Beat the machine challenge (SMAPE loss function).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Beat the machine challenge (SMAPE loss function)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>2018-06-15T00:00:00.000000Z</td>\n",
       "      <td>-27.155995</td>\n",
       "      <td>0.332523</td>\n",
       "      <td>735.701951</td>\n",
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       "      <td>0.099577</td>\n",
       "      <td>-62.472969</td>\n",
       "      <td>122.606756</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.195166</td>\n",
       "      <td>0.966355</td>\n",
       "      <td>102.051843</td>\n",
       "      <td>30.555759</td>\n",
       "      <td>24.130086</td>\n",
       "      <td>680.589152</td>\n",
       "      <td>-927.424059</td>\n",
       "      <td>-87.123208</td>\n",
       "      <td>62.666205</td>\n",
       "      <td>1156.681711</td>\n",
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       "      <th>1</th>\n",
       "      <td>2018-06-14T00:00:00.000000Z</td>\n",
       "      <td>-17.012365</td>\n",
       "      <td>0.269606</td>\n",
       "      <td>723.576863</td>\n",
       "      <td>107.761407</td>\n",
       "      <td>11.138383</td>\n",
       "      <td>-0.254799</td>\n",
       "      <td>-60.965083</td>\n",
       "      <td>122.834661</td>\n",
       "      <td>0.008695</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.080149</td>\n",
       "      <td>0.965490</td>\n",
       "      <td>102.031867</td>\n",
       "      <td>-12.797338</td>\n",
       "      <td>13.641165</td>\n",
       "      <td>699.645742</td>\n",
       "      <td>-924.946099</td>\n",
       "      <td>-84.658409</td>\n",
       "      <td>63.023348</td>\n",
       "      <td>972.159870</td>\n",
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       "      <th>2</th>\n",
       "      <td>2018-06-13T00:00:00.000000Z</td>\n",
       "      <td>-3.306774</td>\n",
       "      <td>-1.054852</td>\n",
       "      <td>738.375336</td>\n",
       "      <td>107.556928</td>\n",
       "      <td>7.168404</td>\n",
       "      <td>-0.158063</td>\n",
       "      <td>-42.867335</td>\n",
       "      <td>122.621138</td>\n",
       "      <td>-0.003350</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036628</td>\n",
       "      <td>0.965188</td>\n",
       "      <td>101.939175</td>\n",
       "      <td>20.709537</td>\n",
       "      <td>27.210881</td>\n",
       "      <td>705.710819</td>\n",
       "      <td>-939.902095</td>\n",
       "      <td>-85.299329</td>\n",
       "      <td>60.244882</td>\n",
       "      <td>1000.999754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-06-12T00:00:00.000000Z</td>\n",
       "      <td>-20.244923</td>\n",
       "      <td>0.199990</td>\n",
       "      <td>729.565275</td>\n",
       "      <td>107.706857</td>\n",
       "      <td>-8.592282</td>\n",
       "      <td>0.089837</td>\n",
       "      <td>-25.569892</td>\n",
       "      <td>122.127499</td>\n",
       "      <td>-0.000076</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001390</td>\n",
       "      <td>0.963559</td>\n",
       "      <td>101.843815</td>\n",
       "      <td>-9.351715</td>\n",
       "      <td>-1.445244</td>\n",
       "      <td>724.573891</td>\n",
       "      <td>-931.245191</td>\n",
       "      <td>-87.296879</td>\n",
       "      <td>57.449428</td>\n",
       "      <td>970.889792</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-06-11T00:00:00.000000Z</td>\n",
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       "      <td>-29.382555</td>\n",
       "      <td>122.137525</td>\n",
       "      <td>-0.002483</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.270608</td>\n",
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       "      <td>-922.523210</td>\n",
       "      <td>-86.495199</td>\n",
       "      <td>55.497440</td>\n",
       "      <td>1021.174747</td>\n",
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       "1 -0.080149  0.965490  102.031867 -12.797338  13.641165  699.645742   \n",
       "2  0.036628  0.965188  101.939175  20.709537  27.210881  705.710819   \n",
       "3  0.001390  0.963559  101.843815  -9.351715  -1.445244  724.573891   \n",
       "4 -0.818158  0.963392  101.896694   1.270608 -11.391039  733.148718   \n",
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      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv('Beat the machine challenge dataset.csv')\n",
    "df = df.fillna(0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Symmetric Mean Absolute Percent Error (SMAPE) \n",
    "def SMAPE(A, F):\n",
    "    return 100/len(A) * np.sum(2 * np.abs(F - A) / (np.abs(A) + np.abs(F)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from itertools import product\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X = df.drop(['y','Unnamed: 0'], axis = 1).values\n",
    "Y = df['y'].fillna(0)\n",
    "\n",
    "X = StandardScaler().fit_transform(X)\n",
    "\n",
    "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size = 0.05, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y[475]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras.backend as K\n",
    "# SMAPE(X_Train,Y_Train)\n",
    "from numba import jit\n",
    "import math\n",
    "\n",
    "@jit\n",
    "def smape_cpcm(y_true, y_pred):\n",
    "    out = 0\n",
    "    for i in range(y_true.shape[0]):\n",
    "        a = y_true[i]\n",
    "        b = y_pred[i]\n",
    "        c = math.fabs(a)+math.fabs(b)\n",
    "        if c == 0:\n",
    "            continue\n",
    "        out += math.fabs(a - b) / c\n",
    "    out *= (200.0 / y_true.shape[0])\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "# smape_fast(X_Train,Y_Train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras.backend as K\n",
    "def smape_kun(y_true, y_pred):\n",
    "    return np.mean((np.abs(y_pred - y_true) * 200/ (np.abs(y_pred) + np.abs(y_true))).fillna(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "199.2119149506594"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smape_kun(X_Train[:,1],Y_Train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2047,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.644971944366066\n"
     ]
    }
   ],
   "source": [
    "# https://www.kaggle.com/nickycan/much-faster-smape-metric-function\n",
    "# https://stackoverflow.com/questions/51444630/how-to-use-smape-evaluation-metric-on-train-dataset\n",
    "# https://stackoverflow.com/questions/41925157/logisticregression-unknown-label-type-continuous-using-sklearn-in-python\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model = RandomForestRegressor(n_jobs=-1000)\n",
    "\n",
    "model.fit(X_Train,Y_Train)\n",
    "\n",
    "y_pred = model.predict(X_Test)\n",
    "\n",
    "print(smape_kun(Y_Test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26.063916740529667\n"
     ]
    }
   ],
   "source": [
    "# https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html\n",
    "from sklearn import linear_model\n",
    "clf = linear_model.Lasso(alpha=0.1)\n",
    "\n",
    "clf.fit(X_Train,Y_Train)\n",
    "\n",
    "y_pred = clf.predict(X_Test)\n",
    "\n",
    "print(smape_kun(Y_Test,y_pred))\n",
    "# print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25.004396613559596\n",
      "[878.94415482 271.43547028 597.30961577 967.43822198 439.99987519\n",
      " 368.86978256 888.68338747 247.13701981 758.57591368 222.06131969\n",
      " -31.0845677   54.86896653 677.90026147 361.20533806 754.39439162\n",
      " 822.70540431 259.26541475 145.6921482  266.32647931 128.17883079\n",
      " 950.93466172 221.66391014 191.10224366 161.51247384 148.57011966]\n"
     ]
    }
   ],
   "source": [
    "# https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html\n",
    "from sklearn.linear_model import LinearRegression\n",
    "reg = LinearRegression().fit(X_Train,Y_Train)\n",
    "\n",
    "y_pred = reg.predict(X_Test)\n",
    "\n",
    "print(smape_kun(Y_Test,y_pred))\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64.2885507824887\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "svr_regressor = SVR(kernel='rbf', gamma='auto')\n",
    "svr_regressor.fit(X_Train,Y_Train)\n",
    "\n",
    "y_pred = svr_regressor.predict(X_Test)\n",
    "\n",
    "print(smape_kun(Y_Test,y_pred))\n",
    "#print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2057,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.31179557795265\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "tree_regressor = DecisionTreeRegressor(random_state = 0)\n",
    "tree_regressor.fit(X_Train,Y_Train)\n",
    "\n",
    "y_pred = tree_regressor.predict(X_Test)\n",
    "\n",
    "print(smape_kun(Y_Test,y_pred))\n",
    "#print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2053,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "[22 22 26  0 -2 29  0 24 -2 -2 -2 19 23  8  6 27 -2 14 -2 -2 29 -2 -2 11\n",
      " -2 -2 12 -2 -2 27 -2 -2 26 24 27  4 24 -2 -2  5 -2 -2 18 -2 29  3 -2 -2\n",
      " -2 22  4 -2  0 -2 -2  7 26  6 -2 -2 -2 12 21 -2  4 -2 -2 12 -2 -2 22  7\n",
      " -2 22 -2 -2  1 -2 -2 27 13  9  5 27  8 -2 -2 -2 14 -2 13  0 -2 -2  2 -2\n",
      " -2 26 -2 -2  6 19 12  3 29 10 -2 -2 -2 15 27 -2 -2 12 -2 -2  0 26 -2  2\n",
      " -2 -2 29 21 -2 -2  0 -2 -2  1 29 29 -2 14 -2 -2 12 -2 -2 15 -2 -2  9  6\n",
      "  0 -2 -2 -2 10  1 -2 -2 12  4 16 -2 -2 27 -2 -2 24  6 -2 -2 -2  6 11 26\n",
      "  5 29  5 -2 15 -2 -2 10  8 18 -2  3 -2 -2 -2 -2  7 17  8  9 -2 17 -2 -2\n",
      " 13 -2 -2 21  8 21 -2 -2  4 -2 -2 -2  8 -2 11 27  6  5 -2 -2 -2  4 28 15\n",
      "  4 -2 -2 -2 -2 12 16 29 -2 -2 13 -2 -2 -2 21 -2 16 -2 -2 29  8  9 24 17\n",
      "  4 -2 -2  7 -2  6 -2 -2  1  1 -2 -2 -2 26 -2 -2 12 -2 12 29 -2 -2  3 -2\n",
      " -2  4 14 18  6 -2 -2 -2  6 15 -2 -2 -2  7 25 10 -2 -2 -2 -2 24  9 22 -2\n",
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      " 25 29 19 -2 -2 29  0  5 15 -2 -2 13 21 -2 -2  1 -2 -2  9 26 -2 -2 12  4\n",
      " -2 -2 -2 13 18  0 -2 -2 -2 24  2 13 -2 -2 -2 -2 19  5 -2 -2 -2 10 -2  7\n",
      " 28 -2 26 -2 -2 11 -2 -2 16  5 27 17 -2  3 19 -2 -2 -2 -2 28 -2 -2 27 19\n",
      " -2 -2  2 -2 -2 29 24 26 25 10  8 -2 -2 -2 10 12 -2 15 -2 -2  2 -2 -2 22\n",
      " 21 -2 -2 22 -2 -2 10  8 -2 -2 13 -2  7 26 -2 -2 17 -2 -2 17 21 27  8 11\n",
      " 12 19 27 -2 -2 -2 16 10 -2 23 11 -2 -2 -2  2 -2  5 27 -2 -2 -2 28  0 21\n",
      " -2 29 -2 10 -2 -2 28  2  5 -2 -2 -2 29 17 -2 -2 13 -2 -2  6 15 11  0 -2\n",
      " 17  3 -2 -2 29  4 -2 -2 -2 -2  7 -2 -2 17 29  5 -2 -2 -2  3 -2 17 13 11\n",
      " -2 13 10 -2 -2 -2 26 22 -2 -2 24  0 17 22 -2 -2 20 -2 25 29 -2 -2 -2 -2\n",
      " 29 21 -2 -2 29 -2 -2 29  8 -2 12 -2 -2 20 12  8 -2 -2 -2  2 -2  4 -2  5\n",
      " -2 -2  6 15 -2 -2 -2 15  0  3 21 18 11 -2 -2 26 -2 20 -2 -2 28  0 -2 -2\n",
      " -2 10  4  0 -2 -2  7 -2 -2 17 20 -2 -2 -2  7 -2 -2  2 -2 15  8 14 -2 -2\n",
      " -2 -2 10 26 -2  4 -2 -2 -2 -2 18 18 15  2 22 11 -2 22 -2 -2 22 -2 -2 -2\n",
      "  7 29 17  0 21 10 26 -2 -2 -2 15 -2 -2 -2  4 18 -2 -2 -2 25 16 13  1 -2\n",
      " -2 -2 26 13 -2 -2 -2 20 -2 -2 26 -2 14 -2 -2 26 15  7 12 -2 -2 -2 29 -2\n",
      "  5  9 28 -2 -2  5 -2 18 -2 12 12 10 -2 -2 -2 -2 26 29 -2 14  2 17 15 18\n",
      " -2 -2 -2 -2  9 23 -2 -2 25 -2 -2 -2 -2  7 13 -2  2 -2 -2 16 14 21 -2 -2\n",
      " 18 -2 -2 -2 18 18  9  5 16 25 -2  9 14 11 25 26 19 -2 -2  4 29 19 -2 -2\n",
      " -2 -2 24 -2 -2  5 14  1  0 16 -2 -2 -2 27 -2 -2 19 -2 24 15 -2  9 -2 -2\n",
      " -2  0 -2 13 -2 -2 28 21 -2 13 -2 -2 14 -2 -2  2 -2 -2 25 -2 -2 13 25 -2\n",
      " 18 -2 -2  4 -2 -2 -2 27 28 27 -2  3 -2 -2  2 10  0 -2 -2 19 10 -2 -2 26\n",
      " -2 -2 14 23 -2 12 -2 -2 -2 17 10 -2 -2 -2 18 11 15 -2 -2 12  1  9 21 -2\n",
      " -2 13 -2 -2 16 -2 -2  4 -2 -2 12 18  4  5 -2 -2 -2 -2 11  1  1 11  8 -2\n",
      " -2 18 -2 -2 13 -2 -2 22 -2 17 18 -2 -2 -2 -2]\n"
     ]
    }
   ],
   "source": [
    "n_nodes = tree_regressor.tree_.node_count\n",
    "children_left = tree_regressor.tree_.children_left\n",
    "children_right = tree_regressor.tree_.children_right\n",
    "feature = tree_regressor.tree_.feature\n",
    "threshold = tree_regressor.tree_.threshold\n",
    "print(feature)\n",
    "\n",
    "#clf = clf.fit(iris.data, iris.target)\n",
    "tree.export_graphviz(tree_regressor,out_file='tree.dot') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import PolynomialFeatures\n",
    "# poly_reg = PolynomialFeatures(degree = 1)\n",
    "# X_poly = poly_reg.fit_transform(X_Train)\n",
    "# poly_reg_model = LinearRegression()\n",
    "# poly_reg_model.fit(X_poly, Y_Train)\n",
    "\n",
    "# y_pred = poly_reg_model.predict(X_Test)\n",
    "\n",
    "# print(smape_kun(Y_Test,y_pred))\n",
    "# #print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classifiers\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import svm\n",
    "from sklearn import tree\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "multiclass\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn import utils\n",
    "\n",
    "lab_enc = preprocessing.LabelEncoder()\n",
    "Y_Train = lab_enc.fit_transform(Y_Train)\n",
    "print(utils.multiclass.type_of_target(Y_Train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66.34879909224844\n"
     ]
    }
   ],
   "source": [
    "trainedmodel = LogisticRegression().fit(X_Train,Y_Train)\n",
    "predictions =trainedmodel.predict(X_Test)\n",
    "print(smape_kun(Y_Test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61.17716928709699\n"
     ]
    }
   ],
   "source": [
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(X_Train,Y_Train)\n",
    "predictionforest = trainedforest.predict(X_Test)\n",
    "print(smape_kun(Y_Test,predictionforest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57.102591933409116\n"
     ]
    }
   ],
   "source": [
    "trainedsvm = svm.LinearSVC().fit(X_Train, Y_Train)\n",
    "predictionsvm = trainedsvm.predict(X_Test)\n",
    "print(smape_kun(Y_Test,predictionsvm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57.102591933409116\n"
     ]
    }
   ],
   "source": [
    "trainedtree = tree.DecisionTreeClassifier().fit(X_Train, Y_Train)\n",
    "predictionstree = trainedtree.predict(X_Test)\n",
    "print(smape_kun(Y_Test,predictionsvm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55.918068761306586\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:442: UserWarning: The priors do not sum to 1. Renormalizing\n",
      "  UserWarning)\n",
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    }
   ],
   "source": [
    "trainedlda = LinearDiscriminantAnalysis().fit(X_Train, Y_Train)\n",
    "predictionlda = trainedlda.predict(X_Test)\n",
    "print(smape_kun(Y_Test,predictionlda))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "174.86033285782491\n"
     ]
    }
   ],
   "source": [
    "trainednb = GaussianNB().fit(X_Train, Y_Train)\n",
    "predictionnb = trainednb.predict(X_Test)\n",
    "print(smape_kun(Y_Test,predictionnb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62.43621080149953\n"
     ]
    }
   ],
   "source": [
    "# https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression(random_state=0, solver='lbfgs',multi_class='multinomial').fit(X_Train,Y_Train)\n",
    "\n",
    "y_pred = clf.predict(X_Test)\n",
    "\n",
    "print(smape_kun(Y_Test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Deep Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras.backend as K\n",
    "def smape_kun2(y_true, y_pred):\n",
    "    return K.mean((np.abs(y_pred - y_true) * 200/ (np.abs(y_pred) + np.abs(y_true))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:29: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 427 samples, validate on 48 samples\n",
      "Epoch 1/10\n",
      " - 7s - loss: 198.3986 - val_loss: 198.3949\n",
      "Epoch 2/10\n",
      " - 0s - loss: 198.3986 - val_loss: 198.3949\n",
      "Epoch 3/10\n",
      " - 0s - loss: 198.3986 - val_loss: 198.3949\n",
      "Epoch 4/10\n",
      " - 0s - loss: 198.3986 - val_loss: 198.3949\n",
      "Epoch 5/10\n",
      " - 0s - loss: 198.3986 - val_loss: 198.3949\n",
      "Epoch 6/10\n",
      " - 0s - loss: 198.3986 - val_loss: 198.3949\n",
      "Epoch 7/10\n",
      " - 0s - loss: 198.3986 - val_loss: 198.3949\n",
      "Epoch 8/10\n",
      " - 0s - loss: 198.3986 - val_loss: 198.3949\n",
      "Epoch 9/10\n",
      " - 0s - loss: 198.3986 - val_loss: 198.3949\n",
      "Epoch 10/10\n",
      " - 0s - loss: 198.3986 - val_loss: 198.3949\n",
      "Generating test predictions...\n",
      "198.1458501453552\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.utils import np_utils\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "X_train = X_Train\n",
    "y_train = Y_Train\n",
    "\n",
    "input_dim = X_train.shape[1]\n",
    "#nb_classes = Y_Train.shape[1]\n",
    "\n",
    "# Here's a Deep Dumb MLP (DDMLP)\n",
    "model = Sequential()\n",
    "model.add(Dense(128, input_dim=input_dim))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.15))\n",
    "model.add(Dense(128))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.15))\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "# we'll use categorical xent for the loss, and RMSprop as the optimizer\n",
    "model.compile(loss=smape_kun2, optimizer='rmsprop')\n",
    "\n",
    "print(\"Training...\")\n",
    "model.fit(X_train, y_train, nb_epoch=10, validation_split=0.1,verbose=2)\n",
    "\n",
    "print(\"Generating test predictions...\")\n",
    "preds = model.predict_classes(X_Test, verbose=0)\n",
    "\n",
    "preds = model.predict(X_Test)\n",
    "def twoone(list1):\n",
    "    return [val for lst in list1 for val in lst]\n",
    "\n",
    "preds = twoone(preds)\n",
    "\n",
    "print(smape_kun(Y_Test,preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=30, units=256)`\n",
      "  import sys\n",
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=128)`\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=64)`\n",
      "  \n",
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 427 samples, validate on 48 samples\n",
      "Epoch 1/1000\n",
      " - 7s - loss: 199.7936 - mean_squared_error: 200855.6350 - mean_absolute_error: 359.9769 - val_loss: 199.8989 - val_mean_squared_error: 239648.8906 - val_mean_absolute_error: 406.2511\n",
      "Epoch 2/1000\n",
