [==============================] - 0s 67ms/step - loss: 0.0364 - val_loss: 0.4709\n",
      "Epoch 11/80\n",
      "3/3 [==============================] - 0s 75ms/step - loss: 0.0169 - val_loss: 0.3116\n",
      "Epoch 12/80\n",
      "3/3 [==============================] - 0s 89ms/step - loss: 0.0118 - val_loss: 0.1713\n",
      "Epoch 13/80\n",
      "3/3 [==============================] - 0s 68ms/step - loss: 0.0190 - val_loss: 0.1130\n",
      "Epoch 14/80\n",
      "3/3 [==============================] - 0s 66ms/step - loss: 0.0129 - val_loss: 0.0961\n",
      "Epoch 15/80\n",
      "3/3 [==============================] - 0s 71ms/step - loss: 0.0114 - val_loss: 0.0688\n",
      "Epoch 16/80\n",
      "3/3 [==============================] - 0s 67ms/step - loss: 0.0090 - val_loss: 0.0486\n",
      "Epoch 17/80\n",
      "3/3 [==============================] - 0s 67ms/step - loss: 0.0051 - val_loss: 0.0464\n",
      "Epoch 18/80\n",
      "3/3 [==============================] - 0s 78ms/step - loss: 0.0089 - val_loss: 0.0237\n",
      "Epoch 19/80\n",
      "3/3 [==============================] - 0s 82ms/step - loss: 0.0069 - val_loss: 0.0041\n",
      "Epoch 20/80\n",
      "3/3 [==============================] - 0s 67ms/step - loss: 0.0068 - val_loss: 0.0091\n",
      "Epoch 21/80\n",
      "3/3 [==============================] - 0s 96ms/step - loss: 0.0061 - val_loss: 0.0288\n",
      "Epoch 22/80\n",
      "3/3 [==============================] - 0s 70ms/step - loss: 0.0058 - val_loss: 0.0411\n",
      "Epoch 23/80\n",
      "3/3 [==============================] - 0s 81ms/step - loss: 0.0066 - val_loss: 0.0462\n",
      "Epoch 24/80\n",
      "3/3 [==============================] - 0s 70ms/step - loss: 0.0095 - val_loss: 0.0217\n",
      "Epoch 25/80\n",
      "3/3 [==============================] - 0s 67ms/step - loss: 0.0072 - val_loss: 0.0068\n",
      "Epoch 26/80\n",
      "3/3 [==============================] - 0s 64ms/step - loss: 0.0065 - val_loss: 0.0040\n",
      "Epoch 27/80\n",
      "3/3 [==============================] - 0s 66ms/step - loss: 0.0057 - val_loss: 0.0039\n",
      "Epoch 28/80\n",
      "3/3 [==============================] - 0s 93ms/step - loss: 0.0053 - val_loss: 0.0042\n",
      "Epoch 29/80\n",
      "3/3 [==============================] - 0s 69ms/step - loss: 0.0053 - val_loss: 0.0059\n",
      "Epoch 30/80\n",
      "3/3 [==============================] - 0s 66ms/step - loss: 0.0067 - val_loss: 0.0058\n",
      "Epoch 31/80\n",
      "3/3 [==============================] - 0s 74ms/step - loss: 0.0050 - val_loss: 0.0061\n",
      "Epoch 32/80\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.0052 - val_loss: 0.0085\n",
      "Epoch 33/80\n",
      "3/3 [==============================] - 0s 77ms/step - loss: 0.0054 - val_loss: 0.0109\n",
      "Epoch 34/80\n",
      "3/3 [==============================] - 0s 76ms/step - loss: 0.0059 - val_loss: 0.0160\n",
      "Epoch 35/80\n",
      "3/3 [==============================] - 0s 87ms/step - loss: 0.0060 - val_loss: 0.0103\n",
      "Epoch 36/80\n",
      "3/3 [==============================] - 0s 64ms/step - loss: 0.0054 - val_loss: 0.0074\n",
      "Epoch 37/80\n",
      "3/3 [==============================] - 0s 68ms/step - loss: 0.0064 - val_loss: 0.0130\n",
      "Epoch 38/80\n",
      "3/3 [==============================] - 0s 69ms/step - loss: 0.0065 - val_loss: 0.0161\n",
      "Epoch 39/80\n",
      "3/3 [==============================] - 0s 67ms/step - loss: 0.0052 - val_loss: 0.0084\n",
      "Epoch 40/80\n",
