LSTM-BASED RNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9d3f7b08",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "os.chdir('G:/Time_Series_Data')\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from keras.preprocessing.sequence import TimeseriesGenerator\n",
    "from keras.layers import Dense, Embedding,Input,LSTM,GlobalMaxPooling1D\n",
    "from keras import Model\n",
    "from plot_keras_history import show_history, plot_history\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "77ea35a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Land-Ocean Temperature Index (C)\n",
      "\n",
      "------\n",
      "--------------------------------\n",
      "\n",
      "------\n",
      "\n",
      "\n",
      "------\n",
      "Year No_Smoothing  Lowess(5)\n",
      "\n",
      "------\n",
      "----------------------------\n",
      "\n",
      "------\n",
      "1880     -0.16     -0.09\n",
      "\n",
      "------\n",
      "1881     -0.08     -0.12\n",
      "\n",
      "------\n",
      "1882     -0.10     -0.16\n",
      "\n",
      "------\n",
      "1883     -0.17     -0.20\n",
      "\n",
      "------\n",
      "1884     -0.28     -0.23\n",
      "\n",
      "------\n",
      "1885     -0.33     -0.26\n",
      "\n",
      "------\n",
      "1886     -0.31     -0.27\n",
      "\n",
      "------\n",
      "1887     -0.36     -0.27\n",
      "\n",
      "------\n",
      "1888     -0.17     -0.26\n",
      "\n",
      "------\n",
      "1889     -0.10     -0.25\n",
      "\n",
      "------\n",
      "1890     -0.35     -0.25\n",
      "\n",
      "------\n",
      "1891     -0.22     -0.26\n",
      "\n",
      "------\n",
      "1892     -0.27     -0.26\n",
      "\n",
      "------\n",
      "1893     -0.31     -0.26\n",
      "\n",
      "------\n",
      "1894     -0.31     -0.24\n",
      "\n",
      "------\n",
      "1895     -0.23     -0.22\n",
      "\n",
      "------\n",
      "1896     -0.11     -0.21\n",
      "\n",
      "------\n",
      "1897     -0.10     -0.18\n",
      "\n",
      "------\n",
      "1898     -0.27     -0.17\n",
      "\n",
      "------\n",
      "1899     -0.18     -0.17\n",
      "\n",
      "------\n",
      "1900     -0.08     -0.20\n",
      "\n",
      "------\n",
      "1901     -0.16     -0.23\n",
      "\n",
      "------\n",
      "1902     -0.27     -0.26\n",
      "\n",
      "------\n",
      "1903     -0.36     -0.28\n",
      "\n",
      "------\n",
      "1904     -0.46     -0.30\n",
      "\n",
      "------\n",
      "1905     -0.26     -0.33\n",
      "\n",
      "------\n",
      "1906     -0.22     -0.35\n",
      "\n",
      "------\n",
      "1907     -0.38     -0.37\n",
      "\n",
      "------\n",
      "1908     -0.42     -0.39\n",
      "\n",
      "------\n",
      "1909     -0.48     -0.40\n",
      "\n",
      "------\n",
      "1910     -0.43     -0.41\n",
      "\n",
      "------\n",
      "1911     -0.44     -0.39\n",
      "\n",
      "------\n",
      "1912     -0.36     -0.35\n",
      "\n",
      "------\n",
      "1913     -0.35     -0.32\n",
      "\n",
      "------\n",
      "1914     -0.15     -0.31\n",
      "\n",
      "------\n",
      "1915     -0.14     -0.30\n",
      "\n",
      "------\n",
      "1916     -0.35     -0.29\n",
      "\n",
      "------\n",
      "1917     -0.46     -0.29\n",
      "\n",
      "------\n",
      "1918     -0.30     -0.30\n",
      "\n",
      "------\n",
      "1919     -0.27     -0.29\n",
      "\n",
      "------\n",
      "1920     -0.27     -0.28\n",
      "\n",
      "------\n",
      "1921     -0.18     -0.26\n",
      "\n",
      "------\n",
      "1922     -0.29     -0.25\n",
      "\n",
      "------\n",
      "1923     -0.27     -0.24\n",
      "\n",
      "------\n",
      "1924     -0.27     -0.23\n",
      "\n",
      "------\n",
      "1925     -0.22     -0.22\n",
      "\n",
      "------\n",
      "1926     -0.10     -0.22\n",
      "\n",