      " - 0s - loss: 199.0881 - mean_squared_error: 200929.9507 - mean_absolute_error: 360.1365 - val_loss: 199.8147 - val_mean_squared_error: 239731.1406 - val_mean_absolute_error: 406.4583\n",
      "Epoch 3/1000\n",
      " - 0s - loss: 198.5350 - mean_squared_error: 201024.6970 - mean_absolute_error: 360.3620 - val_loss: 199.7036 - val_mean_squared_error: 239838.6406 - val_mean_absolute_error: 406.7338\n",
      "Epoch 4/1000\n",
      " - 0s - loss: 197.9387 - mean_squared_error: 201140.2840 - mean_absolute_error: 360.6442 - val_loss: 199.5602 - val_mean_squared_error: 239931.8281 - val_mean_absolute_error: 407.0365\n",
      "Epoch 5/1000\n",
      " - 0s - loss: 197.3886 - mean_squared_error: 201230.7729 - mean_absolute_error: 360.9322 - val_loss: 199.3915 - val_mean_squared_error: 239984.4219 - val_mean_absolute_error: 407.3373\n",
      "Epoch 6/1000\n",
      " - 0s - loss: 196.9860 - mean_squared_error: 201295.8859 - mean_absolute_error: 361.2365 - val_loss: 199.1882 - val_mean_squared_error: 239991.3281 - val_mean_absolute_error: 407.6406\n",
      "Epoch 7/1000\n",
      " - 0s - loss: 196.3649 - mean_squared_error: 201324.6765 - mean_absolute_error: 361.5354 - val_loss: 198.9260 - val_mean_squared_error: 239942.3125 - val_mean_absolute_error: 407.9310\n",
      "Epoch 8/1000\n",
      " - 0s - loss: 195.8065 - mean_squared_error: 201294.7554 - mean_absolute_error: 361.8042 - val_loss: 198.6177 - val_mean_squared_error: 239841.4844 - val_mean_absolute_error: 408.1968\n",
      "Epoch 9/1000\n",
      " - 0s - loss: 195.6130 - mean_squared_error: 201239.4566 - mean_absolute_error: 362.1095 - val_loss: 198.2756 - val_mean_squared_error: 239696.6719 - val_mean_absolute_error: 408.4410\n",
      "Epoch 10/1000\n",
      " - 0s - loss: 195.2014 - mean_squared_error: 201131.7550 - mean_absolute_error: 362.3306 - val_loss: 197.9238 - val_mean_squared_error: 239468.8750 - val_mean_absolute_error: 408.5780\n",
      "Epoch 11/1000\n",
      " - 0s - loss: 194.9746 - mean_squared_error: 200944.2067 - mean_absolute_error: 362.4806 - val_loss: 197.5352 - val_mean_squared_error: 239150.2031 - val_mean_absolute_error: 408.6252\n",
      "Epoch 12/1000\n",
      " - 0s - loss: 194.6209 - mean_squared_error: 200664.2834 - mean_absolute_error: 362.5263 - val_loss: 197.1137 - val_mean_squared_error: 238699.3906 - val_mean_absolute_error: 408.5088\n",
      "Epoch 13/1000\n",
      " - 0s - loss: 194.2438 - mean_squared_error: 200271.7279 - mean_absolute_error: 362.4146 - val_loss: 196.5860 - val_mean_squared_error: 238123.5469 - val_mean_absolute_error: 408.2695\n",
      "Epoch 14/1000\n",
      " - 0s - loss: 193.7788 - mean_squared_error: 199748.4606 - mean_absolute_error: 362.1736 - val_loss: 195.9039 - val_mean_squared_error: 237337.9219 - val_mean_absolute_error: 407.7581\n",
      "Epoch 15/1000\n",
      " - 0s - loss: 193.0639 - mean_squared_error: 199014.4934 - mean_absolute_error: 361.5807 - val_loss: 195.0753 - val_mean_squared_error: 236288.7500 - val_mean_absolute_error: 406.8945\n",
      "Epoch 16/1000\n",
      " - 0s - loss: 192.1902 - mean_squared_error: 198026.4283 - mean_absolute_error: 360.6759 - val_loss: 194.1592 - val_mean_squared_error: 234973.5000 - val_mean_absolute_error: 405.6862\n",
      "Epoch 17/1000\n",
      " - 0s - loss: 191.0707 - mean_squared_error: 196822.2979 - mean_absolute_error: 359.4416 - val_loss: 193.1150 - val_mean_squared_error: 233357.1250 - val_mean_absolute_error: 404.1064\n",
      "Epoch 18/1000\n",
      " - 0s - loss: 189.5629 - mean_squared_error: 195367.5951 - mean_absolute_error: 357.7880 - val_loss: 191.9906 - val_mean_squared_error: 231381.2969 - val_mean_absolute_error: 402.0500\n",
      "Epoch 19/1000\n",
      " - 0s - loss: 187.7289 - mean_squared_error: 193531.0574 - mean_absolute_error: 355.6153 - val_loss: 190.7347 - val_mean_squared_error: 229005.0000 - val_mean_absolute_error: 399.4474\n",
      "Epoch 20/1000\n",
      " - 0s - loss: 185.5313 - mean_squared_error: 191250.7518 - mean_absolute_error: 352.8703 - val_loss: 189.1724 - val_mean_squared_error: 226230.2500 - val_mean_absolute_error: 396.3619\n",
      "Epoch 21/1000\n",
      " - 0s - loss: 183.0788 - mean_squared_error: 188643.3418 - mean_absolute_error: 349.7030 - val_loss: 186.8817 - val_mean_squared_error: 223053.9531 - val_mean_absolute_error: 392.8406\n",
      "Epoch 22/1000\n",
      " - 0s - loss: 180.5497 - mean_squared_error: 185809.5442 - mean_absolute_error: 346.2990 - val_loss: 184.2065 - val_mean_squared_error: 219615.9219 - val_mean_absolute_error: 389.3223\n",
      "Epoch 23/1000\n",
      " - 0s - loss: 177.4982 - mean_squared_error: 182648.8593 - mean_absolute_error: 342.7226 - val_loss: 181.1381 - val_mean_squared_error: 215824.7969 - val_mean_absolute_error: 385.6814\n",
      "Epoch 24/1000\n",
      " - 0s - loss: 173.8071 - mean_squared_error: 179143.5420 - mean_absolute_error: 339.0955 - val_loss: 177.8712 - val_mean_squared_error: 211721.9844 - val_mean_absolute_error: 382.0980\n",
      "Epoch 25/1000\n",
      " - 0s - loss: 169.5263 - mean_squared_error: 175485.5055 - mean_absolute_error: 335.3892 - val_loss: 174.2162 - val_mean_squared_error: 207065.0625 - val_mean_absolute_error: 378.0534\n",
      "Epoch 26/1000\n",
      " - 0s - loss: 164.8934 - mean_squared_error: 171187.6234 - mean_absolute_error: 331.0415 - val_loss: 169.8956 - val_mean_squared_error: 201536.3906 - val_mean_absolute_error: 372.7046\n",
      "Epoch 27/1000\n",
      " - 0s - loss: 159.7134 - mean_squared_error: 166008.7172 - mean_absolute_error: 325.3204 - val_loss: 164.7443 - val_mean_squared_error: 195105.0000 - val_mean_absolute_error: 366.0282\n",
      "Epoch 28/1000\n",
      " - 0s - loss: 153.7066 - mean_squared_error: 160268.9663 - mean_absolute_error: 318.3114 - val_loss: 158.7761 - val_mean_squared_error: 187769.1406 - val_mean_absolute_error: 358.0862\n",
      "Epoch 29/1000\n",
      " - 0s - loss: 147.4468 - mean_squared_error: 153418.3399 - mean_absolute_error: 310.0409 - val_loss: 152.2810 - val_mean_squared_error: 179686.4219 - val_mean_absolute_error: 349.3166\n",
      "Epoch 30/1000\n",
      " - 0s - loss: 140.9638 - mean_squared_error: 146004.0095 - mean_absolute_error: 300.9801 - val_loss: 145.5425 - val_mean_squared_error: 170931.6562 - val_mean_absolute_error: 339.8389\n",
      "Epoch 31/1000\n",
      " - 0s - loss: 133.7811 - mean_squared_error: 138435.0036 - mean_absolute_error: 291.6638 - val_loss: 138.7622 - val_mean_squared_error: 161620.8906 - val_mean_absolute_error: 329.8132\n",
      "Epoch 32/1000\n",
      " - 0s - loss: 126.2055 - mean_squared_error: 130027.3879 - mean_absolute_error: 281.3327 - val_loss: 131.4042 - val_mean_squared_error: 151392.9219 - val_mean_absolute_error: 318.3444\n",
      "Epoch 33/1000\n",
      " - 0s - loss: 118.6127 - mean_squared_error: 120887.3352 - mean_absolute_error: 269.6285 - val_loss: 123.0832 - val_mean_squared_error: 139934.8125 - val_mean_absolute_error: 304.3276\n",
      "Epoch 34/1000\n",
      " - 0s - loss: 110.1613 - mean_squared_error: 111035.5733 - mean_absolute_error: 255.6055 - val_loss: 114.6073 - val_mean_squared_error: 127581.3984 - val_mean_absolute_error: 289.0918\n",
      "Epoch 35/1000\n",
      " - 0s - loss: 103.4974 - mean_squared_error: 100400.0825 - mean_absolute_error: 241.1628 - val_loss: 105.4424 - val_mean_squared_error: 114731.3750 - val_mean_absolute_error: 272.7495\n",
      "Epoch 36/1000\n",
      " - 0s - loss: 95.8249 - mean_squared_error: 89681.6858 - mean_absolute_error: 226.4817 - val_loss: 91.7807 - val_mean_squared_error: 101470.2109 - val_mean_absolute_error: 255.3828\n",
      "Epoch 37/1000\n",
      " - 0s - loss: 84.7997 - mean_squared_error: 78198.2572 - mean_absolute_error: 210.5892 - val_loss: 83.9288 - val_mean_squared_error: 88239.9922 - val_mean_absolute_error: 237.6844\n",
      "Epoch 38/1000\n",
      " - 0s - loss: 75.5700 - mean_squared_error: 67246.2437 - mean_absolute_error: 195.1176 - val_loss: 76.0402 - val_mean_squared_error: 75261.2656 - val_mean_absolute_error: 220.2806\n",
      "Epoch 39/1000\n",
      " - 0s - loss: 68.1686 - mean_squared_error: 57374.3656 - mean_absolute_error: 181.0236 - val_loss: 68.2255 - val_mean_squared_error: 62926.1367 - val_mean_absolute_error: 201.9798\n",
      "Epoch 40/1000\n",
      " - 0s - loss: 61.9810 - mean_squared_error: 47582.7104 - mean_absolute_error: 166.9858 - val_loss: 61.2653 - val_mean_squared_error: 51595.3008 - val_mean_absolute_error: 183.9570\n",
      "Epoch 41/1000\n",
      " - 0s - loss: 57.3199 - mean_squared_error: 39868.5303 - mean_absolute_error: 155.6591 - val_loss: 55.1489 - val_mean_squared_error: 41679.0078 - val_mean_absolute_error: 166.4073\n",
      "Epoch 42/1000\n",
      " - 0s - loss: 53.2822 - mean_squared_error: 32798.6648 - mean_absolute_error: 144.2846 - val_loss: 50.1028 - val_mean_squared_error: 33618.6445 - val_mean_absolute_error: 150.0597\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 43/1000\n",
      " - 0s - loss: 50.3356 - mean_squared_error: 28138.9954 - mean_absolute_error: 135.4953 - val_loss: 46.1601 - val_mean_squared_error: 27448.7012 - val_mean_absolute_error: 135.3173\n",
      "Epoch 44/1000\n",
      " - 0s - loss: 48.3893 - mean_squared_error: 24785.5562 - mean_absolute_error: 128.1077 - val_loss: 42.8395 - val_mean_squared_error: 23157.7500 - val_mean_absolute_error: 121.1001\n",
      "Epoch 45/1000\n",
      " - 0s - loss: 47.1990 - mean_squared_error: 22885.9213 - mean_absolute_error: 122.4502 - val_loss: 40.4085 - val_mean_squared_error: 20537.1816 - val_mean_absolute_error: 108.4279\n",
      "Epoch 46/1000\n",
      " - 0s - loss: 46.5108 - mean_squared_error: 21915.3667 - mean_absolute_error: 118.1790 - val_loss: 39.4949 - val_mean_squared_error: 19289.6055 - val_mean_absolute_error: 102.8799\n",
      "Epoch 47/1000\n",
      " - 0s - loss: 46.2652 - mean_squared_error: 21928.9038 - mean_absolute_error: 116.4363 - val_loss: 39.3178 - val_mean_squared_error: 18822.1348 - val_mean_absolute_error: 101.8852\n",
      "Epoch 48/1000\n",
      " - 0s - loss: 46.1553 - mean_squared_error: 22075.5300 - mean_absolute_error: 115.9476 - val_loss: 39.1984 - val_mean_squared_error: 18643.8223 - val_mean_absolute_error: 101.8194\n",
      "Epoch 49/1000\n",
      " - 0s - loss: 45.8844 - mean_squared_error: 21800.5282 - mean_absolute_error: 115.0282 - val_loss: 38.7542 - val_mean_squared_error: 18240.4238 - val_mean_absolute_error: 100.4979\n",
      "Epoch 50/1000\n",
      " - 0s - loss: 45.2875 - mean_squared_error: 20958.6531 - mean_absolute_error: 112.6465 - val_loss: 37.9161 - val_mean_squared_error: 17447.9629 - val_mean_absolute_error: 97.2322\n",
      "Epoch 51/1000\n",
      " - 0s - loss: 44.2411 - mean_squared_error: 19572.9581 - mean_absolute_error: 108.1858 - val_loss: 36.8865 - val_mean_squared_error: 16432.4160 - val_mean_absolute_error: 92.9738\n",
      "Epoch 52/1000\n",
      " - 0s - loss: 43.0657 - mean_squared_error: 17966.2940 - mean_absolute_error: 102.7680 - val_loss: 35.8200 - val_mean_squared_error: 15373.9678 - val_mean_absolute_error: 88.3510\n",
      "Epoch 53/1000\n",
      " - 0s - loss: 41.8055 - mean_squared_error: 16314.4994 - mean_absolute_error: 96.9770 - val_loss: 35.3727 - val_mean_squared_error: 14507.1719 - val_mean_absolute_error: 85.2915\n",
      "Epoch 54/1000\n",
      " - 0s - loss: 40.8799 - mean_squared_error: 14948.5294 - mean_absolute_error: 92.7080 - val_loss: 35.2740 - val_mean_squared_error: 13936.0586 - val_mean_absolute_error: 83.2738\n",
      "Epoch 55/1000\n",
      " - 0s - loss: 40.1501 - mean_squared_error: 13935.3639 - mean_absolute_error: 89.4039 - val_loss: 35.4089 - val_mean_squared_error: 13636.2773 - val_mean_absolute_error: 82.5687\n",
      "Epoch 56/1000\n",
      " - 0s - loss: 39.7308 - mean_squared_error: 13217.6640 - mean_absolute_error: 87.3618 - val_loss: 35.6155 - val_mean_squared_error: 13465.2607 - val_mean_absolute_error: 82.6139\n",
      "Epoch 57/1000\n",
      " - 0s - loss: 39.4683 - mean_squared_error: 12752.8942 - mean_absolute_error: 86.1877 - val_loss: 35.8267 - val_mean_squared_error: 13358.6826 - val_mean_absolute_error: 82.7575\n",
      "Epoch 58/1000\n",
      " - 0s - loss: 39.1342 - mean_squared_error: 12407.3886 - mean_absolute_error: 84.9253 - val_loss: 35.9809 - val_mean_squared_error: 13247.4688 - val_mean_absolute_error: 82.6977\n",
      "Epoch 59/1000\n",
      " - 0s - loss: 38.8438 - mean_squared_error: 12174.5604 - mean_absolute_error: 83.8099 - val_loss: 36.0289 - val_mean_squared_error: 13117.6064 - val_mean_absolute_error: 82.4125\n",
      "Epoch 60/1000\n",
      " - 0s - loss: 38.4380 - mean_squared_error: 11950.5562 - mean_absolute_error: 82.3987 - val_loss: 35.9386 - val_mean_squared_error: 12978.3291 - val_mean_absolute_error: 81.8576\n",
      "Epoch 61/1000\n",
      " - 0s - loss: 38.0242 - mean_squared_error: 11768.0323 - mean_absolute_error: 80.9604 - val_loss: 35.8101 - val_mean_squared_error: 12884.8594 - val_mean_absolute_error: 81.3952\n",
      "Epoch 62/1000\n",
      " - 0s - loss: 37.5985 - mean_squared_error: 11641.9516 - mean_absolute_error: 79.5664 - val_loss: 35.7827 - val_mean_squared_error: 12853.5625 - val_mean_absolute_error: 81.3853\n",
      "Epoch 63/1000\n",
      " - 0s - loss: 37.3098 - mean_squared_error: 11626.5180 - mean_absolute_error: 78.7755 - val_loss: 35.7838 - val_mean_squared_error: 12900.5469 - val_mean_absolute_error: 81.5827\n",
      "Epoch 64/1000\n",
      " - 0s - loss: 37.0728 - mean_squared_error: 11614.5622 - mean_absolute_error: 78.1895 - val_loss: 35.8494 - val_mean_squared_error: 12938.9766 - val_mean_absolute_error: 81.9480\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Activation, Dense, Dropout\n",
    "from keras.objectives import MSE, MAE\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(input_dim=X_Train.shape[1], output_dim=256))\n",
    "# model.add(Activation(\"tanh\"))\n",
    "model.add(Activation(\"relu\"))\n",
    "#model.add(Dropout(0.50))\n",
    "model.add(Dense(output_dim=128))\n",
    "model.add(Activation(\"relu\"))\n",
    "#model.add(Dropout(0.50))\n",
    "model.add(Dense(output_dim=64))\n",
    "model.add(Activation(\"relu\"))\n",
    "#model.add(Dropout(0.50))\n",
    "model.add(Dense(1, activation='linear'))\n",
    "model.compile(loss=smape_kun2, optimizer='adam', metrics=['mse','mae'])\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
    "train_log = model.fit(X_Train, Y_Train.values, batch_size=256, nb_epoch=1000, validation_split=0.1, verbose=2, callbacks=[early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[911.6407, 187.3954, 571.9978, 957.6853, 495.19437, 229.01299, 921.4407, 247.2915, 712.6823, 254.09769, 276.24014, 280.7891, 627.2105, 261.51898, 694.2395, 815.4807, 273.53857, 164.92758, 250.02097, 232.25717, 940.82605, 244.24734, 228.15686, 175.56685, 142.95177]\n",
      "35.24893362544566\n"
     ]
    }
   ],
   "source": [
    "preds = model.predict(X_Test)\n",
    "def twoone(list1):\n",
    "    return [val for lst in list1 for val in lst]\n",
    "\n",
    "pred = twoone(preds)\n",
    "print(pred)\n",
    "print(smape_kun(Y_Test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wider: -39.94 (9.28) MSE\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "def wider_model():\n",
    "\t# create model\n",
    "\tmodel = Sequential()\n",
    "\tmodel.add(Dense(20, input_dim=30, kernel_initializer='normal', activation='relu'))\n",
    "\tmodel.add(Dense(1, kernel_initializer='normal'))\n",
    "\t# Compile model\n",
    "\tmodel.compile(loss=smape_kun2, optimizer='adam')\n",
    "\treturn model\n",
    "\n",
    "# fix random seed for reproducibility\n",
    "seed = 7\n",
    "np.random.seed(seed)\n",
    "estimators = []\n",
    "estimators.append(('standardize', StandardScaler()))\n",
    "estimators.append(('mlp', KerasRegressor(build_fn=wider_model, epochs=100, batch_size=5, verbose=0)))\n",
    "pipeline = Pipeline(estimators)\n",
    "kfold = KFold(n_splits=10, random_state=seed)\n",
    "results = cross_val_score(pipeline, X_Train, Y_Train, cv=kfold, n_jobs=1)\n",
    "print(\"Wider: %.2f (%.2f) MSE\" % (results.mean(), results.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[911.6407, 187.3954, 571.9978, 957.6853, 495.19437, 229.01299, 921.4407, 247.2915, 712.6823, 254.09769, 276.24014, 280.7891, 627.2105, 261.51898, 694.2395, 815.4807, 273.53857, 164.92758, 250.02097, 232.25717, 940.82605, 244.24734, 228.15686, 175.56685, 142.95177]\n",
      "35.24893362544566\n"
     ]
    }
   ],
   "source": [
    "preds = model.predict(X_Test)\n",
    "\n",
    "preds = twoone(preds)\n",
    "print(preds)\n",
    "print(smape_kun(Y_Test,preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "475/475 [==============================] - 27s 57ms/step - loss: 202821.8991 - mean_absolute_error: 362.6674 - acc: 0.0232\n",
      "Epoch 2/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 196451.7145 - mean_absolute_error: 355.0742 - acc: 0.0000e+00\n",
      "Epoch 3/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 194513.5022 - mean_absolute_error: 352.6288 - acc: 0.0000e+00\n",
      "Epoch 4/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 193337.2537 - mean_absolute_error: 351.2096 - acc: 0.0000e+00\n",
      "Epoch 5/100\n",
      "475/475 [==============================] - 6s 13ms/step - loss: 192200.6888 - mean_absolute_error: 349.8334 - acc: 0.0000e+00\n",
      "Epoch 6/100\n",
      "475/475 [==============================] - 6s 12ms/step - loss: 191086.2868 - mean_absolute_error: 348.5499 - acc: 0.0000e+00\n",
      "Epoch 7/100\n",
      "475/475 [==============================] - 6s 12ms/step - loss: 190031.5357 - mean_absolute_error: 347.2829 - acc: 0.0000e+00\n",
      "Epoch 8/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 188985.2757 - mean_absolute_error: 346.0319 - acc: 0.0000e+00\n",
      "Epoch 9/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 187968.7251 - mean_absolute_error: 344.7933 - acc: 0.0000e+00\n",
      "Epoch 10/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 186963.3622 - mean_absolute_error: 343.5812 - acc: 0.0000e+00\n",
      "Epoch 11/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 185971.4799 - mean_absolute_error: 342.3519 - acc: 0.0000e+00\n",
      "Epoch 12/100\n",
      "475/475 [==============================] - 6s 12ms/step - loss: 184977.0743 - mean_absolute_error: 341.1631 - acc: 0.0000e+00\n",
      "Epoch 13/100\n",