      "3/3 [==============================] - 0s 72ms/step - loss: 0.0078 - val_loss: 0.0116\n",
      "Epoch 41/80\n",
      "3/3 [==============================] - 0s 81ms/step - loss: 0.0045 - val_loss: 0.0146\n",
      "Epoch 42/80\n",
      "3/3 [==============================] - 0s 71ms/step - loss: 0.0058 - val_loss: 0.0084\n",
      "Epoch 43/80\n",
      "3/3 [==============================] - 0s 80ms/step - loss: 0.0052 - val_loss: 0.0090\n",
      "Epoch 44/80\n",
      "3/3 [==============================] - 0s 88ms/step - loss: 0.0069 - val_loss: 0.0140\n",
      "Epoch 45/80\n",
      "3/3 [==============================] - 0s 99ms/step - loss: 0.0049 - val_loss: 0.0113\n",
      "Epoch 46/80\n",
      "3/3 [==============================] - 0s 67ms/step - loss: 0.0042 - val_loss: 0.0104\n",
      "Epoch 47/80\n",
      "3/3 [==============================] - 0s 93ms/step - loss: 0.0060 - val_loss: 0.0170\n",
      "Epoch 48/80\n",
      "3/3 [==============================] - 0s 81ms/step - loss: 0.0068 - val_loss: 0.0074\n",
      "Epoch 49/80\n",
      "3/3 [==============================] - 0s 76ms/step - loss: 0.0080 - val_loss: 0.0059\n",
      "Epoch 50/80\n",
      "3/3 [==============================] - 0s 96ms/step - loss: 0.0055 - val_loss: 0.0085\n",
      "Epoch 51/80\n",
      "3/3 [==============================] - 0s 101ms/step - loss: 0.0050 - val_loss: 0.0054\n",
      "Epoch 52/80\n",
      "3/3 [==============================] - 0s 81ms/step - loss: 0.0062 - val_loss: 0.0094\n",
      "Epoch 53/80\n",
      "3/3 [==============================] - 0s 90ms/step - loss: 0.0061 - val_loss: 0.0207\n",
      "Epoch 54/80\n",
      "3/3 [==============================] - 0s 115ms/step - loss: 0.0061 - val_loss: 0.0069\n",
      "Epoch 55/80\n",
      "3/3 [==============================] - 0s 117ms/step - loss: 0.0054 - val_loss: 0.0058\n",
      "Epoch 56/80\n",
      "3/3 [==============================] - 0s 83ms/step - loss: 0.0051 - val_loss: 0.0076\n",
      "Epoch 57/80\n",
      "3/3 [==============================] - 0s 158ms/step - loss: 0.0069 - val_loss: 0.0062\n",
      "Epoch 58/80\n",
      "3/3 [==============================] - 0s 97ms/step - loss: 0.0038 - val_loss: 0.0053\n",
      "Epoch 59/80\n",
      "3/3 [==============================] - 0s 81ms/step - loss: 0.0068 - val_loss: 0.0057\n",
      "Epoch 60/80\n",
      "3/3 [==============================] - 0s 92ms/step - loss: 0.0067 - val_loss: 0.0091\n",
      "Epoch 61/80\n",
      "3/3 [==============================] - 0s 69ms/step - loss: 0.0043 - val_loss: 0.0049\n",
      "Epoch 62/80\n",
      "3/3 [==============================] - 0s 142ms/step - loss: 0.0059 - val_loss: 0.0051\n",
      "Epoch 63/80\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 132ms/step - loss: 0.0054 - val_loss: 0.0073\n",
      "Epoch 64/80\n",
      "3/3 [==============================] - 0s 88ms/step - loss: 0.0060 - val_loss: 0.0063\n",
      "Epoch 65/80\n",
      "3/3 [==============================] - 0s 78ms/step - loss: 0.0060 - val_loss: 0.0092\n",
      "Epoch 66/80\n",
      "3/3 [==============================] - 0s 65ms/step - loss: 0.0059 - val_loss: 0.0199\n",
      "Epoch 67/80\n",
      "3/3 [==============================] - 0s 65ms/step - loss: 0.0055 - val_loss: 0.0144\n",
      "Epoch 68/80\n",
      "3/3 [==============================] - 0s 64ms/step - loss: 0.0049 - val_loss: 0.0064\n",
      "Epoch 69/80\n",
      "3/3 [==============================] - 0s 83ms/step - loss: 0.0047 - val_loss: 0.0065\n",
      "Epoch 70/80\n",