      "------\n",
      "1927     -0.22     -0.21\n",
      "\n",
      "------\n",
      "1928     -0.20     -0.20\n",
      "\n",
      "------\n",
      "1929     -0.36     -0.19\n",
      "\n",
      "------\n",
      "1930     -0.15     -0.19\n",
      "\n",
      "------\n",
      "1931     -0.09     -0.18\n",
      "\n",
      "------\n",
      "1932     -0.16     -0.17\n",
      "\n",
      "------\n",
      "1933     -0.28     -0.16\n",
      "\n",
      "------\n",
      "1934     -0.12     -0.15\n",
      "\n",
      "------\n",
      "1935     -0.19     -0.13\n",
      "\n",
      "------\n",
      "1936     -0.14     -0.10\n",
      "\n",
      "------\n",
      "1937     -0.02     -0.06\n",
      "\n",
      "------\n",
      "1938      0.00     -0.01\n",
      "\n",
      "------\n",
      "1939     -0.01      0.04\n",
      "\n",
      "------\n",
      "1940      0.13      0.07\n",
      "\n",
      "------\n",
      "1941      0.19      0.09\n",
      "\n",
      "------\n",
      "1942      0.07      0.11\n",
      "\n",
      "------\n",
      "1943      0.09      0.10\n",
      "\n",
      "------\n",
      "1944      0.21      0.08\n",
      "\n",
      "------\n",
      "1945      0.09      0.04\n",
      "\n",
      "------\n",
      "1946     -0.07      0.01\n",
      "\n",
      "------\n",
      "1947     -0.03     -0.04\n",
      "\n",
      "------\n",
      "1948     -0.11     -0.07\n",
      "\n",
      "------\n",
      "1949     -0.11     -0.08\n",
      "\n",
      "------\n",
      "1950     -0.17     -0.08\n",
      "\n",
      "------\n",
      "1951     -0.07     -0.07\n",
      "\n",
      "------\n",
      "1952      0.01     -0.07\n",
      "\n",
      "------\n",
      "1953      0.08     -0.07\n",
      "\n",
      "------\n",
      "1954     -0.13     -0.07\n",
      "\n",
      "------\n",
      "1955     -0.14     -0.06\n",
      "\n",
      "------\n",
      "1956     -0.19     -0.05\n",
      "\n",
      "------\n",
      "1957      0.05     -0.04\n",
      "\n",
      "------\n",
      "1958      0.06     -0.01\n",
      "\n",
      "------\n",
      "1959      0.03      0.01\n",
      "\n",
      "------\n",
      "1960     -0.03      0.03\n",
      "\n",
      "------\n",
      "1961      0.06      0.01\n",
      "\n",
      "------\n",
      "1962      0.03     -0.01\n",
      "\n",
      "------\n",
      "1963      0.05     -0.03\n",
      "\n",
      "------\n",
      "1964     -0.20     -0.04\n",
      "\n",
      "------\n",
      "1965     -0.11     -0.05\n",
      "\n",
      "------\n",
      "1966     -0.06     -0.06\n",
      "\n",
      "------\n",
      "1967     -0.02     -0.05\n",
      "\n",
      "------\n",
      "1968     -0.08     -0.03\n",
      "\n",
      "------\n",
      "1969      0.05     -0.02\n",
      "\n",
      "------\n",
      "1970      0.02     -0.01\n",
      "\n",
      "------\n",
      "1971     -0.08      0.00\n",
      "\n",
      "------\n",
      "1972      0.01      0.00\n",
      "\n",
      "------\n",
      "1973      0.16     -0.00\n",
      "\n",
      "------\n",
      "1974     -0.07      0.01\n",
      "\n",
      "------\n",
      "1975     -0.01      0.02\n",
      "\n",
      "------\n",
      "1976     -0.10      0.04\n",
      "\n",
      "------\n",
      "1977      0.18      0.07\n",
      "\n",
      "------\n",
      "1978      0.07      0.12\n",
      "\n",
      "------\n",
      "1979      0.16      0.16\n",
      "\n",
      "------\n",
      "1980      0.26      0.20\n",
      "\n",
      "------\n",
      "1981      0.32      0.21\n",
      "\n",
      "------\n",
      "1982      0.14      0.22\n",
      "\n",
      "------\n",
      "1983      0.31      0.21\n",
      "\n",
      "------\n",
      "1984      0.16      0.21\n",
      "\n",
      "------\n",
      "1985      0.12      0.22\n",
      "\n",
      "------\n",
      "1986      0.18      0.24\n",
      "\n",
      "------\n",