      "475/475 [==============================] - 6s 13ms/step - loss: 184011.3050 - mean_absolute_error: 339.9652 - acc: 0.0000e+00\n",
      "Epoch 14/100\n",
      "475/475 [==============================] - 6s 12ms/step - loss: 183050.8482 - mean_absolute_error: 338.7816 - acc: 0.0000e+00\n",
      "Epoch 15/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 182093.1026 - mean_absolute_error: 337.6314 - acc: 0.0000e+00\n",
      "Epoch 16/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 181157.1225 - mean_absolute_error: 336.4378 - acc: 0.0000e+00\n",
      "Epoch 17/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 180212.7833 - mean_absolute_error: 335.2804 - acc: 0.0000e+00\n",
      "Epoch 18/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 179293.7012 - mean_absolute_error: 334.1387 - acc: 0.0000e+00\n",
      "Epoch 19/100\n",
      "475/475 [==============================] - 6s 12ms/step - loss: 178364.1012 - mean_absolute_error: 332.9602 - acc: 0.0000e+00\n",
      "Epoch 20/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 177458.4034 - mean_absolute_error: 331.8382 - acc: 0.0000e+00\n",
      "Epoch 21/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 176550.2337 - mean_absolute_error: 330.7085 - acc: 0.0000e+00\n",
      "Epoch 22/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 175650.2513 - mean_absolute_error: 329.5539 - acc: 0.0000e+00\n",
      "Epoch 23/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 174759.2440 - mean_absolute_error: 328.4249 - acc: 0.0000e+00\n",
      "Epoch 24/100\n",
      "475/475 [==============================] - 6s 13ms/step - loss: 173870.4009 - mean_absolute_error: 327.2968 - acc: 0.0000e+00\n",
      "Epoch 25/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 173004.0581 - mean_absolute_error: 326.1725 - acc: 0.0000e+00\n",
      "Epoch 26/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 172118.8774 - mean_absolute_error: 325.0427 - acc: 0.0000e+00\n",
      "Epoch 27/100\n",
      "475/475 [==============================] - 6s 12ms/step - loss: 171253.8317 - mean_absolute_error: 323.9431 - acc: 0.0000e+00\n",
      "Epoch 28/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 170402.9777 - mean_absolute_error: 322.8299 - acc: 0.0000e+00\n",
      "Epoch 29/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 169539.5638 - mean_absolute_error: 321.7165 - acc: 0.0000e+00\n",
      "Epoch 30/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 168689.4277 - mean_absolute_error: 320.6176 - acc: 0.0000e+00\n",
      "Epoch 31/100\n",
      "475/475 [==============================] - 5s 10ms/step - loss: 167858.1762 - mean_absolute_error: 319.5221 - acc: 0.0000e+00\n",
      "Epoch 32/100\n",
      "475/475 [==============================] - 5s 10ms/step - loss: 167008.9288 - mean_absolute_error: 318.4267 - acc: 0.0000e+00\n",
      "Epoch 33/100\n",
      "475/475 [==============================] - 5s 10ms/step - loss: 166188.2261 - mean_absolute_error: 317.3114 - acc: 0.0000e+00\n",
      "Epoch 34/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 165361.4506 - mean_absolute_error: 316.2446 - acc: 0.0000e+00\n",
      "Epoch 35/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 164545.5386 - mean_absolute_error: 315.1523 - acc: 0.0000e+00\n",
      "Epoch 36/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 163729.3075 - mean_absolute_error: 314.0737 - acc: 0.0000e+00\n",
      "Epoch 37/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 162919.1214 - mean_absolute_error: 313.0038 - acc: 0.0000e+00\n",
      "Epoch 38/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 162120.1062 - mean_absolute_error: 311.9211 - acc: 0.0000e+00\n",
      "Epoch 39/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 161334.5730 - mean_absolute_error: 310.8598 - acc: 0.0000e+00\n",
      "Epoch 40/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 160532.8241 - mean_absolute_error: 309.7986 - acc: 0.0000e+00\n",
      "Epoch 41/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 159751.1451 - mean_absolute_error: 308.7143 - acc: 0.0000e+00\n",
      "Epoch 42/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 158973.3361 - mean_absolute_error: 307.6895 - acc: 0.0000e+00\n",
      "Epoch 43/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 158195.3899 - mean_absolute_error: 306.6490 - acc: 0.0000e+00\n",
      "Epoch 44/100\n",
      "475/475 [==============================] - 5s 10ms/step - loss: 157428.2433 - mean_absolute_error: 305.5939 - acc: 0.0000e+00\n",
      "Epoch 45/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 156676.3204 - mean_absolute_error: 304.5663 - acc: 0.0000e+00\n",
      "Epoch 46/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 155905.9262 - mean_absolute_error: 303.5189 - acc: 0.0000e+00\n",
      "Epoch 47/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 155156.6999 - mean_absolute_error: 302.5084 - acc: 0.0000e+00\n",
      "Epoch 48/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 154403.5342 - mean_absolute_error: 301.4848 - acc: 0.0000e+00\n",
      "Epoch 49/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 153669.1100 - mean_absolute_error: 300.4769 - acc: 0.0000e+00\n",
      "Epoch 50/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 152930.4702 - mean_absolute_error: 299.4812 - acc: 0.0000e+00\n",
      "Epoch 51/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 152204.5099 - mean_absolute_error: 298.4765 - acc: 0.0000e+00\n",
      "Epoch 52/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 151476.2298 - mean_absolute_error: 297.4547 - acc: 0.0000e+00\n",
      "Epoch 53/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 150739.7727 - mean_absolute_error: 296.4876 - acc: 0.0000e+00\n",
      "Epoch 54/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 150042.6923 - mean_absolute_error: 295.4933 - acc: 0.0000e+00\n",
      "Epoch 55/100\n",
      "475/475 [==============================] - 6s 12ms/step - loss: 149319.0248 - mean_absolute_error: 294.5338 - acc: 0.0000e+00\n",
      "Epoch 56/100\n",
      "475/475 [==============================] - 6s 13ms/step - loss: 148606.1578 - mean_absolute_error: 293.5529 - acc: 0.0000e+00\n",
      "Epoch 57/100\n",
      "475/475 [==============================] - 6s 12ms/step - loss: 147915.3223 - mean_absolute_error: 292.5640 - acc: 0.0000e+00\n",
      "Epoch 58/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 147221.8562 - mean_absolute_error: 291.6249 - acc: 0.0000e+00\n",
      "Epoch 59/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 146510.0205 - mean_absolute_error: 290.6619 - acc: 0.0000e+00\n",
      "Epoch 60/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "475/475 [==============================] - 4s 9ms/step - loss: 145837.8639 - mean_absolute_error: 289.7079 - acc: 0.0000e+00\n",
      "Epoch 61/100\n",
      "475/475 [==============================] - 4s 8ms/step - loss: 145141.7851 - mean_absolute_error: 288.7561 - acc: 0.0000e+00\n",
      "Epoch 62/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 144472.5931 - mean_absolute_error: 287.8102 - acc: 0.0000e+00\n",
      "Epoch 63/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 143795.0927 - mean_absolute_error: 286.8742 - acc: 0.0000e+00\n",
      "Epoch 64/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 143118.9632 - mean_absolute_error: 285.9249 - acc: 0.0000e+00\n",
      "Epoch 65/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 142465.5583 - mean_absolute_error: 285.0191 - acc: 0.0000e+00\n",
      "Epoch 66/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 141800.0370 - mean_absolute_error: 284.0722 - acc: 0.0000e+00\n",
      "Epoch 67/100\n",
      "475/475 [==============================] - 5s 10ms/step - loss: 141137.5125 - mean_absolute_error: 283.1514 - acc: 0.0000e+00\n",
      "Epoch 68/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 140492.6428 - mean_absolute_error: 282.2351 - acc: 0.0000e+00\n",
      "Epoch 69/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 139845.8302 - mean_absolute_error: 281.3178 - acc: 0.0000e+00\n",
      "Epoch 70/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 139209.0713 - mean_absolute_error: 280.3826 - acc: 0.0000e+00\n",
      "Epoch 71/100\n",
      "475/475 [==============================] - 5s 10ms/step - loss: 138561.9391 - mean_absolute_error: 279.4621 - acc: 0.0000e+00\n",
      "Epoch 72/100\n",
      "475/475 [==============================] - 5s 10ms/step - loss: 137938.7003 - mean_absolute_error: 278.5530 - acc: 0.0000e+00\n",
      "Epoch 73/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 137305.0189 - mean_absolute_error: 277.6297 - acc: 0.0000e+00\n",
      "Epoch 74/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 136672.9206 - mean_absolute_error: 276.7350 - acc: 0.0000e+00\n",
      "Epoch 75/100\n",
      "475/475 [==============================] - 5s 10ms/step - loss: 136066.6048 - mean_absolute_error: 275.8364 - acc: 0.0000e+00\n",
      "Epoch 76/100\n",
      "475/475 [==============================] - 5s 10ms/step - loss: 135449.7227 - mean_absolute_error: 274.9144 - acc: 0.0000e+00\n",
      "Epoch 77/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 134846.5142 - mean_absolute_error: 274.0332 - acc: 0.0000e+00\n",
      "Epoch 78/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 134218.5298 - mean_absolute_error: 273.1294 - acc: 0.0000e+00\n",
      "Epoch 79/100\n",
      "475/475 [==============================] - 5s 11ms/step - loss: 133630.9570 - mean_absolute_error: 272.2690 - acc: 0.0000e+00\n",
      "Epoch 80/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 133025.8988 - mean_absolute_error: 271.4166 - acc: 0.0000e+00\n",
      "Epoch 81/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 132429.6924 - mean_absolute_error: 270.5583 - acc: 0.0000e+00\n",
      "Epoch 82/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 131844.4665 - mean_absolute_error: 269.7246 - acc: 0.0000e+00\n",
      "Epoch 83/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 131271.3757 - mean_absolute_error: 268.8840 - acc: 0.0000e+00\n",
      "Epoch 84/100\n",
      "475/475 [==============================] - 5s 10ms/step - loss: 130658.3854 - mean_absolute_error: 268.0425 - acc: 0.0000e+00\n",
      "Epoch 85/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 130102.5063 - mean_absolute_error: 267.2258 - acc: 0.0000e+00\n",
      "Epoch 86/100\n",
      "475/475 [==============================] - 5s 10ms/step - loss: 129525.3101 - mean_absolute_error: 266.3719 - acc: 0.0000e+00\n",
      "Epoch 87/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 128960.6533 - mean_absolute_error: 265.5774 - acc: 0.0000e+00\n",
      "Epoch 88/100\n",
      "475/475 [==============================] - 5s 9ms/step - loss: 128381.2617 - mean_absolute_error: 264.7384 - acc: 0.0000e+00\n",
      "Epoch 89/100\n",
      "475/475 [==============================] - 5s 9ms/step - loss: 127832.4589 - mean_absolute_error: 263.9396 - acc: 0.0000e+00\n",
      "Epoch 90/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 127273.8583 - mean_absolute_error: 263.1504 - acc: 0.0000e+00\n",
      "Epoch 91/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 126726.0896 - mean_absolute_error: 262.3290 - acc: 0.0000e+00\n",
      "Epoch 92/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 126168.1014 - mean_absolute_error: 261.5258 - acc: 0.0000e+00\n",
      "Epoch 93/100\n",
      "475/475 [==============================] - 4s 8ms/step - loss: 125626.6365 - mean_absolute_error: 260.7223 - acc: 0.0000e+00\n",
      "Epoch 94/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 125090.4165 - mean_absolute_error: 259.9385 - acc: 0.0000e+00\n",
      "Epoch 95/100\n",
      "475/475 [==============================] - 5s 10ms/step - loss: 124553.9194 - mean_absolute_error: 259.1546 - acc: 0.0000e+00\n",
      "Epoch 96/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 124020.5415 - mean_absolute_error: 258.3880 - acc: 0.0000e+00\n",
      "Epoch 97/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 123487.1697 - mean_absolute_error: 257.5917 - acc: 0.0000e+00\n",
      "Epoch 98/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 122964.9750 - mean_absolute_error: 256.8475 - acc: 0.0000e+00\n",
      "Epoch 99/100\n",
      "475/475 [==============================] - 5s 10ms/step - loss: 122450.5227 - mean_absolute_error: 256.0958 - acc: 0.0000e+00\n",
      "Epoch 100/100\n",
      "475/475 [==============================] - 4s 9ms/step - loss: 121929.7867 - mean_absolute_error: 255.3338 - acc: 0.0000e+00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1b59dca2cf8>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import keras.backend as K\n",
    "def smape_kun2(y_true, y_pred):\n",
    "    return K.mean((np.abs(y_pred - y_true) * 200/ (np.abs(y_pred) + np.abs(y_true))))\n",
    "\n",
    "from keras.layers import LSTM\n",
    "import keras.backend as K\n",
    "\n",
    "X_Train2 = np.reshape(X_Train, (X_Train.shape[0], X_Train.shape[1], 1))\n",
    "\n",
    "regressor = Sequential()\n",
    "\n",
    "regressor.add(LSTM(units = 100, return_sequences = True, input_shape = (X_Train2.shape[1],1)))\n",
    "# regressor.add(Dropout(0.2))\n",
    "\n",
    "regressor.add(LSTM(units = 100, return_sequences = True))\n",
    "# regressor.add(Dropout(0.2))\n",
    "\n",
    "regressor.add(LSTM(units = 100, return_sequences = True))\n",
    "# regressor.add(Dropout(0.2))\n",
    "\n",
    "regressor.add(LSTM(units = 100))\n",
    "# regressor.add(Dropout(0.2))\n",
    "\n",
    "regressor.add(Dense(units = 1))\n",
    "\n",
    "regressor.compile(optimizer = 'adam', loss='mean_squared_error',  metrics=['mae','accuracy'])\n",
    "\n",
    "regressor.fit(X_Train2, Y_Train, epochs = 100, batch_size = 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123, 142.48123]\n",
      "91.85384851003653\n"
     ]
    }
   ],
   "source": [
    "X_Test2 = np.reshape(X_Test, (X_Test.shape[0], X_Test.shape[1], 1))\n",
    "\n",
    "preds = regressor.predict(X_Test2)\n",
    "\n",
    "preds = twoone(preds)\n",
    "print(preds)\n",
    "print(smape_kun(Y_Test,preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "475/475 [==============================] - 10s 20ms/step - loss: 204036.5968 - smape_kun2: 198.5021\n",
      "Epoch 2/500\n",
      "475/475 [==============================] - 0s 141us/step - loss: 202708.7019 - smape_kun2: 196.4696\n",
      "Epoch 3/500\n",
      "475/475 [==============================] - 0s 99us/step - loss: 201133.3982 - smape_kun2: 194.6221\n",
      "Epoch 4/500\n",
      "475/475 [==============================] - 0s 130us/step - loss: 198962.5322 - smape_kun2: 192.8430\n",
      "Epoch 5/500\n",
      "475/475 [==============================] - 0s 130us/step - loss: 195706.1873 - smape_kun2: 190.5870\n",
      "Epoch 6/500\n",
      "475/475 [==============================] - 0s 143us/step - loss: 191418.3061 - smape_kun2: 187.9163\n",
      "Epoch 7/500\n",
      "475/475 [==============================] - 0s 178us/step - loss: 185839.9747 - smape_kun2: 184.8510\n",
      "Epoch 8/500\n",
      "475/475 [==============================] - 0s 124us/step - loss: 178785.6684 - smape_kun2: 180.8041\n",
      "Epoch 9/500\n",
      "475/475 [==============================] - 0s 218us/step - loss: 170023.8937 - smape_kun2: 176.1281\n",
      "Epoch 10/500\n",
      "475/475 [==============================] - 0s 216us/step - loss: 161170.6503 - smape_kun2: 170.5348\n",
      "Epoch 11/500\n",
      "475/475 [==============================] - 0s 208us/step - loss: 151192.5098 - smape_kun2: 164.3745\n",
      "Epoch 12/500\n",
      "475/475 [==============================] - 0s 181us/step - loss: 143542.4468 - smape_kun2: 158.4061\n",
      "Epoch 13/500\n",
      "475/475 [==============================] - 0s 218us/step - loss: 137808.6543 - smape_kun2: 153.1916\n",
      "Epoch 14/500\n",
      "475/475 [==============================] - 0s 170us/step - loss: 133835.4497 - smape_kun2: 148.9678\n",
      "Epoch 15/500\n",
      "475/475 [==============================] - 0s 168us/step - loss: 130567.4682 - smape_kun2: 146.4039\n",
      "Epoch 16/500\n",
      "475/475 [==============================] - 0s 176us/step - loss: 127784.6199 - smape_kun2: 144.9697\n",
      "Epoch 17/500\n",
      "475/475 [==============================] - 0s 193us/step - loss: 124915.2141 - smape_kun2: 144.6079\n",
      "Epoch 18/500\n",
      "475/475 [==============================] - 0s 130us/step - loss: 122003.1788 - smape_kun2: 144.5424\n",
      "Epoch 19/500\n",
      "475/475 [==============================] - 0s 126us/step - loss: 118798.7246 - smape_kun2: 143.9884\n",
      "Epoch 20/500\n",
      "475/475 [==============================] - 0s 147us/step - loss: 115580.9678 - smape_kun2: 143.1008\n",
      "Epoch 21/500\n",
      "475/475 [==============================] - 0s 130us/step - loss: 112304.6534 - smape_kun2: 142.8189\n",
      "Epoch 22/500\n",
      "475/475 [==============================] - 0s 130us/step - loss: 108627.9089 - smape_kun2: 142.4969\n",
      "Epoch 23/500\n",
      "475/475 [==============================] - 0s 130us/step - loss: 104941.4925 - smape_kun2: 141.9222\n",
      "Epoch 24/500\n",
      "475/475 [==============================] - 0s 124us/step - loss: 101054.6867 - smape_kun2: 141.3998\n",
      "Epoch 25/500\n",
      "475/475 [==============================] - 0s 118us/step - loss: 96791.6244 - smape_kun2: 141.0719\n",
      "Epoch 26/500\n",
      "475/475 [==============================] - 0s 134us/step - loss: 92299.6879 - smape_kun2: 140.2917\n",
      "Epoch 27/500\n",
      "475/475 [==============================] - 0s 122us/step - loss: 87656.7114 - smape_kun2: 139.0290\n",
      "Epoch 28/500\n",
      "475/475 [==============================] - 0s 178us/step - loss: 82532.8526 - smape_kun2: 138.2623\n",
      "Epoch 29/500\n",
      "475/475 [==============================] - 0s 187us/step - loss: 77297.4564 - smape_kun2: 137.4894\n",
      "Epoch 30/500\n",
      "475/475 [==============================] - 0s 118us/step - loss: 71837.4653 - smape_kun2: 135.5474\n",
      "Epoch 31/500\n",
      "475/475 [==============================] - 0s 124us/step - loss: 66329.5076 - smape_kun2: 132.8144\n",
      "Epoch 32/500\n",