      "3/3 [==============================] - 0s 65ms/step - loss: 0.0041 - val_loss: 0.0083\n",
      "Epoch 71/80\n",
      "3/3 [==============================] - 0s 67ms/step - loss: 0.0053 - val_loss: 0.0113\n",
      "Epoch 72/80\n",
      "3/3 [==============================] - 0s 95ms/step - loss: 0.0054 - val_loss: 0.0116\n",
      "Epoch 73/80\n",
      "3/3 [==============================] - 0s 71ms/step - loss: 0.0054 - val_loss: 0.0249\n",
      "Epoch 74/80\n",
      "3/3 [==============================] - 0s 71ms/step - loss: 0.0045 - val_loss: 0.0181\n",
      "Epoch 75/80\n",
      "3/3 [==============================] - 0s 91ms/step - loss: 0.0048 - val_loss: 0.0119\n",
      "Epoch 76/80\n",
      "3/3 [==============================] - 0s 64ms/step - loss: 0.0045 - val_loss: 0.0132\n",
      "Epoch 77/80\n",
      "3/3 [==============================] - 0s 66ms/step - loss: 0.0046 - val_loss: 0.0153\n",
      "Epoch 78/80\n",
      "3/3 [==============================] - 0s 65ms/step - loss: 0.0054 - val_loss: 0.0206\n",
      "Epoch 79/80\n",
      "3/3 [==============================] - 0s 91ms/step - loss: 0.0045 - val_loss: 0.0134\n",
      "Epoch 80/80\n",
      "3/3 [==============================] - 0s 80ms/step - loss: 0.0062 - val_loss: 0.0129\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 0.034\n",
      "0.8376960672143843\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/5d/8ypm7r1d4fq_hpvg_s1r82vw0000gn/T/ipykernel_3043/544464558.py:120: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_test['pre']=pred\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Discharge B0005 (prediction) starts in cycle 81, window-size=5')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys \n",
    "import numpy as np # linear algebra\n",
    "from scipy.stats import randint\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv), data manipulation as in SQL\n",
    "import matplotlib.pyplot as plt # this is used for the plot the graph \n",
    "import seaborn as sns # used for plot interactive graph. \n",
    "from sklearn.model_selection import train_test_split # to split the data into two parts\n",
    "#from sklearn.cross_validation import KFold # use for cross validation\n",
    "from sklearn.preprocessing import StandardScaler # for normalization\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.pipeline import Pipeline # pipeline making\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn import metrics # for the check the error and accuracy of the model\n",
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "\n",
    "## for Deep-learing:\n",
    "import keras\n",
    "from keras.layers import Dense\n",
    "from keras.models import Sequential\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.utils import np_utils\n",
    "import itertools\n",
    "from keras.layers import LSTM\n",
    "from keras.layers.convolutional import Conv1D\n",
    "from keras.layers.convolutional import MaxPooling1D\n",
    "from keras.layers import Dropout\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "df=pd.read_csv(\"/Users/mac/Discharge5_H1.csv\")\n",
    "featurs=['Batt_name','cycle','amb_temp','voltage_battery','current_battery','temp_battery','current_load','voltage_load','time','Capacity','H']\n",
    "f1=['Capacity','cycle','time','Max_Temp_Time']\n",
    "\n",
    "dataset=df[f1]\n",
    "data_train=dataset[(dataset['cycle']<82)]\n",
    "data_set_train=data_train.iloc[:,0:4].values\n",
    "data_test=dataset[(dataset['cycle']>=82)]\n",
    "data_set_test=data_test.iloc[:,0:4].values\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc=MinMaxScaler(feature_range=(0,1))\n",