      "1987      0.32      0.27\n",
      "\n",
      "------\n",
      "1988      0.39      0.31\n",
      "\n",
      "------\n",
      "1989      0.27      0.33\n",
      "\n",
      "------\n",
      "1990      0.45      0.33\n",
      "\n",
      "------\n",
      "1991      0.40      0.33\n",
      "\n",
      "------\n",
      "1992      0.22      0.33\n",
      "\n",
      "------\n",
      "1993      0.23      0.33\n",
      "\n",
      "------\n",
      "1994      0.31      0.34\n",
      "\n",
      "------\n",
      "1995      0.45      0.37\n",
      "\n",
      "------\n",
      "1996      0.33      0.40\n",
      "\n",
      "------\n",
      "1997      0.46      0.42\n",
      "\n",
      "------\n",
      "1998      0.61      0.44\n",
      "\n",
      "------\n",
      "1999      0.38      0.47\n",
      "\n",
      "------\n",
      "2000      0.39      0.50\n",
      "\n",
      "------\n",
      "2001      0.53      0.52\n",
      "\n",
      "------\n",
      "2002      0.62      0.54\n",
      "\n",
      "------\n",
      "2003      0.61      0.58\n",
      "\n",
      "------\n",
      "2004      0.53      0.60\n",
      "\n",
      "------\n",
      "2005      0.67      0.61\n",
      "\n",
      "------\n",
      "2006      0.63      0.62\n",
      "\n",
      "------\n",
      "2007      0.66      0.63\n",
      "\n",
      "------\n",
      "2008      0.54      0.64\n",
      "\n",
      "------\n",
      "2009      0.65      0.64\n",
      "\n",
      "------\n",
      "2010      0.72      0.64\n",
      "\n",
      "------\n",
      "2011      0.61      0.66\n",
      "\n",
      "------\n",
      "2012      0.64      0.69\n",
      "\n",
      "------\n",
      "2013      0.67      0.73\n",
      "\n",
      "------\n",
      "2014      0.74      0.78\n",
      "\n",
      "------\n",
      "2015      0.90      0.83\n",
      "\n",
      "------\n",
      "2016      1.01      0.87\n",
      "\n",
      "------\n",
      "2017      0.92      0.91\n",
      "\n",
      "------\n",
      "2018      0.85      0.92\n",
      "\n",
      "------\n",
      "2019      0.97      0.92\n",
      "\n",
      "------\n",
      "2020      1.01      0.93\n",
      "\n",
      "------\n",
      "2021      0.84      0.93\n",
      "\n",
      "------\n"
     ]
    }
   ],
   "source": [
    "temp_data_set = {}\n",
    "temp_dev=[]\n",
    "year=[]\n",
    "with open(os.path.join('graph.txt'),encoding=\"utf-8\") as f:\n",
    "  # is just a space-separated text file in the format:\n",
    "  # word vec[0] vec[1] vec[2] ...\n",
    "  for i,line in enumerate(f):\n",
    "        print(line)\n",
    "        print('------')\n",
    "        if len(line)==25:\n",
    "            values = line.split()\n",
    "            year.append(values[0])\n",
    "            temp_dev.append(float(values[2]))\n",
    "            \n",
    "            \n",
    "Data_set=pd.DataFrame({'Year':year,'Temperature Increase (C)':temp_dev})\n",
    "Data_set['Year']=pd.to_datetime(Data_set.Year,format='%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "11c09583",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data_set_emission\n",
    "Data_set2=pd.read_csv('annual-co2-emissions-per-country.csv')\n",
    "Data_set_emission_world=Data_set2[Data_set2['Entity']=='World'].loc[:,['Year','Annual CO2 emissions']]\n",
    "\n",
    "Data_set_emission_world\n",
    "Data_set_emission_world.Year=pd.to_datetime(Data_set_emission_world.Year,format='%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a56671e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data_all=pd.merge(Data_set,Data_set_emission_world,on='Year')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29bc908c",
   "metadata": {},
   "source": [
    "# Plotting temperature increase and co2 emmision by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e7e9b2a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.0, '0'),\n",