      "475/475 [==============================] - 0s 128us/step - loss: 60565.8304 - smape_kun2: 129.9466\n",
      "Epoch 33/500\n",
      "475/475 [==============================] - 0s 155us/step - loss: 54691.9507 - smape_kun2: 126.2962\n",
      "Epoch 34/500\n",
      "475/475 [==============================] - 0s 202us/step - loss: 49065.1886 - smape_kun2: 120.3007\n",
      "Epoch 35/500\n",
      "475/475 [==============================] - 0s 235us/step - loss: 43193.3256 - smape_kun2: 112.3049\n",
      "Epoch 36/500\n",
      "475/475 [==============================] - 0s 216us/step - loss: 37604.8490 - smape_kun2: 104.2107\n",
      "Epoch 37/500\n",
      "475/475 [==============================] - 0s 181us/step - loss: 32469.1539 - smape_kun2: 95.4743\n",
      "Epoch 38/500\n",
      "475/475 [==============================] - 0s 189us/step - loss: 27398.1257 - smape_kun2: 85.6950\n",
      "Epoch 39/500\n",
      "475/475 [==============================] - 0s 164us/step - loss: 23061.3950 - smape_kun2: 77.3420\n",
      "Epoch 40/500\n",
      "475/475 [==============================] - 0s 197us/step - loss: 19156.8218 - smape_kun2: 69.4382\n",
      "Epoch 41/500\n",
      "475/475 [==============================] - 0s 170us/step - loss: 15950.8269 - smape_kun2: 63.2039\n",
      "Epoch 42/500\n",
      "475/475 [==============================] - 0s 166us/step - loss: 13215.3480 - smape_kun2: 56.5138\n",
      "Epoch 43/500\n",
      "475/475 [==============================] - 0s 139us/step - loss: 11055.7746 - smape_kun2: 50.2729\n",
      "Epoch 44/500\n",
      "475/475 [==============================] - 0s 124us/step - loss: 9401.7835 - smape_kun2: 45.5309\n",
      "Epoch 45/500\n",
      "475/475 [==============================] - 0s 132us/step - loss: 8200.1962 - smape_kun2: 42.7479\n",
      "Epoch 46/500\n",
      "475/475 [==============================] - 0s 128us/step - loss: 7341.0385 - smape_kun2: 40.7274\n",
      "Epoch 47/500\n",
      "475/475 [==============================] - 0s 143us/step - loss: 6754.4061 - smape_kun2: 38.8838\n",
      "Epoch 48/500\n",
      "475/475 [==============================] - 0s 109us/step - loss: 6407.5610 - smape_kun2: 38.5067\n",
      "Epoch 49/500\n",
      "475/475 [==============================] - 0s 122us/step - loss: 6150.1786 - smape_kun2: 37.3894\n",
      "Epoch 50/500\n",
      "475/475 [==============================] - 0s 136us/step - loss: 5998.8090 - smape_kun2: 36.8786\n",
      "Epoch 51/500\n",
      "475/475 [==============================] - 0s 113us/step - loss: 5928.3962 - smape_kun2: 36.6664\n",
      "Epoch 52/500\n",
      "475/475 [==============================] - 0s 113us/step - loss: 5881.4781 - smape_kun2: 36.7584\n",
      "Epoch 53/500\n",
      "475/475 [==============================] - 0s 118us/step - loss: 5831.0183 - smape_kun2: 36.7802\n",
      "Epoch 54/500\n",
      "475/475 [==============================] - 0s 120us/step - loss: 5763.5724 - smape_kun2: 36.5979\n",
      "Epoch 55/500\n",
      "475/475 [==============================] - 0s 126us/step - loss: 5746.2117 - smape_kun2: 36.5936\n",
      "Epoch 56/500\n",
      "475/475 [==============================] - 0s 153us/step - loss: 5730.7687 - smape_kun2: 36.5587\n",
      "Epoch 57/500\n",
      "475/475 [==============================] - 0s 178us/step - loss: 5693.7329 - smape_kun2: 36.4627\n",
      "Epoch 58/500\n",
      "475/475 [==============================] - 0s 136us/step - loss: 5683.3443 - smape_kun2: 36.4360\n",
      "Epoch 59/500\n",
      "475/475 [==============================] - 0s 120us/step - loss: 5672.4209 - smape_kun2: 36.4369\n",
      "Epoch 60/500\n",
      "475/475 [==============================] - 0s 134us/step - loss: 5648.7028 - smape_kun2: 36.3392\n",
      "Epoch 61/500\n",
      "475/475 [==============================] - 0s 227us/step - loss: 5658.0760 - smape_kun2: 36.3996\n",
      "Epoch 62/500\n",
      "475/475 [==============================] - 0s 162us/step - loss: 5632.1975 - smape_kun2: 36.3181\n",
      "Epoch 63/500\n",
      "475/475 [==============================] - 0s 208us/step - loss: 5622.8333 - smape_kun2: 36.3208\n",
      "Epoch 64/500\n",
      "475/475 [==============================] - 0s 157us/step - loss: 5596.0034 - smape_kun2: 36.2729\n",
      "Epoch 65/500\n",
      "475/475 [==============================] - 0s 113us/step - loss: 5585.4278 - smape_kun2: 36.3125\n",
      "Epoch 66/500\n",
      "475/475 [==============================] - 0s 120us/step - loss: 5609.3201 - smape_kun2: 36.4847\n",
      "Epoch 67/500\n",
      "475/475 [==============================] - 0s 139us/step - loss: 5564.1719 - smape_kun2: 36.2805\n",
      "Epoch 68/500\n",
      "475/475 [==============================] - 0s 111us/step - loss: 5586.7558 - smape_kun2: 36.3304\n",
      "Epoch 69/500\n",
      "475/475 [==============================] - 0s 126us/step - loss: 5544.1418 - smape_kun2: 36.3607\n",
      "Epoch 70/500\n",
      "475/475 [==============================] - 0s 126us/step - loss: 5547.9500 - smape_kun2: 36.2431\n",
      "Epoch 71/500\n",
      "475/475 [==============================] - 0s 118us/step - loss: 5534.4092 - smape_kun2: 36.2659\n",
      "Epoch 72/500\n",
      "475/475 [==============================] - 0s 139us/step - loss: 5537.5880 - smape_kun2: 36.2853\n",
      "Epoch 73/500\n",
      "475/475 [==============================] - 0s 120us/step - loss: 5551.5642 - smape_kun2: 36.3955\n",
      "Epoch 74/500\n",
      "475/475 [==============================] - 0s 139us/step - loss: 5526.5130 - smape_kun2: 36.1734\n",
      "Epoch 75/500\n",
      "475/475 [==============================] - 0s 115us/step - loss: 5529.7883 - smape_kun2: 36.1987\n",
      "Epoch 76/500\n",
      "475/475 [==============================] - 0s 155us/step - loss: 5522.6981 - smape_kun2: 36.3925\n",
      "Epoch 77/500\n",
      "475/475 [==============================] - 0s 309us/step - loss: 5515.1383 - smape_kun2: 36.1853\n",
      "Epoch 78/500\n",
      "475/475 [==============================] - 0s 254us/step - loss: 5498.4396 - smape_kun2: 36.0854\n",
      "Epoch 79/500\n",
      "475/475 [==============================] - 0s 286us/step - loss: 5491.2148 - smape_kun2: 36.2415\n",
      "Epoch 80/500\n",
      "475/475 [==============================] - 0s 283us/step - loss: 5481.9041 - smape_kun2: 36.2192\n",
      "Epoch 81/500\n",
      "475/475 [==============================] - 0s 340us/step - loss: 5482.8853 - smape_kun2: 36.1929\n",
      "Epoch 82/500\n",
      "475/475 [==============================] - 0s 260us/step - loss: 5486.4230 - smape_kun2: 36.1666\n",
      "Epoch 83/500\n",
      "475/475 [==============================] - 0s 269us/step - loss: 5526.9952 - smape_kun2: 36.3833\n",
      "Epoch 84/500\n",
      "475/475 [==============================] - ETA: 0s - loss: 5338.3874 - smape_kun2: 34.00 - 0s 239us/step - loss: 5485.0657 - smape_kun2: 36.2241\n",
      "Epoch 85/500\n",
      "475/475 [==============================] - 0s 220us/step - loss: 5470.5504 - smape_kun2: 36.1097\n",
      "Epoch 86/500\n",
      "475/475 [==============================] - 0s 248us/step - loss: 5461.6687 - smape_kun2: 36.1526\n",
      "Epoch 87/500\n",
      "475/475 [==============================] - 0s 338us/step - loss: 5473.3285 - smape_kun2: 36.2625\n",
      "Epoch 88/500\n",
      "475/475 [==============================] - 0s 231us/step - loss: 5484.1142 - smape_kun2: 36.1772\n",
      "Epoch 89/500\n",
      "475/475 [==============================] - 0s 231us/step - loss: 5471.3855 - smape_kun2: 36.1509\n",
      "Epoch 90/500\n",
      "475/475 [==============================] - 0s 265us/step - loss: 5452.3095 - smape_kun2: 36.1656\n",
      "Epoch 91/500\n",
      "475/475 [==============================] - 0s 250us/step - loss: 5446.5490 - smape_kun2: 36.2671\n",
      "Epoch 92/500\n",
      "475/475 [==============================] - 0s 233us/step - loss: 5466.6643 - smape_kun2: 36.2521\n",
      "Epoch 93/500\n",
      "475/475 [==============================] - 0s 286us/step - loss: 5473.5558 - smape_kun2: 36.1470\n",
      "Epoch 94/500\n",
      "475/475 [==============================] - 0s 315us/step - loss: 5443.3883 - smape_kun2: 36.1173\n",
      "Epoch 95/500\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "475/475 [==============================] - 0s 195us/step - loss: 5326.0696 - smape_kun2: 36.1678\n",
      "Epoch 397/500\n",
      "475/475 [==============================] - 0s 189us/step - loss: 5334.1663 - smape_kun2: 36.1293\n",
      "Epoch 398/500\n",
      "475/475 [==============================] - 0s 170us/step - loss: 5349.5133 - smape_kun2: 36.3158\n",
      "Epoch 399/500\n",
      "475/475 [==============================] - 0s 170us/step - loss: 5357.2645 - smape_kun2: 36.0812\n",
      "Epoch 400/500\n",
      "475/475 [==============================] - 0s 139us/step - loss: 5379.8247 - smape_kun2: 36.1473\n",
      "Epoch 401/500\n",
      "475/475 [==============================] - 0s 147us/step - loss: 5306.8036 - smape_kun2: 36.0253\n",
      "Epoch 402/500\n",
      "475/475 [==============================] - 0s 290us/step - loss: 5359.8918 - smape_kun2: 36.0265\n",
      "Epoch 403/500\n",
      "475/475 [==============================] - 0s 244us/step - loss: 5337.1738 - smape_kun2: 36.1113\n",
      "Epoch 404/500\n",
      "475/475 [==============================] - 0s 237us/step - loss: 5328.9656 - smape_kun2: 36.1131\n",
      "Epoch 405/500\n",
      "475/475 [==============================] - 0s 292us/step - loss: 5321.4881 - smape_kun2: 36.1307\n",
      "Epoch 406/500\n",
      "475/475 [==============================] - 0s 172us/step - loss: 5336.3375 - smape_kun2: 36.1833\n",
      "Epoch 407/500\n",
      "475/475 [==============================] - 0s 189us/step - loss: 5346.4779 - smape_kun2: 36.2114\n",
      "Epoch 408/500\n",
      "475/475 [==============================] - 0s 248us/step - loss: 5362.9017 - smape_kun2: 35.9757\n",
      "Epoch 409/500\n",
      "475/475 [==============================] - 0s 178us/step - loss: 5358.1874 - smape_kun2: 36.1029\n",
      "Epoch 410/500\n",
      "475/475 [==============================] - 0s 185us/step - loss: 5343.6404 - smape_kun2: 36.1056\n",
      "Epoch 411/500\n",
      "475/475 [==============================] - 0s 193us/step - loss: 5345.5125 - smape_kun2: 36.1495\n",
      "Epoch 412/500\n",
      "475/475 [==============================] - 0s 130us/step - loss: 5359.4325 - smape_kun2: 36.1567\n",
      "Epoch 413/500\n",
      "475/475 [==============================] - 0s 181us/step - loss: 5319.6273 - smape_kun2: 36.1059\n",
      "Epoch 414/500\n",
      "475/475 [==============================] - 0s 332us/step - loss: 5345.0340 - smape_kun2: 36.1271\n",
      "Epoch 415/500\n",
      "475/475 [==============================] - 0s 210us/step - loss: 5334.1389 - smape_kun2: 36.2057\n",
      "Epoch 416/500\n",
      "475/475 [==============================] - 0s 206us/step - loss: 5330.1800 - smape_kun2: 36.2485\n",
      "Epoch 417/500\n",
      "475/475 [==============================] - 0s 130us/step - loss: 5330.2989 - smape_kun2: 36.1257\n",
      "Epoch 418/500\n",
      "475/475 [==============================] - 0s 134us/step - loss: 5362.5737 - smape_kun2: 36.0655\n",
      "Epoch 419/500\n",
      "475/475 [==============================] - 0s 183us/step - loss: 5364.7645 - smape_kun2: 36.4326\n",
      "Epoch 420/500\n",
      "475/475 [==============================] - 0s 212us/step - loss: 5348.0740 - smape_kun2: 36.3973\n",
      "Epoch 421/500\n",
      "475/475 [==============================] - 0s 199us/step - loss: 5329.5100 - smape_kun2: 36.1204\n",
      "Epoch 422/500\n",
      "475/475 [==============================] - 0s 233us/step - loss: 5345.2658 - smape_kun2: 36.0381\n",
      "Epoch 423/500\n",
      "475/475 [==============================] - 0s 187us/step - loss: 5353.1296 - smape_kun2: 36.2862\n",
      "Epoch 424/500\n",
      "475/475 [==============================] - 0s 183us/step - loss: 5346.3408 - smape_kun2: 36.0861\n",
      "Epoch 425/500\n",
      "475/475 [==============================] - 0s 178us/step - loss: 5349.3607 - smape_kun2: 36.2074\n",
      "Epoch 426/500\n",
      "475/475 [==============================] - 0s 206us/step - loss: 5340.3496 - smape_kun2: 36.2247\n",
      "Epoch 427/500\n",
      "475/475 [==============================] - 0s 162us/step - loss: 5336.1658 - smape_kun2: 36.0836\n",
      "Epoch 428/500\n",
      "475/475 [==============================] - 0s 214us/step - loss: 5317.6027 - smape_kun2: 36.0672\n",
      "Epoch 429/500\n",
      "475/475 [==============================] - 0s 187us/step - loss: 5321.3043 - smape_kun2: 36.1559\n",
      "Epoch 430/500\n",
      "475/475 [==============================] - 0s 120us/step - loss: 5324.5088 - smape_kun2: 36.0839\n",
      "Epoch 431/500\n",
      "475/475 [==============================] - 0s 109us/step - loss: 5312.7430 - smape_kun2: 36.0697\n",
      "Epoch 432/500\n",
      "475/475 [==============================] - 0s 99us/step - loss: 5323.8841 - smape_kun2: 36.0738\n",
      "Epoch 433/500\n",
      "475/475 [==============================] - 0s 101us/step - loss: 5342.4236 - smape_kun2: 36.0269\n",
      "Epoch 434/500\n",
      "475/475 [==============================] - 0s 103us/step - loss: 5328.0928 - smape_kun2: 36.0683\n",
      "Epoch 435/500\n",
      "475/475 [==============================] - 0s 107us/step - loss: 5338.6731 - smape_kun2: 36.2631\n",
      "Epoch 436/500\n",
      "475/475 [==============================] - 0s 105us/step - loss: 5466.6376 - smape_kun2: 36.8124\n",
      "Epoch 437/500\n",
      "475/475 [==============================] - 0s 107us/step - loss: 5384.8742 - smape_kun2: 36.1681\n",
      "Epoch 438/500\n",
      "475/475 [==============================] - 0s 103us/step - loss: 5349.3016 - smape_kun2: 36.2343\n",
      "Epoch 439/500\n",
      "475/475 [==============================] - 0s 122us/step - loss: 5484.2797 - smape_kun2: 36.1562\n",
      "Epoch 440/500\n",
      "475/475 [==============================] - 0s 126us/step - loss: 5375.0232 - smape_kun2: 36.2552\n",
      "Epoch 441/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "475/475 [==============================] - 0s 111us/step - loss: 5377.0448 - smape_kun2: 36.1810\n",
      "Epoch 442/500\n",
      "475/475 [==============================] - 0s 92us/step - loss: 5356.5267 - smape_kun2: 36.1158\n",
      "Epoch 443/500\n",
      "475/475 [==============================] - 0s 120us/step - loss: 5363.3819 - smape_kun2: 36.2975\n",
      "Epoch 444/500\n",
      "475/475 [==============================] - 0s 103us/step - loss: 5318.9728 - smape_kun2: 36.0456\n",
      "Epoch 445/500\n",
      "475/475 [==============================] - 0s 84us/step - loss: 5345.1021 - smape_kun2: 36.0801\n",
      "Epoch 446/500\n",
      "475/475 [==============================] - 0s 94us/step - loss: 5349.0850 - smape_kun2: 36.3693\n",
      "Epoch 447/500\n",
      "475/475 [==============================] - 0s 105us/step - loss: 5343.7777 - smape_kun2: 36.0449\n",
      "Epoch 448/500\n",
      "475/475 [==============================] - 0s 176us/step - loss: 5336.7034 - smape_kun2: 36.0533\n",
      "Epoch 449/500\n",
      "475/475 [==============================] - 0s 189us/step - loss: 5318.1898 - smape_kun2: 36.1343\n",
      "Epoch 450/500\n",
      "475/475 [==============================] - 0s 115us/step - loss: 5323.5095 - smape_kun2: 36.0929\n",
      "Epoch 451/500\n",
      "475/475 [==============================] - 0s 124us/step - loss: 5347.2822 - smape_kun2: 36.2960\n",
      "Epoch 452/500\n",
      "475/475 [==============================] - 0s 113us/step - loss: 5358.0013 - smape_kun2: 36.1869\n",
      "Epoch 453/500\n",
      "475/475 [==============================] - 0s 103us/step - loss: 5408.3108 - smape_kun2: 36.4798\n",
      "Epoch 454/500\n",
      "475/475 [==============================] - 0s 124us/step - loss: 5324.1272 - smape_kun2: 36.2735\n",
      "Epoch 455/500\n",
      "475/475 [==============================] - 0s 99us/step - loss: 5332.2806 - smape_kun2: 36.0180\n",
      "Epoch 456/500\n",
      "475/475 [==============================] - 0s 90us/step - loss: 5336.2863 - smape_kun2: 35.9756\n",
      "Epoch 457/500\n",
      "475/475 [==============================] - 0s 86us/step - loss: 5338.9308 - smape_kun2: 36.1774\n",
      "Epoch 458/500\n",
      "475/475 [==============================] - 0s 69us/step - loss: 5378.2120 - smape_kun2: 35.9871\n",
      "Epoch 459/500\n",
      "475/475 [==============================] - 0s 88us/step - loss: 5431.9256 - smape_kun2: 36.2942\n",
      "Epoch 460/500\n",
      "475/475 [==============================] - 0s 66us/step - loss: 5338.9349 - smape_kun2: 36.1686\n",
      "Epoch 461/500\n",
      "475/475 [==============================] - 0s 124us/step - loss: 5352.7071 - smape_kun2: 36.1291\n",
      "Epoch 462/500\n",
      "475/475 [==============================] - 0s 88us/step - loss: 5341.9195 - smape_kun2: 36.2484\n",
      "Epoch 463/500\n",
      "475/475 [==============================] - 0s 86us/step - loss: 5328.1453 - smape_kun2: 36.1036\n",
      "Epoch 464/500\n",
      "475/475 [==============================] - 0s 94us/step - loss: 5334.9626 - smape_kun2: 36.1879\n",
      "Epoch 465/500\n",
      "475/475 [==============================] - 0s 122us/step - loss: 5330.9116 - smape_kun2: 36.2422\n",
      "Epoch 466/500\n",
      "475/475 [==============================] - 0s 206us/step - loss: 5343.0218 - smape_kun2: 36.2149\n",
      "Epoch 467/500\n",
      "475/475 [==============================] - 0s 128us/step - loss: 5386.8922 - smape_kun2: 36.0545\n",
      "Epoch 468/500\n",
      "475/475 [==============================] - 0s 115us/step - loss: 5359.4217 - smape_kun2: 36.2772\n",
      "Epoch 469/500\n",
      "475/475 [==============================] - 0s 120us/step - loss: 5345.7411 - smape_kun2: 36.2542\n",
      "Epoch 470/500\n",
      "475/475 [==============================] - 0s 109us/step - loss: 5340.6559 - smape_kun2: 36.1890\n",
      "Epoch 471/500\n",
      "475/475 [==============================] - 0s 67us/step - loss: 5349.9471 - smape_kun2: 36.1955\n",
      "Epoch 472/500\n",