    "data_set_train=sc.fit_transform(data_set_train)\n",
    "data_set_test=sc.transform(data_set_test)\n",
    "\n",
    "\n",
    "\n",
    "X_train=[]\n",
    "y_train=[]\n",
    "for i in range(5,81):\n",
    "    X_train.append(data_set_train[i-5:i,:])\n",
    "    y_train.append(data_set_train[i,0])\n",
    "X_train,y_train=np.array(X_train),np.array(y_train)\n",
    " \n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "\n",
    "\n",
    "\n",
    "X_train=np.reshape(X_train,(X_train.shape[0],X_train.shape[1],4))\n",
    "\n",
    "\n",
    "data_total=pd.concat((data_train.iloc[:,0:4], data_test.iloc[:,0:4]),axis=0)\n",
    "inputs=data_total[len(data_total)-len(data_test)-5:].values\n",
    "inputs=inputs.reshape(-1,4)\n",
    "inputs=sc.transform(inputs)\n",
    "\n",
    "\n",
    "X_test=[]\n",
    "y_test=[]\n",
    "for i in range(5,92):\n",
    "    X_test.append(inputs[i-5:i,:])\n",
    "    y_test.append(inputs[i,0])\n",
    "X_test=np.array(X_test)\n",
    "y_test=np.array(y_test)\n",
    "X_test=np.reshape(X_test,(X_test.shape[0],X_test.shape[1],4))\n",
    "\n",
    "\n",
    "regress = Sequential()\n",
    "regress.add(LSTM(units=100, return_sequences=True, input_shape=(X_train.shape[1],4)))\n",
    "regress.add(Dropout(0.3))\n",
    "regress.add(LSTM(units=100, return_sequences=True))\n",
    "regress.add(Dropout(0.3))\n",
    "regress.add(LSTM(units=100, return_sequences=True))\n",
    "regress.add(Dropout(0.3))\n",
    "regress.add(LSTM(units=100, return_sequences=True))\n",
    "regress.add(Dropout(0.3))\n",
    "regress.add(LSTM(units=150, return_sequences=True))\n",
    "regress.add(Dropout(0.3))\n",
    "regress.add(LSTM(units=200))\n",
    "regress.add(Dropout(0.3))\n",
    "regress.add(Dense(units=1))\n",
    "regress.compile(optimizer='adam',loss='mean_squared_error')\n",
    "regress.summary()\n",
    "\n",
    "\n",
    "history = regress.fit(X_train,y_train,epochs=80,batch_size=30,validation_data=(X_test, y_test))\n",
    "\n",
    "\n",
    "plt.plot(history.history['loss'], label='train')\n",
    "plt.plot(history.history['val_loss'], label='test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "pred=regress.predict(X_test)\n",
    "pn = np.zeros(shape=(len(pred), 4))\n",
    "pn[:,0] = pred[:,0]\n",
    "pred = sc.inverse_transform(pn )[:,0]\n",
    "#print(pred)\n",
    "tests=data_test.iloc[:,0:1]\n",
    "rmse = np.sqrt(mean_squared_error(tests, pred))\n",
    "print('Test RMSE: %.3f' % rmse)\n",
    "print(metrics.r2_score(tests,pred))\n",
    "\n",
    "ln = len(data_train)\n",
    "data_test['pre']=pred\n",
    "plot_df = dataset.loc[(dataset['cycle']>=1),['cycle','Capacity']]\n",
    "plot_per = data_test.loc[(data_test['cycle']>=ln),['cycle','pre']]\n",
    "plt.figure(figsize=(16, 10))\n",
    "plt.plot(plot_df['cycle'], plot_df['Capacity'], label=\"Actual data\", color='blue')\n",
    "plt.plot(plot_per['cycle'],plot_per['pre'],label=\"Prediction data\", color='red')\n",
    "#Draw threshold\n",
    "plt.plot([0.,168], [1.38, 1.38],dashes=[6, 2], label=\"threshold\")\n",
    "plt.ylabel('Capacity')\n",
    "# make x-axis ticks legible\n",
    "adf = plt.gca().get_xaxis().get_major_formatter()\n",
    "plt.xlabel('cycle')\n",
    "plt.legend()\n",
    "plt.title('Discharge B0005 (prediction) starts in cycle 81, window-size=5')\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fef634ba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2f215fc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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