       " Text(0, 10000000000.0, '1'),\n",
       " Text(0, 20000000000.0, '2'),\n",
       " Text(0, 30000000000.0, '3'),\n",
       " Text(0, 40000000000.0, '4')]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(10,8))\n",
    "sns.scatterplot(x='Year',y='Temperature Increase (C)',data=Data_all)\n",
    "ax.set_xlabel('Year',fontsize=16,fontweight='bold',family='Times New Roman')\n",
    "ax.set_ylabel('Temperature Increase (C)',fontsize=16,fontweight='bold',family='Times New Roman')\n",
    "date_x=pd.DataFrame({'dates_axis':['1880','1900','1920','1940','1960','1980','2000','2020']})\n",
    "ax.set_xticks(pd.to_datetime(date_x['dates_axis']))\n",
    "ax.set_xticklabels(['1880','1900','1920','1940','1960','1980','2000','2020'],fontsize=18,fontweight='bold',family='Times New Roman')\n",
    "ax.set_yticks([-0.4,-0.2,0,0.2,0.4,0.6,0.8,1])\n",
    "ax.set_yticklabels([-0.4,-0.2,0,0.2,0.4,0.6,0.8,1],fontsize=18,fontweight='bold',family='Times New Roman')\n",
    "\n",
    "fig,ax=plt.subplots(figsize=(10,8))\n",
    "sns.scatterplot(x='Year',y='Annual CO2 emissions',data=Data_all)\n",
    "ax.set_xlabel('Year',fontsize=16,fontweight='bold',family='Times New Roman')\n",
    "ax.set_ylabel(r'CO2 Emission  ($10^{10}$ tone)',fontsize=16,fontweight='bold',family='Times New Roman')\n",
    "date_x=pd.DataFrame({'dates_axis':['1880','1900','1920','1940','1960','1980','2000','2020']})\n",
    "ax.set_xticks(pd.to_datetime(date_x['dates_axis']))\n",
    "ax.set_xticklabels(['1880','1900','1920','1940','1960','1980','2000','2020'],fontsize=18,fontweight='bold',family='Times New Roman')\n",
    "ax.set_yticks([0,1e10,2e10,3e10,4e10])\n",
    "ax.set_yticklabels([0,1,2,3,4],fontsize=18,fontweight='bold',family='Times New Roman')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "63e1b982",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating sequences of data to be used for RNN\n",
    "min_max=MinMaxScaler()\n",
    "min_max_em=MinMaxScaler()\n",
    "train=Data_all.iloc[:130,:]\n",
    "test=Data_all.iloc[130:,:]\n",
    "temp=train['Temperature Increase (C)'].values\n",
    "train_T_scaled=min_max.fit_transform(temp.reshape((-1,1)))\n",
    "emis_=train['Annual CO2 emissions'].values\n",
    "train__scaled=min_max_em.fit_transform(emis_.reshape((-1,1)))\n",
    "X=np.concatenate((train_T_scaled, train__scaled), axis=1, out=None, dtype=None, casting=\"same_kind\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "113099b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-14-17cf8773c2a3>:12: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  history=model.fit_generator(generator,epochs=40)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120/120 [==============================] - 2s 4ms/step - loss: 0.0171\n",
      "Epoch 2/40\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0037\n",
      "Epoch 3/40\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 0.0036\n",
      "Epoch 4/40\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0036\n",
      "Epoch 5/40\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 0.0031\n",
      "Epoch 6/40\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0029\n",
      "Epoch 7/40\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0027\n",
      "Epoch 8/40\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0026\n",
      "Epoch 9/40\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 0.0025\n",
      "Epoch 10/40\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 0.0025\n",
      "Epoch 11/40\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 0.0023\n",
      "Epoch 12/40\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0022\n",
      "Epoch 13/40\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 0.0022\n",
      "Epoch 14/40\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 0.0022\n",
      "Epoch 15/40\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 0.0019\n",
      "Epoch 16/40\n",
      "120/120 [==============================] - 1s 5ms/step - loss: 0.0018\n",