      "475/475 [==============================] - 0s 134us/step - loss: 5343.8413 - smape_kun2: 36.1865\n",
      "Epoch 473/500\n",
      "475/475 [==============================] - 0s 99us/step - loss: 5331.0970 - smape_kun2: 36.2430\n",
      "Epoch 474/500\n",
      "475/475 [==============================] - 0s 90us/step - loss: 5333.1835 - smape_kun2: 36.1705\n",
      "Epoch 475/500\n",
      "475/475 [==============================] - 0s 97us/step - loss: 5343.6369 - smape_kun2: 36.1573\n",
      "Epoch 476/500\n",
      "475/475 [==============================] - 0s 101us/step - loss: 5335.1976 - smape_kun2: 36.0965\n",
      "Epoch 477/500\n",
      "475/475 [==============================] - 0s 94us/step - loss: 5331.1176 - smape_kun2: 36.1299\n",
      "Epoch 478/500\n",
      "475/475 [==============================] - 0s 90us/step - loss: 5337.2761 - smape_kun2: 36.0551\n",
      "Epoch 479/500\n",
      "475/475 [==============================] - 0s 92us/step - loss: 5363.2186 - smape_kun2: 36.1788\n",
      "Epoch 480/500\n",
      "475/475 [==============================] - 0s 103us/step - loss: 5325.2847 - smape_kun2: 36.0789\n",
      "Epoch 481/500\n",
      "475/475 [==============================] - 0s 88us/step - loss: 5346.2634 - smape_kun2: 36.0341\n",
      "Epoch 482/500\n",
      "475/475 [==============================] - 0s 90us/step - loss: 5323.0998 - smape_kun2: 36.1112\n",
      "Epoch 483/500\n",
      "475/475 [==============================] - 0s 86us/step - loss: 5349.9697 - smape_kun2: 36.2335\n",
      "Epoch 484/500\n",
      "475/475 [==============================] - 0s 90us/step - loss: 5356.2153 - smape_kun2: 36.0442\n",
      "Epoch 485/500\n",
      "475/475 [==============================] - 0s 86us/step - loss: 5340.1761 - smape_kun2: 36.2121\n",
      "Epoch 486/500\n",
      "475/475 [==============================] - 0s 94us/step - loss: 5327.2610 - smape_kun2: 36.2968\n",
      "Epoch 487/500\n",
      "475/475 [==============================] - 0s 84us/step - loss: 5319.4816 - smape_kun2: 36.1377\n",
      "Epoch 488/500\n",
      "475/475 [==============================] - 0s 105us/step - loss: 5347.2216 - smape_kun2: 36.1244\n",
      "Epoch 489/500\n",
      "475/475 [==============================] - 0s 86us/step - loss: 5336.1449 - smape_kun2: 36.1490\n",
      "Epoch 490/500\n",
      "475/475 [==============================] - 0s 83us/step - loss: 5341.2647 - smape_kun2: 36.1527\n",
      "Epoch 491/500\n",
      "475/475 [==============================] - 0s 66us/step - loss: 5330.8299 - smape_kun2: 36.2821\n",
      "Epoch 492/500\n",
      "475/475 [==============================] - 0s 79us/step - loss: 5356.5780 - smape_kun2: 36.2123\n",
      "Epoch 493/500\n",
      "475/475 [==============================] - 0s 66us/step - loss: 5347.5576 - smape_kun2: 36.0939\n",
      "Epoch 494/500\n",
      "475/475 [==============================] - 0s 66us/step - loss: 5338.2573 - smape_kun2: 36.2533\n",
      "Epoch 495/500\n",
      "475/475 [==============================] - 0s 80us/step - loss: 5329.8580 - smape_kun2: 36.1253\n",
      "Epoch 496/500\n",
      "475/475 [==============================] - 0s 101us/step - loss: 5315.3754 - smape_kun2: 36.1528\n",
      "Epoch 497/500\n",
      "475/475 [==============================] - 0s 78us/step - loss: 5333.2289 - smape_kun2: 36.1969\n",
      "Epoch 498/500\n",
      "475/475 [==============================] - 0s 78us/step - loss: 5351.5919 - smape_kun2: 36.1773\n",
      "Epoch 499/500\n",
      "475/475 [==============================] - 0s 191us/step - loss: 5388.0360 - smape_kun2: 36.1219\n",
      "Epoch 500/500\n",
      "475/475 [==============================] - 0s 122us/step - loss: 5357.7534 - smape_kun2: 36.2610\n"
     ]
    }
   ],
   "source": [
    "from keras import Sequential\n",
    "from keras.layers import Dense\n",
    "def build_regressor():\n",
    "    regressor = Sequential()\n",
    "    regressor.add(Dense(units=60, input_dim=30))\n",
    "    regressor.add(Dense(units=80))\n",
    "    regressor.add(Dense(units=1))\n",
    "    regressor.compile(optimizer='adam', loss='mean_squared_error',  metrics=[smape_kun2])\n",
    "    return regressor\n",
    "\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "regressor = KerasRegressor(build_fn=build_regressor, batch_size=64,epochs=500)\n",
    "\n",
    "results=regressor.fit(X_Train,Y_Train)\n",
    "\n",
    "preds = regressor.predict(X_Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[890.8737   276.46103  596.31995  967.76605  444.57666  368.0778\n",
      " 908.9635   254.65173  760.87427  210.63638  -42.37513   48.488903\n",
      " 678.8518   359.79123  754.20416  835.45465  257.5565   132.3937\n",
      " 260.53668  133.7095   947.7647   232.34094  186.837    155.31703\n",
      " 138.75868 ]\n",
      "26.052232887845776\n"
     ]
    }
   ],
   "source": [
    "#preds = twoone(preds)\n",
    "print(preds)\n",
    "print(smape_kun(Y_Test,preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4) PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import svm\n",
    "from sklearn import tree\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2,svd_solver='full')\n",
    "X_pca = pca.fit_transform(X)\n",
    "# print(pca.explained_variance_)\n",
    "\n",
    "X_reduced, X_test_reduced, Y_Train, Y_Test = train_test_split(X_pca, Y, test_size = 0.05, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40.09596894778119\n",
      "[808.00692392 168.07008994 655.99543112 868.81104253 534.1630428\n",
      " 249.16776434 825.62297657 329.74390178 722.22092148 186.66001476\n",
      "  60.02655008 123.26864696 672.00425871 204.35558482 719.59177543\n",
      " 751.34258603 135.16065898 263.91702929 127.47422573 171.28422122\n",
      " 838.67664428 287.51104222 125.58149886 215.24300264 238.27416895]\n"
     ]
    }
   ],
   "source": [
    "# https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html\n",
    "from sklearn.linear_model import LinearRegression\n",
    "reg = LinearRegression().fit(X_reduced,Y_Train)\n",
    "\n",
    "y_pred = reg.predict(X_test_reduced)\n",
    "\n",
    "print(smape_kun(Y_Test,y_pred))\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.89290866285403\n"
     ]
    }
   ],
   "source": [
    "# https://www.kaggle.com/nickycan/much-faster-smape-metric-function\n",
    "# https://stackoverflow.com/questions/51444630/how-to-use-smape-evaluation-metric-on-train-dataset\n",
    "# https://stackoverflow.com/questions/41925157/logisticregression-unknown-label-type-continuous-using-sklearn-in-python\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model = RandomForestRegressor(n_jobs=-100000000000000000000000000000000000000000000000000000000000000000000000)\n",
    "\n",
    "model.fit(X_Train,Y_Train)\n",
    "\n",
    "y_pred = model.predict(X_Test)\n",
    "\n",
    "print(smape_kun(Y_Test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 858.10393263  338.35985324  593.78704778  980.26435107  395.15560108\n",
      "  322.48834466  852.3925124   282.36088116  707.46396514  236.61318795\n",
      "    0.          -29.68706276  607.4751719   303.83908373  781.92738793\n",
      "  800.64062639  307.42732284  215.41203314  242.88912554   90.35830554\n",
      " 1045.57883515  170.22135781  184.55509373  177.8287852   196.20445605]\n"
     ]
    }
   ],
   "source": [
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5) Reduced Dataset Using Main Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2095,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from itertools import product\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X = df[['x23','x27','x1']].values\n",
    "Y = df['y'].fillna(0)\n",
    "\n",
    "X = StandardScaler().fit_transform(X)\n",
    "\n",
    "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size = 0.05, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.947119659857407\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model = RandomForestRegressor(n_jobs=-100000000000000000000000000000000000000000000000000000000000000000000000)\n",
    "\n",
    "model.fit(X_Train,Y_Train)\n",
    "\n",
    "y_pred = model.predict(X_Test)\n",
    "\n",
    "print(smape_kun(Y_Test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[973.35741739 290.7526603  550.16044726 965.88266561 389.49491833\n",
      " 326.10931005 796.04043019 226.79088581 757.97755271 248.23066973\n",
      "   0.         -35.35359376 625.74974575 332.37854465 819.4811063\n",
      " 856.29751248 292.83895775 226.67837731 261.63100507  59.87685686\n",
      " 980.00510116 203.11722888 135.09892497 173.76461476 195.18597236]\n"
     ]
    }
   ],
   "source": [
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6) Reduced Dataset Using Main Features (amplified)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from itertools import product\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X = df[['x23','x27','x1','x30','x25']].values\n",
    "Y = df['y'].fillna(0)\n",
    "\n",
    "X = StandardScaler().fit_transform(X)\n",
    "\n",
    "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size = 0.05, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2788,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.01631533302832\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model = RandomForestRegressor(n_jobs=-100000000000000000000000000000000000000000000000000000000000000000000000)\n",
    "\n",
    "model.fit(X_Train,Y_Train)\n",
    "\n",
    "y_pred = model.predict(X_Test)\n",
    "\n",
    "print(smape_kun(Y_Test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2789,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1004.49299284  296.67215509  606.92203677  930.80696417  389.45011453\n",
      "  322.79420456  797.41376627  224.13645043  776.42725742  224.60236181\n",
      "    0.          -28.86249122  607.64636444  312.73809626  768.1643219\n",
      "  832.00251452  342.38202229  185.27106599  217.03195252   81.4940105\n",
      " 1010.36273526  189.87525196  167.49650988  170.41302696  237.07337841]\n"
     ]
    }
   ],
   "source": [
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23ae848d400>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = [1004.49299284,  296.67215509,  606.92203677,  930.80696417,  389.45011453,\n",
    "              322.79420456,  797.41376627,  224.13645043,  776.42725742,  224.60236181,\n",
    "                0.,          -28.86249122,  607.64636444,  312.73809626,  768.1643219,\n",
    "              832.00251452,  342.38202229,  185.27106599,  217.03195252,   81.4940105,\n",
    "             1010.36273526,  189.87525196,  167.49650988,  170.41302696,  237.07337841]\n",
    "\n",
    "plt.figure(figsize=(12,7))\n",
    "# plt.plot(df['Open'], 'green', color='blue', label='Training Data')\n",
    "plt.plot(test_data.index, predictions, color='green', marker='o', linestyle='dashed', \n",
    "         label='Predicted Price')\n",
    "plt.plot(df['y'], 'blue', label='Series output')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2599,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46.56804321077557\n",
      "[819.75728623 156.31577423 585.4534609  773.7382153  469.04389869\n",
      " 216.02018993 778.59398493 374.52788425 642.70970511  69.50485025\n",
      "  71.26889868  16.69786567 620.97426679 181.84609393 664.33560279\n",
      " 746.48143099 162.70711136 376.33849894 237.81512018  64.85932864\n",
      " 809.79270624 315.69572433 307.93111503 370.78816388 370.88070697]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg = LinearRegression().fit(X_Train,Y_Train)\n",
    "\n",
    "y_pred = reg.predict(X_Test)\n",
    "\n",
    "print(smape_kun(Y_Test,y_pred))\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7) Deep Learning (new approach)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2806,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "475/475 [==============================] - 11s 24ms/step - loss: 203049.2304 - smape_kun2: 195.7171\n",
      "Epoch 2/8\n",
      "475/475 [==============================] - 0s 210us/step - loss: 186720.7950 - smape_kun2: 181.7566\n",
      "Epoch 3/8\n",
      "475/475 [==============================] - 0s 229us/step - loss: 149973.8881 - smape_kun2: 146.1624\n",
      "Epoch 4/8\n",
      "475/475 [==============================] - 0s 216us/step - loss: 122084.5621 - smape_kun2: 125.3523\n",
      "Epoch 5/8\n",
      "475/475 [==============================] - 0s 233us/step - loss: 102739.6822 - smape_kun2: 124.3061\n",
      "Epoch 6/8\n",
      "475/475 [==============================] - 0s 231us/step - loss: 72982.2772 - smape_kun2: 107.5489\n",
      "Epoch 7/8\n",
      "475/475 [==============================] - 0s 267us/step - loss: 37188.7445 - smape_kun2: 76.9594\n",
      "Epoch 8/8\n",
      "475/475 [==============================] - 0s 301us/step - loss: 23492.4762 - smape_kun2: 54.0372\n"
     ]
    }
   ],
   "source": [
    "from keras import Sequential\n",
    "from keras.layers import Dense\n",
    "def build_regressor():\n",
    "    regressor = Sequential()\n",
    "    regressor.add(Dense(units=20, input_dim=5))\n",
    "    regressor.add(Dense(units=80))\n",
    "    regressor.add(Dense(units=40))\n",
    "    regressor.add(Dense(units=10))\n",
    "    regressor.add(Dense(units=80))\n",
    "    regressor.add(Dense(units=40))\n",
    "    regressor.add(Dense(units=1))\n",
    "    regressor.compile(optimizer='adam', loss='mean_squared_error',  metrics=[smape_kun2])\n",
    "    return regressor\n",
    "\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "regressor = KerasRegressor(build_fn=build_regressor, batch_size=32,epochs=8)\n",
    "\n",
    "results=regressor.fit(X_Train,Y_Train)\n",
    "\n",
    "preds = regressor.predict(X_Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2807,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[710.4563   215.78812  593.3473   727.5604   501.24008  223.35695\n",
      " 665.9081   438.1447   586.57715  140.50111   44.925117  50.298145\n",
      " 625.37634  234.62103  616.1633   640.2709   213.27249  397.76004\n",
      " 299.56528  108.69307  726.1779   444.77487  304.3918   357.29996\n",
      " 379.29443 ]\n",
      "47.17531720243735\n"
     ]
    }
   ],
   "source": [
    "#preds = twoone(preds)\n",
    "print(preds)\n",
    "print(smape_kun(Y_Test,preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of input data:\n",
      "(497, 124)\n",
      "Training set shape for X (inputs):\n",
      "(472, 123)\n",
      "Training set shape for Y (output):\n",
      "(472,)\n",
      "(472, 123, 1)\n",
      "(25, 123, 1)\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_91 (LSTM)               (None, 123, 3)            60        \n",
      "_________________________________________________________________\n",
      "dropout_77 (Dropout)         (None, 123, 3)            0         \n",
      "_________________________________________________________________\n",
      "lstm_92 (LSTM)               (None, 256)               266240    \n",
      "_________________________________________________________________\n",
      "dropout_78 (Dropout)         (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_84 (Dense)             (None, 1)                 257       \n",
      "_________________________________________________________________\n",
      "activation_39 (Activation)   (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 266,557\n",
      "Trainable params: 266,557\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 424 samples, validate on 48 samples\n",
      "Epoch 1/100\n",
      "424/424 [==============================] - 22s 52ms/step - loss: 196.7672 - val_loss: 181.2472\n",
      "Epoch 2/100\n",
      "424/424 [==============================] - 6s 14ms/step - loss: 182.9561 - val_loss: 171.3772\n",
      "Epoch 3/100\n",
      "424/424 [==============================] - 6s 14ms/step - loss: 171.7416 - val_loss: 157.6683\n",
      "Epoch 4/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 154.9986 - val_loss: 136.6821\n",
      "Epoch 5/100\n",
      "424/424 [==============================] - 6s 15ms/step - loss: 132.8697 - val_loss: 102.3504\n",
      "Epoch 6/100\n",
      "424/424 [==============================] - 7s 15ms/step - loss: 100.8569 - val_loss: 154.3274\n",
      "Epoch 7/100\n",
      "424/424 [==============================] - 6s 15ms/step - loss: 154.8047 - val_loss: 91.0977\n",
      "Epoch 8/100\n",
      "424/424 [==============================] - 6s 15ms/step - loss: 100.4826 - val_loss: 85.5094\n",
      "Epoch 9/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 88.2387 - val_loss: 95.2731\n",
      "Epoch 10/100\n",
      "424/424 [==============================] - 7s 17ms/step - loss: 94.2712 - val_loss: 99.3287\n",
      "Epoch 11/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 98.6190 - val_loss: 100.3767\n",
      "Epoch 12/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 99.0197 - val_loss: 99.6721\n",
      "Epoch 13/100\n",
      "424/424 [==============================] - 7s 17ms/step - loss: 98.2685 - val_loss: 97.8437\n",
      "Epoch 14/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 96.5077 - val_loss: 95.1525\n",
      "Epoch 15/100\n",
      "424/424 [==============================] - 8s 19ms/step - loss: 94.0423 - val_loss: 91.7735\n",
      "Epoch 16/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 91.3501 - val_loss: 87.7617\n",
      "Epoch 17/100\n",
      "424/424 [==============================] - 6s 15ms/step - loss: 88.4748 - val_loss: 83.9422\n",
      "Epoch 18/100\n",
      "424/424 [==============================] - 7s 15ms/step - loss: 86.3650 - val_loss: 80.2093\n",
      "Epoch 19/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 84.0166 - val_loss: 77.2433\n",
      "Epoch 20/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 82.9946 - val_loss: 75.3853\n",
      "Epoch 21/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 84.0637 - val_loss: 75.2517\n",
      "Epoch 22/100\n",
      "424/424 [==============================] - 6s 15ms/step - loss: 84.7922 - val_loss: 75.7612\n",
      "Epoch 23/100\n",
      "424/424 [==============================] - 6s 14ms/step - loss: 86.3470 - val_loss: 75.7691\n",
      "Epoch 24/100\n",
      "424/424 [==============================] - 8s 19ms/step - loss: 86.1426 - val_loss: 75.2548\n",