      "Epoch 17/40\n",
      "120/120 [==============================] - 1s 5ms/step - loss: 0.0018\n",
      "Epoch 18/40\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 0.0019\n",
      "Epoch 19/40\n",
      "120/120 [==============================] - 1s 5ms/step - loss: 0.0017\n",
      "Epoch 20/40\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0018\n",
      "Epoch 21/40\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0015\n",
      "Epoch 22/40\n",
      "120/120 [==============================] - 1s 5ms/step - loss: 0.0014\n",
      "Epoch 23/40\n",
      "120/120 [==============================] - 1s 5ms/step - loss: 0.0014\n",
      "Epoch 24/40\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0014\n",
      "Epoch 25/40\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0013\n",
      "Epoch 26/40\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 0.0014\n",
      "Epoch 27/40\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0012\n",
      "Epoch 28/40\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0012\n",
      "Epoch 29/40\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0012\n",
      "Epoch 30/40\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 0.0012\n",
      "Epoch 31/40\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 0.0012\n",
      "Epoch 32/40\n",
      "120/120 [==============================] - 1s 5ms/step - loss: 0.0011\n",
      "Epoch 33/40\n",
      "120/120 [==============================] - 1s 5ms/step - loss: 0.0012\n",
      "Epoch 34/40\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 0.0011\n",
      "Epoch 35/40\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 9.9505e-04\n",
      "Epoch 36/40\n",
      "120/120 [==============================] - 1s 6ms/step - loss: 9.8694e-04\n",
      "Epoch 37/40\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 9.8217e-04\n",
      "Epoch 38/40\n",
      "120/120 [==============================] - 1s 5ms/step - loss: 9.4188e-04\n",
      "Epoch 39/40\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 9.2515e-04\n",
      "Epoch 40/40\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 9.1598e-04\n"
     ]
    }
   ],
   "source": [
    "#code for return sequence case\n",
    "n_input=10\n",
    "n_features=2\n",
    "generator=TimeseriesGenerator(X,train_T_scaled,length=n_input,batch_size=1)\n",
    "\n",
    "input_=Input(shape=(n_input,2))\n",
    "x=LSTM(30,return_sequences=True,activation='relu')(input_)\n",
    "x=GlobalMaxPooling1D()(x)\n",
    "output=Dense(1)(x)\n",
    "model=Model(input_,output)\n",
    "model.compile(loss=\"mean_squared_error\")\n",
    "history=model.fit_generator(generator,epochs=40)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0c8953d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "show_history(history)\n",
    "plot_history(history, path=\"standard.png\")\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a9da2083",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 10, 2)]           0         \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 10, 30)            3960      \n",
      "                                                                 \n",
      " global_max_pooling1d_1 (Glo  (None, 30)               0         \n",
      " balMaxPooling1D)                                                \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 31        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3,991\n",
      "Trainable params: 3,991\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1311b0eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1bccedbfeb0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# training data predictions\n",
    "y_prediction=np.zeros((len(train['Temperature Increase (C)'].values),))\n",
    "k=0\n",
    "for i in range(len(train['Temperature Increase (C)'].values)-n_input):\n",
    "    y_prediction[k]=model.predict(generator[i][0])\n",