      "Epoch 25/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 85.0906 - val_loss: 74.6867\n",
      "Epoch 26/100\n",
      "424/424 [==============================] - 7s 18ms/step - loss: 84.5048 - val_loss: 74.8864\n",
      "Epoch 27/100\n",
      "424/424 [==============================] - 7s 17ms/step - loss: 82.9599 - val_loss: 75.6900\n",
      "Epoch 28/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 82.8440 - val_loss: 76.6169\n",
      "Epoch 29/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 82.2312 - val_loss: 77.4726\n",
      "Epoch 30/100\n",
      "424/424 [==============================] - 7s 15ms/step - loss: 82.0806 - val_loss: 78.4233\n",
      "Epoch 31/100\n",
      "424/424 [==============================] - 6s 15ms/step - loss: 83.1273 - val_loss: 79.1351\n",
      "Epoch 32/100\n",
      "424/424 [==============================] - 6s 15ms/step - loss: 82.9800 - val_loss: 79.6366\n",
      "Epoch 33/100\n",
      "424/424 [==============================] - 6s 15ms/step - loss: 83.6304 - val_loss: 79.8158\n",
      "Epoch 34/100\n",
      "424/424 [==============================] - 7s 17ms/step - loss: 83.7712 - val_loss: 79.7507\n",
      "Epoch 35/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 83.2507 - val_loss: 79.4105\n",
      "Epoch 36/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 83.1496 - val_loss: 78.8301\n",
      "Epoch 37/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 82.6612 - val_loss: 78.0922\n",
      "Epoch 38/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 82.5410 - val_loss: 77.2827\n",
      "Epoch 39/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 81.8106 - val_loss: 76.5132\n",
      "Epoch 40/100\n",
      "424/424 [==============================] - 6s 15ms/step - loss: 81.6108 - val_loss: 75.8454\n",
      "Epoch 41/100\n",
      "424/424 [==============================] - 6s 15ms/step - loss: 81.1823 - val_loss: 75.2927\n",
      "Epoch 42/100\n",
      "424/424 [==============================] - 6s 15ms/step - loss: 81.5965 - val_loss: 74.7386\n",
      "Epoch 43/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 82.3389 - val_loss: 74.3896\n",
      "Epoch 44/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 81.9079 - val_loss: 74.1817\n",
      "Epoch 45/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 81.7974 - val_loss: 74.0597\n",
      "Epoch 46/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 81.9157 - val_loss: 73.9990\n",
      "Epoch 47/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 82.0212 - val_loss: 74.0260\n",
      "Epoch 48/100\n",
      "424/424 [==============================] - 6s 15ms/step - loss: 81.7516 - val_loss: 74.1188\n",
      "Epoch 49/100\n",
      "424/424 [==============================] - 6s 15ms/step - loss: 81.6870 - val_loss: 74.2931\n",
      "Epoch 50/100\n",
      "424/424 [==============================] - 6s 14ms/step - loss: 81.6389 - val_loss: 74.4749\n",
      "Epoch 51/100\n",
      "424/424 [==============================] - 6s 15ms/step - loss: 81.5708 - val_loss: 74.7213\n",
      "Epoch 52/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 81.2570 - val_loss: 74.9715\n",
      "Epoch 53/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 81.0896 - val_loss: 75.2046\n",
      "Epoch 54/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 81.0615 - val_loss: 75.4595\n",
      "Epoch 55/100\n",
      "424/424 [==============================] - 7s 17ms/step - loss: 81.5871 - val_loss: 75.5774\n",
      "Epoch 56/100\n",
      "424/424 [==============================] - 6s 15ms/step - loss: 81.1563 - val_loss: 75.5469\n",
      "Epoch 57/100\n",
      "424/424 [==============================] - 6s 14ms/step - loss: 81.0020 - val_loss: 75.4159\n",
      "Epoch 58/100\n",
      "424/424 [==============================] - 6s 14ms/step - loss: 81.0649 - val_loss: 75.0983\n",
      "Epoch 59/100\n",
      "424/424 [==============================] - 8s 20ms/step - loss: 81.0781 - val_loss: 74.6346\n",
      "Epoch 60/100\n",
      "424/424 [==============================] - 10s 24ms/step - loss: 80.6418 - val_loss: 74.2112\n",
      "Epoch 61/100\n",
      "424/424 [==============================] - 11s 26ms/step - loss: 81.0066 - val_loss: 73.8833\n",
      "Epoch 62/100\n",
      "424/424 [==============================] - 9s 22ms/step - loss: 80.3579 - val_loss: 73.5490\n",
      "Epoch 63/100\n",
      "424/424 [==============================] - 9s 22ms/step - loss: 80.7598 - val_loss: 73.1913\n",
      "Epoch 64/100\n",
      "424/424 [==============================] - 9s 20ms/step - loss: 80.9440 - val_loss: 72.9367\n",
      "Epoch 65/100\n",
      "424/424 [==============================] - 9s 21ms/step - loss: 80.3054 - val_loss: 72.8065\n",
      "Epoch 66/100\n",
      "424/424 [==============================] - 8s 18ms/step - loss: 80.7451 - val_loss: 72.7416\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 67/100\n",
      "424/424 [==============================] - 8s 20ms/step - loss: 80.0644 - val_loss: 72.6984\n",
      "Epoch 68/100\n",
      "424/424 [==============================] - 8s 20ms/step - loss: 80.4553 - val_loss: 72.7068\n",
      "Epoch 69/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 80.2698 - val_loss: 72.7497\n",
      "Epoch 70/100\n",
      "424/424 [==============================] - 8s 18ms/step - loss: 80.2121 - val_loss: 72.7964\n",
      "Epoch 71/100\n",
      "424/424 [==============================] - 8s 18ms/step - loss: 80.0919 - val_loss: 72.7845\n",
      "Epoch 72/100\n",
      "424/424 [==============================] - 8s 20ms/step - loss: 79.8311 - val_loss: 72.7789\n",
      "Epoch 73/100\n",
      "424/424 [==============================] - 7s 17ms/step - loss: 80.0159 - val_loss: 72.7410\n",
      "Epoch 74/100\n",
      "424/424 [==============================] - 8s 20ms/step - loss: 80.0130 - val_loss: 72.6863\n",
      "Epoch 75/100\n",
      "424/424 [==============================] - 10s 23ms/step - loss: 79.9028 - val_loss: 72.5806\n",
      "Epoch 76/100\n",
      "424/424 [==============================] - 8s 19ms/step - loss: 79.5459 - val_loss: 72.4304\n",
      "Epoch 77/100\n",
      "424/424 [==============================] - 8s 18ms/step - loss: 79.5396 - val_loss: 72.1787\n",
      "Epoch 78/100\n",
      "424/424 [==============================] - 8s 18ms/step - loss: 79.7059 - val_loss: 71.8184\n",
      "Epoch 79/100\n",
      "424/424 [==============================] - 7s 17ms/step - loss: 79.8166 - val_loss: 71.4692\n",
      "Epoch 80/100\n",
      "424/424 [==============================] - 7s 17ms/step - loss: 79.1595 - val_loss: 71.2237\n",
      "Epoch 81/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 79.4883 - val_loss: 71.0191\n",
      "Epoch 82/100\n",
      "424/424 [==============================] - 7s 17ms/step - loss: 78.9734 - val_loss: 70.8792\n",
      "Epoch 83/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 79.5937 - val_loss: 70.7286\n",
      "Epoch 84/100\n",
      "424/424 [==============================] - 7s 17ms/step - loss: 79.0744 - val_loss: 70.5363\n",
      "Epoch 85/100\n",
      "424/424 [==============================] - 7s 17ms/step - loss: 79.4009 - val_loss: 70.4570\n",
      "Epoch 86/100\n",
      "424/424 [==============================] - 7s 17ms/step - loss: 79.7576 - val_loss: 70.4112\n",
      "Epoch 87/100\n",
      "424/424 [==============================] - 7s 17ms/step - loss: 79.3233 - val_loss: 70.4597\n",
      "Epoch 88/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 78.4216 - val_loss: 70.5156\n",
      "Epoch 89/100\n",
      "424/424 [==============================] - 7s 15ms/step - loss: 79.0878 - val_loss: 70.5014\n",
      "Epoch 90/100\n",
      "424/424 [==============================] - 7s 18ms/step - loss: 79.1670 - val_loss: 70.3695\n",
      "Epoch 91/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 78.3487 - val_loss: 70.3423\n",
      "Epoch 92/100\n",
      "424/424 [==============================] - 8s 18ms/step - loss: 78.0766 - val_loss: 70.3293\n",
      "Epoch 93/100\n",
      "424/424 [==============================] - 8s 20ms/step - loss: 78.5588 - val_loss: 70.3605\n",
      "Epoch 94/100\n",
      "424/424 [==============================] - 8s 19ms/step - loss: 78.3493 - val_loss: 70.3709\n",
      "Epoch 95/100\n",
      "424/424 [==============================] - 10s 23ms/step - loss: 78.4026 - val_loss: 70.3458\n",
      "Epoch 96/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 78.4194 - val_loss: 70.3177\n",
      "Epoch 97/100\n",
      "424/424 [==============================] - 7s 15ms/step - loss: 78.8779 - val_loss: 70.3499\n",
      "Epoch 98/100\n",
      "424/424 [==============================] - 6s 15ms/step - loss: 78.8018 - val_loss: 70.2802\n",
      "Epoch 99/100\n",
      "424/424 [==============================] - 7s 17ms/step - loss: 78.9087 - val_loss: 70.1481\n",
      "Epoch 100/100\n",
      "424/424 [==============================] - 7s 16ms/step - loss: 78.2791 - val_loss: 70.0581\n"
     ]
    }
   ],
   "source": [
    "from pandas import DataFrame\n",
    "from pandas import Series\n",
    "from pandas import concat\n",
    "from pandas import read_csv\n",
    "from pandas import datetime\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from math import sqrt\n",
    "from matplotlib import pyplot\n",
    "import numpy\n",
    "from sklearn.utils import shuffle\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    " \n",
    "df = df.fillna(0)\n",
    "# Normalize data on the (-1, 1) interval.\n",
    "scaler = MinMaxScaler(feature_range = (-1, 1))\n",
    "scaled = scaler.fit_transform(df.drop(['Unnamed: 0'], axis = 1).values)\n",
    "y = df['y'].values.astype(\"float32\").reshape(-1, 1)\n",
    "y = scaler.fit_transform(y)\n",
    "\n",
    "# Convert to data frame.\n",
    "series = pd.DataFrame(scaled)\n",
    "\n",
    "# Helper function to create a windowed data set.\n",
    "# FIXME: Copying & overwriting is flawed!\n",
    "def create_window(data, window_size = 1):    \n",
    "    data_s = data.copy()\n",
    "    for i in range(window_size):\n",
    "        data = pd.concat([data, data_s.shift(-(i + 1))], \n",
    "                            axis = 1)\n",
    "        \n",
    "    data.dropna(axis=0, inplace=True)\n",
    "    return(data)\n",
    "\n",
    "# FIXME: We'll use this only for demonstration purposes.\n",
    "series_backup = series.copy()\n",
    "t = create_window(series_backup, 1)\n",
    "t.head()\n",
    "\n",
    "window_size = 3\n",
    "series = create_window(series, window_size)\n",
    "print(\"Shape of input data:\")\n",
    "print(series.shape)\n",
    "\n",
    "# Using 80% of data for training, 20% for validation.\n",
    "# FIXME: Need to align with example 1.\n",
    "TRAIN_SIZE = 0.95\n",
    "\n",
    "nrow = round(TRAIN_SIZE * series.shape[0])\n",
    "\n",
    "train = series.iloc[:nrow, :]\n",
    "test = series.iloc[nrow:, :]\n",
    "\n",
    "# Shuffle training data.\n",
    "train = shuffle(train)\n",
    "\n",
    "train_X = train.iloc[:, :-1]\n",
    "test_X = test.iloc[:, :-1]\n",
    "\n",
    "train_Y = train.iloc[:, -1]\n",
    "test_Y = test.iloc[:, -1]\n",
    "#print(test_Y)\n",
    "\n",
    "print(\"Training set shape for X (inputs):\")\n",
    "print(train_X.shape)\n",
    "print(\"Training set shape for Y (output):\")\n",
    "print(train_Y.shape)\n",
    "\n",
    "train_X = np.reshape(train_X.values, (train_X.shape[0], train_X.shape[1], 1))\n",
    "test_X = np.reshape(test_X.values, (test_X.shape[0], test_X.shape[1], 1))\n",
    "\n",
    "print(train_X.shape)\n",
    "print(test_X.shape)\n",
    "\n",
    "# Define the model.\n",
    "model2 = Sequential()\n",
    "model2.add(LSTM(input_shape = (123, 1), \n",
    "               units = window_size, \n",
    "               return_sequences = True))\n",
    "model2.add(Dropout(0.5))\n",
    "model2.add(LSTM(256))\n",
    "model2.add(Dropout(0.5))\n",
    "model2.add(Dense(1))\n",
    "model2.add(Activation(\"linear\"))\n",
    "model2.compile(loss = smape_kun2, \n",
    "              optimizer = \"adam\")\n",
    "model2.summary()\n",
    "\n",
    "# Fit the model.\n",
    "model2.fit(train_X, \n",
    "          train_Y, \n",
    "          batch_size = 512,\n",
    "          epochs = 100,\n",
    "          validation_split = 0.1)\n",
    "\n",
    "# Predict on test data.\n",
    "pred_test = model2.predict(test_X)\n",
    "\n",
    "\n",
    "#print('2',test_Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25, 1)\n",
      "MSE for predicted test set: 0.480295\n"
     ]
    }
   ],
   "source": [
    "# Apply inverse transformation to get back true values.\n",
    "o = np.reshape(test_Y.values,(-1, 1))\n",
    "print(o.shape)\n",
    "test_y_actual = scaler.inverse_transform(o)\n",
    "\n",
    "print(\"MSE for predicted test set: %2f\" % mean_squared_error(test_y_actual, pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.61111957]\n",
      " [-0.72331846]\n",
      " [-0.72034   ]\n",
      " [-0.6269158 ]\n",
      " [-0.6582618 ]\n",
      " [-0.64835536]\n",
      " [-0.7090759 ]\n",
      " [-0.673497  ]\n",
      " [-0.70071363]\n",
      " [-0.7666764 ]\n",
      " [-0.6609383 ]\n",
      " [-0.7463302 ]\n",
      " [-0.6972548 ]\n",
      " [-0.6859517 ]\n",
      " [-0.61553586]\n",
      " [-0.6359142 ]\n",
      " [-0.7767145 ]\n",
      " [-0.55652344]\n",
      " [-0.72518015]\n",
      " [-0.6811406 ]\n",
      " [-0.7377026 ]\n",
      " [-0.68439806]\n",
      " [-0.71651304]\n",
      " [-0.72356   ]\n",
      " [-0.7897708 ]]\n",
      "200.0\n"
     ]
    }
   ],
   "source": [
    "#preds = twoone(preds)\n",
    "print(pred_test)\n",
    "\n",
    "# def twoone(list1):\n",
    "#     return [val for lst in list1 for val in lst]\n",
    "\n",
    "# pred_test = twoone(pred_test)\n",
    "# print(smape_kun2(test_y_actual, pred_test))\n",
    "Y_Test = K.variable(Y_Test)\n",
    "pred_test = K.variable(pred_test)\n",
    "\n",
    "print(K.eval(smape_kun2(Y_Test, pred_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of entries (training set, test set): (475, 25)\n"
     ]
    }
   ],
   "source": [
    "# Get the raw data values from the pandas data frame.\n",
    "df = df.fillna(0)\n",
    "data_raw = (df.drop(['Unnamed: 0'], axis = 1)).values.astype(\"float32\")\n",
    "y = df['y'].values.astype(\"float32\").reshape(-1, 1)\n",
    "y = scaler.fit_transform(y)\n",
    "\n",
    "# We apply the MinMax scaler from sklearn\n",
    "# to normalize data in the (0, 1) interval.\n",
    "scaler = MinMaxScaler(feature_range = (0, 1))\n",
    "dataset = scaler.fit_transform(data_raw)\n",
    "\n",
    "# Print a few values.\n",
    "dataset[0:5]\n",
    "\n",
    "TRAIN_SIZE = 0.95\n",
    "\n",
    "train_size = int(len(dataset) * TRAIN_SIZE)\n",
    "test_size = len(dataset) - train_size\n",
    "train, test = dataset[0:train_size, :], dataset[train_size:len(dataset), :]\n",
    "print(\"Number of entries (training set, test set): \" + str((len(train), len(test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FIXME: This helper function should be rewritten using numpy's shift function. See below.\n",
    "def create_dataset(dataset, window_size = 1):\n",
    "    data_X, data_Y = [], []\n",
    "    for i in range(len(dataset) - window_size - 1):\n",
    "        a = dataset[i:(i + window_size), 0]\n",
    "        data_X.append(a)\n",
    "        data_Y.append(dataset[i + window_size, 0])\n",
    "    return(np.array(data_X), np.array(data_Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original training data shape:\n",
      "(473, 1)\n",
      "New training data shape:\n",
      "(473, 1, 1)\n",
      "Epoch 1/100\n",
      " - 16s - loss: 55.0756\n",
      "Epoch 2/100\n",
      " - 2s - loss: 26.1142\n",
      "Epoch 3/100\n",
      " - 2s - loss: 25.9600\n",
      "Epoch 4/100\n",
      " - 2s - loss: 25.7724\n",
      "Epoch 5/100\n",
      " - 2s - loss: 25.6087\n",
      "Epoch 6/100\n",
      " - 2s - loss: 25.4123\n",
      "Epoch 7/100\n",
      " - 2s - loss: 25.4521\n",
      "Epoch 8/100\n",
      " - 2s - loss: 25.2616\n",
      "Epoch 9/100\n",
      " - 2s - loss: 25.2138\n",
      "Epoch 10/100\n",
      " - 1s - loss: 25.1534\n",
      "Epoch 11/100\n",
      " - 1s - loss: 25.2288\n",
      "Epoch 12/100\n",
      " - 1s - loss: 25.2183\n",
      "Epoch 13/100\n",
      " - 1s - loss: 25.1427\n",
      "Epoch 14/100\n",
      " - 1s - loss: 25.0938\n",
      "Epoch 15/100\n",
      " - 1s - loss: 25.0517\n",
      "Epoch 16/100\n",
      " - 1s - loss: 25.1864\n",
      "Epoch 17/100\n",
      " - 1s - loss: 25.0677\n",
      "Epoch 18/100\n",
      " - 1s - loss: 25.0811\n",
      "Epoch 19/100\n",
      " - 1s - loss: 25.1333\n",
      "Epoch 20/100\n",
      " - 1s - loss: 25.1060\n",
      "Epoch 21/100\n",
      " - 2s - loss: 25.0837\n",
      "Epoch 22/100\n",
      " - 1s - loss: 25.0206\n",
      "Epoch 23/100\n",
      " - 1s - loss: 25.1240\n",
      "Epoch 24/100\n",
      " - 1s - loss: 25.0657\n",
      "Epoch 25/100\n",
      " - 1s - loss: 25.0347\n",
      "Epoch 26/100\n",
      " - 1s - loss: 25.1397\n",
      "Epoch 27/100\n",
      " - 1s - loss: 25.0357\n",
      "Epoch 28/100\n",
      " - 1s - loss: 25.1069\n",
      "Epoch 29/100\n",
      " - 1s - loss: 25.0039\n",
      "Epoch 30/100\n",
      " - 1s - loss: 25.0392\n",
      "Epoch 31/100\n",
      " - 1s - loss: 25.1024\n",
      "Epoch 32/100\n",
      " - 2s - loss: 25.0854\n",
      "Epoch 33/100\n",
      " - 1s - loss: 25.1123\n",
      "Epoch 34/100\n",
      " - 1s - loss: 25.0905\n",
      "Epoch 35/100\n",
      " - 1s - loss: 25.0203\n",
      "Epoch 36/100\n",
      " - 1s - loss: 25.1244\n",
      "Epoch 37/100\n",
      " - 1s - loss: 25.0053\n",
      "Epoch 38/100\n",
      " - 1s - loss: 24.9940\n",
      "Epoch 39/100\n",
      " - 1s - loss: 25.1458\n",
      "Epoch 40/100\n",
      " - 1s - loss: 25.0618\n",
      "Epoch 41/100\n",
      " - 1s - loss: 25.0504\n",
      "Epoch 42/100\n",
      " - 1s - loss: 25.1427\n",
      "Epoch 43/100\n",
      " - 1s - loss: 25.0401\n",
      "Epoch 44/100\n",
      " - 1s - loss: 25.0968\n",
      "Epoch 45/100\n",
      " - 1s - loss: 25.0223\n",
      "Epoch 46/100\n",
      " - 1s - loss: 25.0473\n",