    "    k+=1\n",
    "y_pre_unscaled=min_max.inverse_transform(y_prediction.reshape(-1,1))\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(train['Year'].iloc[n_input:],y_pre_unscaled[:-n_input],label='Prediction')\n",
    "plt.scatter(train['Year'],temp[:].reshape((-1,1)),label='Training Data')\n",
    "plt.ylim([-0.5,0.7])\n",
    "yearly_data=pd.DataFrame({'Year':[1880,1900,1920,1940,1960,1980,2000]})\n",
    "xticks_data=pd.to_datetime(yearly_data['Year'].values,format='%Y')\n",
    "\n",
    "plt.yticks([-0.4,-0.2,0,0.2,0.4,0.6],fontweight='bold',fontsize=18,family='Times New Roman')\n",
    "plt.xticks(fontsize=20,rotation=0,family='Times New Roman',fontweight='bold')\n",
    "plt.xlabel('Year',fontsize=28,family='Times New Roman')\n",
    "plt.ylabel('Temperature Increase (C)',fontsize=28,family='Times New Roman')\n",
    "\n",
    "plt.legend(loc='upper left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a34c3c04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.0352604]]\n",
      "[[1.0586609]]\n",
      "[[1.0832999]]\n",
      "[[1.1100878]]\n",
      "[[1.1382983]]\n",
      "[[1.1671753]]\n",
      "[[1.1976185]]\n",
      "[[1.229587]]\n",
      "[[1.2629787]]\n",
      "[[1.2971044]]\n",
      "[[1.3331835]]\n",
      "Percentage of Error for the test dataset\n",
      "0.47560889154598907\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-18-b548abe93401>:18: 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",
      "  test['predictions']=min_max.inverse_transform(y_test_predict.reshape((-1,1)))\n"
     ]
    }
   ],
   "source": [
    "temp_test=test['Temperature Increase (C)'].values\n",
    "test_T_scaled=min_max.transform(temp_test.reshape((-1,1)))\n",
    "emis_test=test['Annual CO2 emissions'].values\n",
    "test_scaled=min_max_em.transform(emis_test.reshape((-1,1)))\n",
    "X_test=np.concatenate((test_T_scaled, test_scaled), axis=1, out=None, dtype=None, casting=\"same_kind\")\n",
    "n_features=2\n",
    "first_eval_batch=X[-n_input:]\n",
    "current_batch=first_eval_batch.reshape((1,n_input,n_features))\n",
    "y_test_predict=[]\n",
    "for i in range(X_test.shape[0]):\n",
    "    y_now=model.predict(current_batch)\n",
    "    y_test_predict.append(y_now[0,0])\n",
    "    new_row=np.array([[y_now[0,0],test_scaled[i,0]]])\n",
    "    first_eval_batch=np.concatenate((first_eval_batch[1:],new_row),axis=0)\n",
    "    current_batch=first_eval_batch.reshape((1,n_input,n_features))\n",
    "    print(y_now)\n",
    "y_test_predict=np.array(y_test_predict)\n",
    "test['predictions']=min_max.inverse_transform(y_test_predict.reshape((-1,1)))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print('Percentage of Error for the test dataset')\n",
    "print(mean_squared_error(test['Temperature Increase (C)'],test['predictions'].values)/np.mean(np.abs(temp))*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c1274bcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1bccef4e760>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(color_codes=True)\n",
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "sns.scatterplot(y='Temperature Increase (C)',x='Year',data=train.iloc[:,:],label='train')\n",
    "sns.scatterplot(y='predictions',x='Year',data=test.iloc[:,:],label='predicted for test data')\n",
    "sns.scatterplot(y='Temperature Increase (C)',x='Year',data=test.iloc[:,:],label=' test data')\n",
    "plt.yticks([-0.4,-0.2,0,0.2,0.4,0.6,0.8],fontweight='bold',fontsize=18,family='Times New Roman')\n",
    "plt.xticks(fontsize=20,rotation=0,family='Times New Roman',fontweight='bold')\n",
    "plt.xlabel('Year',fontsize=28,family='Times New Roman')\n",
    "plt.ylabel('Temperature Increase (C)',fontsize=28,family='Times New Roman')\n",
    "\n",
    "plt.legend(loc='upper left',fontsize=14)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38b6ace5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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