      "Epoch 47/100\n",
      " - 1s - loss: 24.9087\n",
      "Epoch 48/100\n",
      " - 1s - loss: 25.1301\n",
      "Epoch 49/100\n",
      " - 2s - loss: 25.0812\n",
      "Epoch 50/100\n",
      " - 1s - loss: 24.9856\n",
      "Epoch 51/100\n",
      " - 1s - loss: 25.1150\n",
      "Epoch 52/100\n",
      " - 1s - loss: 25.1346\n",
      "Epoch 53/100\n",
      " - 1s - loss: 25.1228\n",
      "Epoch 54/100\n",
      " - 1s - loss: 25.0214\n",
      "Epoch 55/100\n",
      " - 1s - loss: 25.1631\n",
      "Epoch 56/100\n",
      " - 1s - loss: 25.1265\n",
      "Epoch 57/100\n",
      " - 1s - loss: 24.9689\n",
      "Epoch 58/100\n",
      " - 1s - loss: 25.1180\n",
      "Epoch 59/100\n",
      " - 1s - loss: 24.9824\n",
      "Epoch 60/100\n",
      " - 2s - loss: 25.1574\n",
      "Epoch 61/100\n",
      " - 1s - loss: 25.0411\n",
      "Epoch 62/100\n",
      " - 1s - loss: 25.1916\n",
      "Epoch 63/100\n",
      " - 2s - loss: 25.0352\n",
      "Epoch 64/100\n",
      " - 2s - loss: 25.0082\n",
      "Epoch 65/100\n",
      " - 2s - loss: 25.1062\n",
      "Epoch 66/100\n",
      " - 2s - loss: 25.0115\n",
      "Epoch 67/100\n",
      " - 2s - loss: 25.0103\n",
      "Epoch 68/100\n",
      " - 2s - loss: 24.9456\n",
      "Epoch 69/100\n",
      " - 3s - loss: 25.0815\n",
      "Epoch 70/100\n",
      " - 2s - loss: 25.0669\n",
      "Epoch 71/100\n",
      " - 1s - loss: 25.0828\n",
      "Epoch 72/100\n",
      " - 1s - loss: 25.0545\n",
      "Epoch 73/100\n",
      " - 2s - loss: 24.9388\n",
      "Epoch 74/100\n",
      " - 2s - loss: 25.1438\n",
      "Epoch 75/100\n",
      " - 2s - loss: 25.0153\n",
      "Epoch 76/100\n",
      " - 2s - loss: 24.9720\n",
      "Epoch 77/100\n",
      " - 1s - loss: 25.0226\n",
      "Epoch 78/100\n",
      " - 2s - loss: 25.0698\n",
      "Epoch 79/100\n",
      " - 1s - loss: 25.1097\n",
      "Epoch 80/100\n",
      " - 1s - loss: 25.0237\n",
      "Epoch 81/100\n",
      " - 1s - loss: 25.0611\n",
      "Epoch 82/100\n",
      " - 1s - loss: 25.0615\n",
      "Epoch 83/100\n",
      " - 2s - loss: 25.0156\n",
      "Epoch 84/100\n",
      " - 1s - loss: 25.1001\n",
      "Epoch 85/100\n",
      " - 1s - loss: 25.0180\n",
      "Epoch 86/100\n",
      " - 1s - loss: 25.0842\n",
      "Epoch 87/100\n",
      " - 2s - loss: 25.0301\n",
      "Epoch 88/100\n",
      " - 2s - loss: 25.0493\n",
      "Epoch 89/100\n",
      " - 1s - loss: 24.8339\n",
      "Epoch 90/100\n",
      " - 1s - loss: 25.1190\n",
      "Epoch 91/100\n",
      " - 1s - loss: 25.0054\n",
      "Epoch 92/100\n",
      " - 1s - loss: 25.0787\n",
      "Epoch 93/100\n",
      " - 2s - loss: 25.0257\n",
      "Epoch 94/100\n",
      " - 2s - loss: 24.9703\n",
      "Epoch 95/100\n",
      " - 1s - loss: 25.0284\n",
      "Epoch 96/100\n",
      " - 1s - loss: 25.0707\n",
      "Epoch 97/100\n",
      " - 2s - loss: 25.0053\n",
      "Epoch 98/100\n",
      " - 2s - loss: 25.0364\n",
      "Epoch 99/100\n",
      " - 3s - loss: 25.0342\n",
      "Epoch 100/100\n",
      " - 2s - loss: 25.0686\n"
     ]
    }
   ],
   "source": [
    "# Create test and training sets for one-step-ahead regression.\n",
    "window_size = 1\n",
    "train_X, train_Y = create_dataset(train, window_size)\n",
    "test_X, test_Y = create_dataset(test, window_size)\n",
    "print(\"Original training data shape:\")\n",
    "print(train_X.shape)\n",
    "\n",
    "# Reshape the input data into appropriate form for Keras.\n",
    "train_X = np.reshape(train_X, (train_X.shape[0], 1, train_X.shape[1]))\n",
    "test_X = np.reshape(test_X, (test_X.shape[0], 1, test_X.shape[1]))\n",
    "print(\"New training data shape:\")\n",
    "print(train_X.shape)\n",
    "\n",
    "def fit_model(train_X, train_Y, window_size = 1):\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(LSTM(4, \n",
    "                   input_shape = (1, window_size)))\n",
    "    model.add(Dense(1))\n",
    "    model.compile(loss = smape_kun2, \n",
    "                  optimizer = \"adam\")\n",
    "    model.fit(train_X, \n",
    "              train_Y, \n",
    "              epochs = 100, \n",
    "              batch_size = 1, \n",
    "              verbose = 2)\n",
    "    \n",
    "    return(model)\n",
    "\n",
    "# Fit the first model.\n",
    "model1 = fit_model(train_X, train_Y, window_size)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data score: 173.11 RMSE\n",
      "Test data score: 172.13 RMSE\n"
     ]
    }
   ],
   "source": [
    "y = df['y'].values.astype(\"float32\").reshape(-1, 1)\n",
    "y = scaler.fit_transform(y)\n",
    "\n",
    "def predict_and_score(model, X, Y):\n",
    "    # Make predictions on the original scale of the data.\n",
    "    pred = scaler.inverse_transform(model.predict(X))\n",
    "    # Prepare Y data to also be on the original scale for interpretability.\n",
    "    orig_data = scaler.inverse_transform([Y])\n",
    "    # Calculate RMSE.\n",
    "    score = math.sqrt(mean_squared_error(orig_data[0], pred[:, 0]))\n",
    "    return(score, pred)\n",
    "\n",
    "rmse_train, train_predict = predict_and_score(model1, train_X, train_Y)\n",
    "rmse_test, test_predict = predict_and_score(model1, test_X, test_Y)\n",
    "\n",
    "print(\"Training data score: %.2f RMSE\" % rmse_train)\n",
    "print(\"Test data score: %.2f RMSE\" % rmse_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73.45538\n"
     ]
    }
   ],
   "source": [
    "y = df['y'].values.astype(\"float32\").reshape(-1, 1)\n",
    "y = scaler.fit_transform(y)\n",
    "\n",
    "pred = scaler.inverse_transform(model1.predict(test_X))\n",
    "\n",
    "Y_Test = K.variable(Y_Test.values)\n",
    "pred = K.variable(pred)\n",
    "\n",
    "print(K.eval(smape_kun2(Y_Test, pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras.backend as K\n",
    "def smape_kun2(y_true, y_pred):\n",
    "    res = K.mean((np.abs(y_pred - y_true) * 200/ (np.abs(y_pred) + np.abs(y_true))))\n",
    "    if res == np.nan:\n",
    "        return K.variable(0)\n",
    "    else: \n",
    "        return res\n",
    "#     return K.mean((np.abs(y_pred - y_true) * 200/ (np.abs(y_pred) + np.abs(y_true))).fillna(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(475, 30, 1)\n",
      "Epoch 1/200\n",
      "475/475 [==============================] - 15s 32ms/step - loss: 203996.6898 - smape_kun2: 198.5727\n",
      "Epoch 2/200\n",
      "475/475 [==============================] - 0s 446us/step - loss: 200040.4642 - smape_kun2: 190.8903\n",
      "Epoch 3/200\n",
      "475/475 [==============================] - 0s 488us/step - loss: 186928.3396 - smape_kun2: 170.2987\n",
      "Epoch 4/200\n",
      "475/475 [==============================] - 0s 496us/step - loss: 156921.6122 - smape_kun2: 131.9623\n",
      "Epoch 5/200\n",
      "475/475 [==============================] - 0s 379us/step - loss: 108262.6940 - smape_kun2: 85.8509\n",
      "Epoch 6/200\n",
      "475/475 [==============================] - 0s 353us/step - loss: 66258.6245 - smape_kun2: 60.3617\n",
      "Epoch 7/200\n",
      "475/475 [==============================] - 0s 353us/step - loss: 48855.7920 - smape_kun2: 57.7586\n",
      "Epoch 8/200\n",
      "475/475 [==============================] - 0s 505us/step - loss: 38909.8090 - smape_kun2: 55.1537\n",
      "Epoch 9/200\n",
      "475/475 [==============================] - 0s 420us/step - loss: 32349.3881 - smape_kun2: 52.3547\n",
      "Epoch 10/200\n",
      "475/475 [==============================] - 0s 521us/step - loss: 28051.4199 - smape_kun2: 50.8338\n",
      "Epoch 11/200\n",
      "475/475 [==============================] - 0s 420us/step - loss: 25073.4297 - smape_kun2: 49.6296\n",
      "Epoch 12/200\n",
      "475/475 [==============================] - 0s 387us/step - loss: 22949.7272 - smape_kun2: 48.8517\n",
      "Epoch 13/200\n",
      "475/475 [==============================] - 0s 374us/step - loss: 21490.3959 - smape_kun2: 48.3054\n",
      "Epoch 14/200\n",
      "475/475 [==============================] - 0s 453us/step - loss: 20338.4917 - smape_kun2: 48.1617\n",
      "Epoch 15/200\n",
      "475/475 [==============================] - 0s 412us/step - loss: 19475.3478 - smape_kun2: 48.2808\n",
      "Epoch 16/200\n",
      "475/475 [==============================] - 0s 387us/step - loss: 18788.3105 - smape_kun2: 48.2426\n",
      "Epoch 17/200\n",
      "475/475 [==============================] - 0s 362us/step - loss: 18177.1956 - smape_kun2: 48.3766\n",
      "Epoch 18/200\n",
      "475/475 [==============================] - 0s 353us/step - loss: 17646.8242 - smape_kun2: 48.4548\n",
      "Epoch 19/200\n",
      "475/475 [==============================] - 0s 370us/step - loss: 17478.6899 - smape_kun2: 48.3093\n",
      "Epoch 20/200\n",
      "475/475 [==============================] - 0s 370us/step - loss: 16804.0122 - smape_kun2: 48.3329\n",
      "Epoch 21/200\n",
      "475/475 [==============================] - 0s 370us/step - loss: 16508.5011 - smape_kun2: 48.4031\n",
      "Epoch 22/200\n",
      "475/475 [==============================] - 0s 353us/step - loss: 16245.0166 - smape_kun2: 48.4001\n",
      "Epoch 23/200\n",
      "475/475 [==============================] - 0s 353us/step - loss: 15952.9947 - smape_kun2: 48.0516\n",
      "Epoch 24/200\n",
      "475/475 [==============================] - 0s 362us/step - loss: 15812.2492 - smape_kun2: 48.2793 0s - loss: 15123.4648 - smape_kun2: 47.796\n",
      "Epoch 25/200\n",
      "475/475 [==============================] - 0s 429us/step - loss: 15431.0797 - smape_kun2: 47.8965\n",
      "Epoch 26/200\n",
      "475/475 [==============================] - 0s 437us/step - loss: 15168.8224 - smape_kun2: 47.9523\n",
      "Epoch 27/200\n",
      "475/475 [==============================] - 0s 387us/step - loss: 14808.9616 - smape_kun2: 47.6607\n",
      "Epoch 28/200\n",
      "475/475 [==============================] - 0s 437us/step - loss: 14642.3865 - smape_kun2: 47.3468 0s - loss: 15922.5029 - smape_kun2: 50.5\n",
      "Epoch 29/200\n",
      "475/475 [==============================] - 0s 362us/step - loss: 14409.7047 - smape_kun2: 47.4305\n",
      "Epoch 30/200\n",
      "475/475 [==============================] - 0s 378us/step - loss: 14155.9127 - smape_kun2: 47.0255\n",
      "Epoch 31/200\n",
      "475/475 [==============================] - 0s 446us/step - loss: 13896.7721 - smape_kun2: 46.8087 0s - loss: 13616.5950 - smape_kun2: 46.992\n",
      "Epoch 32/200\n",
      "475/475 [==============================] - 0s 387us/step - loss: 13627.4618 - smape_kun2: 46.8216\n",
      "Epoch 33/200\n",
      "475/475 [==============================] - 0s 378us/step - loss: 13335.9213 - smape_kun2: 46.5628\n",
      "Epoch 34/200\n",
      "475/475 [==============================] - 0s 420us/step - loss: 13058.5865 - smape_kun2: 46.1958\n",
      "Epoch 35/200\n",
      "475/475 [==============================] - 0s 353us/step - loss: 12780.7152 - smape_kun2: 46.2957\n",
      "Epoch 36/200\n",
      "475/475 [==============================] - 0s 454us/step - loss: 12410.0104 - smape_kun2: 45.6813\n",
      "Epoch 37/200\n",
      "475/475 [==============================] - 0s 479us/step - loss: 12072.1760 - smape_kun2: 45.3904\n",
      "Epoch 38/200\n",
      "475/475 [==============================] - 0s 412us/step - loss: 11785.1840 - smape_kun2: 45.3260\n",
      "Epoch 39/200\n",
      "475/475 [==============================] - 0s 381us/step - loss: 11451.0720 - smape_kun2: 44.8929\n",
      "Epoch 40/200\n",
      "475/475 [==============================] - 0s 401us/step - loss: 11169.4045 - smape_kun2: 44.4187\n",
      "Epoch 41/200\n",
      "475/475 [==============================] - 0s 427us/step - loss: 10923.2185 - smape_kun2: 44.0741\n",
      "Epoch 42/200\n",
      "475/475 [==============================] - 0s 547us/step - loss: 10646.3225 - smape_kun2: 44.2737\n",
      "Epoch 43/200\n",
      "475/475 [==============================] - 0s 437us/step - loss: 10318.9938 - smape_kun2: 43.5413\n",
      "Epoch 44/200\n",
      "475/475 [==============================] - 0s 420us/step - loss: 10137.0231 - smape_kun2: 43.2312\n",
      "Epoch 45/200\n",
      "475/475 [==============================] - 0s 354us/step - loss: 9892.7271 - smape_kun2: 42.9794\n",
      "Epoch 46/200\n",
      "475/475 [==============================] - 0s 496us/step - loss: 9658.0345 - smape_kun2: 42.7105\n",
      "Epoch 47/200\n",
      "475/475 [==============================] - 0s 437us/step - loss: 9419.2583 - smape_kun2: 42.3278\n",
      "Epoch 48/200\n",
      "475/475 [==============================] - 0s 362us/step - loss: 9253.8904 - smape_kun2: 42.0808\n",
      "Epoch 49/200\n",
      "475/475 [==============================] - 0s 362us/step - loss: 9101.3962 - smape_kun2: 42.0401\n",
      "Epoch 50/200\n",
      "475/475 [==============================] - 0s 580us/step - loss: 8895.4011 - smape_kun2: 41.7332\n",
      "Epoch 51/200\n",
      "475/475 [==============================] - 0s 359us/step - loss: 8771.2626 - smape_kun2: 41.4825\n",
      "Epoch 52/200\n",
      "475/475 [==============================] - 0s 331us/step - loss: 8633.3593 - smape_kun2: 41.3731\n",
      "Epoch 53/200\n",
      "475/475 [==============================] - 0s 389us/step - loss: 8491.4524 - smape_kun2: 41.1268\n",
      "Epoch 54/200\n",
      "475/475 [==============================] - 0s 519us/step - loss: 8413.9884 - smape_kun2: 41.0986\n",
      "Epoch 55/200\n",
      "475/475 [==============================] - 0s 513us/step - loss: 8275.3797 - smape_kun2: 40.9188\n",
      "Epoch 56/200\n",
      "475/475 [==============================] - 0s 389us/step - loss: 8211.9699 - smape_kun2: 40.6271\n",
      "Epoch 57/200\n",
      "475/475 [==============================] - 0s 412us/step - loss: 8143.9151 - smape_kun2: 40.8253\n",
      "Epoch 58/200\n",
      "475/475 [==============================] - 0s 362us/step - loss: 8013.7743 - smape_kun2: 40.4235\n",
      "Epoch 59/200\n",
      "475/475 [==============================] - 0s 370us/step - loss: 7960.7765 - smape_kun2: 40.29390s - loss: 8094.1164 - smape_kun2: 41.84\n",
      "Epoch 60/200\n",
      "475/475 [==============================] - 0s 353us/step - loss: 7860.7502 - smape_kun2: 40.1170\n",
      "Epoch 61/200\n",
      "475/475 [==============================] - 0s 479us/step - loss: 7802.2546 - smape_kun2: 40.1353\n",
      "Epoch 62/200\n",
      "475/475 [==============================] - 0s 555us/step - loss: 7742.3562 - smape_kun2: 40.0428\n",
      "Epoch 63/200\n",
      "475/475 [==============================] - 0s 454us/step - loss: 7671.4235 - smape_kun2: 39.8937\n",
      "Epoch 64/200\n",
      "475/475 [==============================] - 0s 364us/step - loss: 7663.3353 - smape_kun2: 39.7306\n",
      "Epoch 65/200\n",
      "475/475 [==============================] - 0s 352us/step - loss: 7599.9625 - smape_kun2: 39.8574\n",
      "Epoch 66/200\n",
      "475/475 [==============================] - 0s 345us/step - loss: 7527.2252 - smape_kun2: 39.7161\n",
      "Epoch 67/200\n",
      "475/475 [==============================] - 0s 336us/step - loss: 7552.2367 - smape_kun2: 39.4147\n",
      "Epoch 68/200\n",
      "475/475 [==============================] - 0s 309us/step - loss: 7559.4067 - smape_kun2: 40.1933\n",
      "Epoch 69/200\n",
      "475/475 [==============================] - 0s 356us/step - loss: 7508.4141 - smape_kun2: 39.3642\n",
      "Epoch 70/200\n",
      "475/475 [==============================] - 0s 363us/step - loss: 7415.8927 - smape_kun2: 39.41010s - loss: 7168.9054 - smape_kun2: 33.\n",
      "Epoch 71/200\n",
      "475/475 [==============================] - 0s 442us/step - loss: 7325.7393 - smape_kun2: 39.3011\n",
      "Epoch 72/200\n",
      "475/475 [==============================] - 0s 362us/step - loss: 7309.6152 - smape_kun2: 39.2394\n",
      "Epoch 73/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "475/475 [==============================] - 0s 362us/step - loss: 7280.5651 - smape_kun2: 39.3943\n",
      "Epoch 74/200\n",
      "475/475 [==============================] - 0s 505us/step - loss: 7262.4858 - smape_kun2: 39.1597\n",
      "Epoch 75/200\n",
      "475/475 [==============================] - 0s 378us/step - loss: 7246.0146 - smape_kun2: 39.4484\n",
      "Epoch 76/200\n",
      "475/475 [==============================] - 0s 387us/step - loss: 7196.1622 - smape_kun2: 39.1309\n",
      "Epoch 77/200\n",
      "475/475 [==============================] - 0s 505us/step - loss: 7185.5806 - smape_kun2: 39.1040\n",
      "Epoch 78/200\n",
      "475/475 [==============================] - 0s 446us/step - loss: 7168.1956 - smape_kun2: 38.9994\n",
      "Epoch 79/200\n",
      "475/475 [==============================] - 0s 471us/step - loss: 7162.6535 - smape_kun2: 39.1626\n",
      "Epoch 80/200\n",
      "475/475 [==============================] - 0s 419us/step - loss: 7093.2609 - smape_kun2: 38.8484\n",
      "Epoch 81/200\n",
      "475/475 [==============================] - 0s 734us/step - loss: 7070.1488 - smape_kun2: 38.9343\n",
      "Epoch 82/200\n",
      "475/475 [==============================] - 0s 711us/step - loss: 7047.0499 - smape_kun2: 39.0492\n",
      "Epoch 83/200\n",
      "475/475 [==============================] - 0s 631us/step - loss: 7050.8157 - smape_kun2: 38.7607\n",
      "Epoch 84/200\n",
      "475/475 [==============================] - 0s 782us/step - loss: 7036.8342 - smape_kun2: 39.0621\n",
      "Epoch 85/200\n",
      "475/475 [==============================] - 0s 556us/step - loss: 7011.9863 - smape_kun2: 38.6765\n",
      "Epoch 86/200\n",
      "475/475 [==============================] - 0s 590us/step - loss: 7013.5216 - smape_kun2: 38.9587\n",
      "Epoch 87/200\n",
      "475/475 [==============================] - 0s 795us/step - loss: 6979.3416 - smape_kun2: 38.6870\n",
      "Epoch 88/200\n",
      "475/475 [==============================] - 1s 1ms/step - loss: 6958.5749 - smape_kun2: 39.1430\n",
      "Epoch 89/200\n",
      "475/475 [==============================] - 1s 1ms/step - loss: 6885.9740 - smape_kun2: 38.5071\n",
      "Epoch 90/200\n",
      "475/475 [==============================] - 0s 1ms/step - loss: 6873.1709 - smape_kun2: 38.6678: 0s - loss: 6284.0883 - smape_kun2: 35.80 - ETA: 0s - loss: 6473.6507 - smape_kun2: 37.\n",
      "Epoch 91/200\n",
      "475/475 [==============================] - 0s 715us/step - loss: 6869.4747 - smape_kun2: 38.5389\n",
      "Epoch 92/200\n",
      "475/475 [==============================] - 0s 807us/step - loss: 6814.9164 - smape_kun2: 38.4974\n",
      "Epoch 93/200\n",
      "475/475 [==============================] - 0s 858us/step - loss: 6806.4065 - smape_kun2: 38.4003\n",
      "Epoch 94/200\n",
      "475/475 [==============================] - 0s 723us/step - loss: 6787.1854 - smape_kun2: 38.5605\n",
      "Epoch 95/200\n",
      "475/475 [==============================] - 0s 799us/step - loss: 6785.6208 - smape_kun2: 38.47040s - loss: 7333.6640 - smape_kun2: 3\n",
      "Epoch 96/200\n",
      "475/475 [==============================] - 0s 841us/step - loss: 6758.3314 - smape_kun2: 38.5379\n",
      "Epoch 97/200\n",
      "475/475 [==============================] - 0s 715us/step - loss: 6751.2012 - smape_kun2: 38.3757\n",
      "Epoch 98/200\n",
      "475/475 [==============================] - 0s 882us/step - loss: 6711.6881 - smape_kun2: 38.5247\n",
      "Epoch 99/200\n",
      "475/475 [==============================] - 0s 675us/step - loss: 6707.9697 - smape_kun2: 38.3295\n",
      "Epoch 100/200\n",
      "475/475 [==============================] - 0s 580us/step - loss: 6725.0804 - smape_kun2: 38.3587\n",
      "Epoch 101/200\n",
      "475/475 [==============================] - 0s 833us/step - loss: 6673.6135 - smape_kun2: 38.2683\n",
      "Epoch 102/200\n",
      "475/475 [==============================] - 0s 614us/step - loss: 6659.3384 - smape_kun2: 38.3472\n",
      "Epoch 103/200\n",
      "475/475 [==============================] - 0s 631us/step - loss: 6612.7896 - smape_kun2: 38.1039\n",
      "Epoch 104/200\n",
      "475/475 [==============================] - 0s 1ms/step - loss: 6591.2123 - smape_kun2: 38.2381\n",
      "Epoch 105/200\n",
      "475/475 [==============================] - 0s 913us/step - loss: 6588.1666 - smape_kun2: 38.2353\n",
      "Epoch 106/200\n",
      "475/475 [==============================] - 0s 942us/step - loss: 6561.2636 - smape_kun2: 38.1080\n",
      "Epoch 107/200\n",
      "475/475 [==============================] - 0s 932us/step - loss: 6570.6691 - smape_kun2: 38.1795\n",
      "Epoch 108/200\n",
      "475/475 [==============================] - 0s 934us/step - loss: 6561.7295 - smape_kun2: 38.0908\n",
      "Epoch 109/200\n",
      "475/475 [==============================] - 0s 614us/step - loss: 6543.7095 - smape_kun2: 38.1046\n",
      "Epoch 110/200\n",
      "475/475 [==============================] - 0s 606us/step - loss: 6500.3213 - smape_kun2: 37.9279\n",
      "Epoch 111/200\n",
      "475/475 [==============================] - 0s 614us/step - loss: 6553.9107 - smape_kun2: 38.0843\n",
      "Epoch 112/200\n",
      "475/475 [==============================] - 0s 585us/step - loss: 6478.1102 - smape_kun2: 37.9276\n",
      "Epoch 113/200\n",
      "475/475 [==============================] - 0s 633us/step - loss: 6540.9383 - smape_kun2: 38.0486\n",
      "Epoch 114/200\n",
      "475/475 [==============================] - 0s 706us/step - loss: 6471.7820 - smape_kun2: 37.8902\n",
      "Epoch 115/200\n",
      "475/475 [==============================] - 0s 614us/step - loss: 6493.3080 - smape_kun2: 37.8646\n",
      "Epoch 116/200\n",
      "475/475 [==============================] - 0s 824us/step - loss: 6419.2273 - smape_kun2: 37.8884\n",
      "Epoch 117/200\n",
      "475/475 [==============================] - 0s 1ms/step - loss: 6436.8773 - smape_kun2: 37.8970\n",
      "Epoch 118/200\n",
      "475/475 [==============================] - 0s 989us/step - loss: 6398.3211 - smape_kun2: 37.9870\n",
      "Epoch 119/200\n",
      "475/475 [==============================] - 0s 965us/step - loss: 6378.3449 - smape_kun2: 37.7767\n",
      "Epoch 120/200\n",
      "475/475 [==============================] - 0s 706us/step - loss: 6396.0981 - smape_kun2: 37.8617\n",
      "Epoch 121/200\n",
      "475/475 [==============================] - 0s 597us/step - loss: 6358.5542 - smape_kun2: 37.7972\n",
      "Epoch 122/200\n",
      "475/475 [==============================] - 0s 862us/step - loss: 6365.7075 - smape_kun2: 37.6536\n",
      "Epoch 123/200\n",
      "475/475 [==============================] - 0s 882us/step - loss: 6372.4188 - smape_kun2: 37.81550s - loss: 6693.2113 - smape_kun2: 39.\n",
      "Epoch 124/200\n",
      "475/475 [==============================] - 0s 841us/step - loss: 6282.1054 - smape_kun2: 37.5841\n",
      "Epoch 125/200\n",
      "475/475 [==============================] - 0s 919us/step - loss: 6318.5794 - smape_kun2: 37.7120\n",
      "Epoch 126/200\n",
      "475/475 [==============================] - 0s 747us/step - loss: 6264.4750 - smape_kun2: 37.5634\n",
      "Epoch 127/200\n",
      "475/475 [==============================] - 0s 538us/step - loss: 6268.2003 - smape_kun2: nan\n",
      "Epoch 128/200\n",
      "475/475 [==============================] - 0s 614us/step - loss: 6335.8758 - smape_kun2: 37.5875\n",
      "Epoch 129/200\n",
      "475/475 [==============================] - 0s 614us/step - loss: 6243.0217 - smape_kun2: nan\n",
      "Epoch 130/200\n",
      "475/475 [==============================] - 0s 614us/step - loss: 6215.4427 - smape_kun2: nan\n",
      "Epoch 131/200\n",
      "475/475 [==============================] - 0s 908us/step - loss: 6206.8996 - smape_kun2: nan\n",
      "Epoch 132/200\n",
      "475/475 [==============================] - 0s 950us/step - loss: 6193.2446 - smape_kun2: nan\n",
      "Epoch 133/200\n",
      "475/475 [==============================] - 0s 840us/step - loss: 6268.1226 - smape_kun2: nan\n",
      "Epoch 134/200\n",
      "475/475 [==============================] - 0s 723us/step - loss: 6167.3585 - smape_kun2: nan\n",
      "Epoch 135/200\n",
      "475/475 [==============================] - 0s 589us/step - loss: 6141.8595 - smape_kun2: nan\n",
      "Epoch 136/200\n",
      "475/475 [==============================] - 0s 606us/step - loss: 6154.8497 - smape_kun2: nan\n",
      "Epoch 137/200\n",
      "475/475 [==============================] - 0s 631us/step - loss: 6137.5082 - smape_kun2: nan\n",
      "Epoch 138/200\n",
      "475/475 [==============================] - 0s 572us/step - loss: 6145.5504 - smape_kun2: nan\n",
      "Epoch 139/200\n",
      "475/475 [==============================] - 0s 597us/step - loss: 6084.4273 - smape_kun2: nan\n",
      "Epoch 140/200\n",
      "475/475 [==============================] - 0s 622us/step - loss: 6081.0355 - smape_kun2: nan\n",
      "Epoch 141/200\n",
      "475/475 [==============================] - 0s 664us/step - loss: 6107.7905 - smape_kun2: nan\n",
      "Epoch 142/200\n",
      "475/475 [==============================] - 0s 1ms/step - loss: 6083.4794 - smape_kun2: nan\n",
      "Epoch 143/200\n",
      "475/475 [==============================] - 0s 757us/step - loss: 6048.6303 - smape_kun2: nan\n",
      "Epoch 144/200\n",
      "475/475 [==============================] - 0s 605us/step - loss: 6019.5960 - smape_kun2: nan\n",
      "Epoch 145/200\n",
      "475/475 [==============================] - 0s 664us/step - loss: 6013.6070 - smape_kun2: nan\n",
      "Epoch 146/200\n",
      "475/475 [==============================] - 0s 656us/step - loss: 6005.4980 - smape_kun2: nan\n",
      "Epoch 147/200\n",
      "475/475 [==============================] - 0s 631us/step - loss: 6012.9272 - smape_kun2: nan\n",
      "Epoch 148/200\n",
      "475/475 [==============================] - 0s 572us/step - loss: 6006.8328 - smape_kun2: nan\n",
      "Epoch 149/200\n",
      "475/475 [==============================] - 0s 824us/step - loss: 6065.0695 - smape_kun2: nan\n",
      "Epoch 150/200\n",
      "475/475 [==============================] - 0s 639us/step - loss: 5947.9008 - smape_kun2: nan\n",
      "Epoch 151/200\n",
      "475/475 [==============================] - 0s 563us/step - loss: 5944.0066 - smape_kun2: nan\n",
      "Epoch 152/200\n",
      "475/475 [==============================] - 0s 547us/step - loss: 5929.8151 - smape_kun2: nan\n",
      "Epoch 153/200\n",
      "475/475 [==============================] - 0s 555us/step - loss: 5912.0391 - smape_kun2: nan\n",
      "Epoch 154/200\n",
      "475/475 [==============================] - 0s 563us/step - loss: 5920.2413 - smape_kun2: nan\n",
      "Epoch 155/200\n",
      "475/475 [==============================] - 0s 547us/step - loss: 5910.5486 - smape_kun2: nan\n",
      "Epoch 156/200\n",
      "475/475 [==============================] - 0s 540us/step - loss: 5924.4376 - smape_kun2: nan\n",
      "Epoch 157/200\n",
      "475/475 [==============================] - 0s 540us/step - loss: 5970.4551 - smape_kun2: nan\n",
      "Epoch 158/200\n",
      "475/475 [==============================] - 0s 538us/step - loss: 6041.2291 - smape_kun2: nan\n",
      "Epoch 159/200\n",
      "475/475 [==============================] - 0s 538us/step - loss: 5857.4950 - smape_kun2: nan\n",
      "Epoch 160/200\n",
      "475/475 [==============================] - 0s 521us/step - loss: 5976.5600 - smape_kun2: nan\n",
      "Epoch 161/200\n",
      "475/475 [==============================] - 0s 572us/step - loss: 5965.8074 - smape_kun2: nan\n",
      "Epoch 162/200\n",
      "475/475 [==============================] - 0s 538us/step - loss: 5842.3028 - smape_kun2: nan\n",
      "Epoch 163/200\n",
      "475/475 [==============================] - 0s 521us/step - loss: 5839.3946 - smape_kun2: nan\n",
      "Epoch 164/200\n",
      "475/475 [==============================] - 0s 530us/step - loss: 5924.4664 - smape_kun2: nan\n",
      "Epoch 165/200\n",
      "475/475 [==============================] - 0s 555us/step - loss: 5810.5255 - smape_kun2: nan\n",
      "Epoch 166/200\n",
      "475/475 [==============================] - 0s 597us/step - loss: 5758.0851 - smape_kun2: nan\n",
      "Epoch 167/200\n",
      "475/475 [==============================] - 0s 547us/step - loss: 5739.6715 - smape_kun2: nan\n",
      "Epoch 168/200\n",
      "475/475 [==============================] - 0s 563us/step - loss: 5759.0237 - smape_kun2: nan\n",
      "Epoch 169/200\n",
      "475/475 [==============================] - 0s 580us/step - loss: 5830.3633 - smape_kun2: nan\n",
      "Epoch 170/200\n",
      "475/475 [==============================] - 0s 580us/step - loss: 5775.7000 - smape_kun2: nan\n",
      "Epoch 171/200\n",
      "475/475 [==============================] - 0s 538us/step - loss: 5744.9568 - smape_kun2: nan\n",
      "Epoch 172/200\n",
      "475/475 [==============================] - 0s 555us/step - loss: 5735.2820 - smape_kun2: nan\n",
      "Epoch 173/200\n",
      "475/475 [==============================] - 0s 547us/step - loss: 5713.7479 - smape_kun2: nan\n",
      "Epoch 174/200\n",
      "475/475 [==============================] - 0s 505us/step - loss: 5718.9460 - smape_kun2: nan\n",
      "Epoch 175/200\n",
      "475/475 [==============================] - 0s 530us/step - loss: 5686.7225 - smape_kun2: nan\n",
      "Epoch 176/200\n",
      "475/475 [==============================] - 0s 562us/step - loss: 5678.5745 - smape_kun2: nan\n",
      "Epoch 177/200\n",
      "475/475 [==============================] - 0s 866us/step - loss: 5705.6264 - smape_kun2: nan\n",
      "Epoch 178/200\n",
      "475/475 [==============================] - 0s 757us/step - loss: 5670.4182 - smape_kun2: nan\n",
      "Epoch 179/200\n",
      "475/475 [==============================] - 0s 631us/step - loss: 5615.7399 - smape_kun2: nan\n",
      "Epoch 180/200\n",
      "475/475 [==============================] - 0s 606us/step - loss: 5648.7598 - smape_kun2: nan\n",
      "Epoch 181/200\n",
      "475/475 [==============================] - 0s 572us/step - loss: 5626.0289 - smape_kun2: nan\n",
      "Epoch 182/200\n",
      "475/475 [==============================] - 0s 765us/step - loss: 5611.7054 - smape_kun2: nan: 0s - loss: 5515.2627 - smape_kun\n",
      "Epoch 183/200\n",
      "475/475 [==============================] - 1s 1ms/step - loss: 5679.6195 - smape_kun2: nan\n",
      "Epoch 184/200\n",
      "475/475 [==============================] - 0s 984us/step - loss: 5701.6383 - smape_kun2: nan\n",
      "Epoch 185/200\n",
      "475/475 [==============================] - 0s 858us/step - loss: 5544.5325 - smape_kun2: nan\n",
      "Epoch 186/200\n",
      "475/475 [==============================] - 0s 937us/step - loss: 5573.7028 - smape_kun2: nan\n",
      "Epoch 187/200\n",
      "475/475 [==============================] - 0s 834us/step - loss: 5656.5117 - smape_kun2: nan\n",
      "Epoch 188/200\n",
      "475/475 [==============================] - 0s 572us/step - loss: 5557.5393 - smape_kun2: nan\n",
      "Epoch 189/200\n",
      "475/475 [==============================] - 0s 603us/step - loss: 5489.6652 - smape_kun2: nan\n",
      "Epoch 190/200\n",
      "475/475 [==============================] - 0s 588us/step - loss: 5519.4809 - smape_kun2: nan\n",
      "Epoch 191/200\n",
      "475/475 [==============================] - 0s 563us/step - loss: 5510.8585 - smape_kun2: nan\n",
      "Epoch 192/200\n",
      "475/475 [==============================] - 0s 574us/step - loss: 5461.1642 - smape_kun2: nan\n",
      "Epoch 193/200\n",
      "475/475 [==============================] - 0s 698us/step - loss: 5488.3873 - smape_kun2: nan\n",
      "Epoch 194/200\n",
      "475/475 [==============================] - 0s 976us/step - loss: 5441.0892 - smape_kun2: nan\n",
      "Epoch 195/200\n",
      "475/475 [==============================] - 0s 869us/step - loss: 5478.5196 - smape_kun2: nan\n",
      "Epoch 196/200\n",
      "475/475 [==============================] - 0s 938us/step - loss: 5426.9608 - smape_kun2: nan\n",
      "Epoch 197/200\n",
      "475/475 [==============================] - 0s 969us/step - loss: 5486.6397 - smape_kun2: nan\n",
      "Epoch 198/200\n",
      "475/475 [==============================] - 0s 758us/step - loss: 5474.3631 - smape_kun2: nan\n",
      "Epoch 199/200\n",
      "475/475 [==============================] - 0s 597us/step - loss: 5440.3422 - smape_kun2: nan\n",
      "Epoch 200/200\n",
      "475/475 [==============================] - 0s 622us/step - loss: 5428.0825 - smape_kun2: nan\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1522cc53128>"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Conv1D, Flatten\n",
    "#create model\n",
    "# train_X = np.reshape(X_Train, (X_Train.shape[0], X_Train.shape[1], 1))\n",
    "# test_X = np.reshape(X_Test, (X_Test.shape[0], X_Test.shape[1], 1))\n",
    "# print(train_X.shape)\n",
    "\n",
    "train_X = X_Train.reshape(X_Train.shape[0], X_Train.shape[1], 1)   #Reshape for CNN -  should work!!\n",
    "print(train_X.shape)\n",
    "test_X = X_Test.reshape(X_Test.shape[0], X_Test.shape[1], 1)\n",
    "\n",
    "model = Sequential()\n",
    "#add model layers\n",
    "model.add(Conv1D(64, kernel_size=3, activation='relu', input_shape=(30, 1)))\n",
    "model.add(Conv1D(32, kernel_size=3, activation='relu'))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1, activation='relu'))\n",
    "\n",
    "#compile model using accuracy to measure model performance\n",
    "model.compile(optimizer='adam', loss='mean_squared_error', metrics=[smape_kun2])\n",
    "\n",
    "#train the model\n",
    "model.fit(train_X, Y_Train, epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[867.7157 ]\n",
      " [262.68402]\n",
      " [607.5696 ]\n",
      " [946.83057]\n",
      " [432.23187]\n",
      " [337.71848]\n",
      " [859.1907 ]\n",
      " [277.25235]\n",
      " [732.86316]\n",
      " [247.81816]\n",
      " [  0.     ]\n",
      " [ 35.62082]\n",
      " [697.0402 ]\n",
      " [354.8297 ]\n",
      " [735.61194]\n",
      " [869.08417]\n",
      " [248.44254]\n",
      " [141.53867]\n",
      " [299.8991 ]\n",
      " [162.2626 ]\n",
      " [927.0357 ]\n",
      " [268.90594]\n",
      " [226.74643]\n",
      " [152.10052]\n",
      " [123.06133]]\n",
      "20.52364306574942\n"
     ]
    }
   ],
   "source": [
    "#import tensorflow as t\n",
    "#predict first 4 images in the test set\n",
    "# pred = model.predict(test_X)\n",
    "# print(pred)\n",
    "# Y_Test = K.variable(Y_Test)\n",
    "# pred = K.variable(pred)\n",
    "\n",
    "# print(K.eval(smape_fast(Y_Test, pred)))\n",
    "\n",
    "pred = model.predict(test_X)\n",
    "print(pred)\n",
    "#Y_Test = K.variable(Y_Test)\n",
    "#pred = K.variable(pred)\n",
    "pred = twoone(pred)\n",
    "\n",
    "print((smape_kun(Y_Test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8) ARIMA Time Series Forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import datetime\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2028c0e5c50>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data, test_data = df['y'][0:int(len(df)*0.95)], df['y'][int(len(df)*0.95):]\n",
    "plt.figure(figsize=(12,7))\n",
    "plt.title('Series')\n",
    "plt.plot(df['y'][0:len(df)-25], 'blue', label='Series output')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\scipy\\signal\\signaltools.py:1341: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  out_full[ind] += zi\n",
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\scipy\\signal\\signaltools.py:1344: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  out = out_full[ind]\n",
      "C:\\Users\\hp\\Anaconda3\\lib\\site-packages\\scipy\\signal\\signaltools.py:1350: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  zf = out_full[ind]\n"
     ]
    }
   ],
   "source": [
    "train_ar = train_data.values\n",
    "test_ar = test_data.values\n",
    "\n",
    "# https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/\n",
    "history = [x for x in train_ar]\n",
    "print(type(history))\n",
    "predictions = list()\n",
    "for t in range(len(test_ar)):\n",
    "    model = ARIMA(history, order=(15,2,0))\n",
    "    model_fit = model.fit(disp=0)\n",
    "    output = model_fit.forecast()\n",
    "    yhat = output[0]\n",
    "    predictions.append(yhat)\n",
    "    #obs = test_ar[t]\n",
    "    history.append(yhat)\n",
    "    #print('predicted=%f, expected=%f' % (yhat, obs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Mean Squared Error: 3911.441\n",
      "Symmetric mean absolute percentage error: 136.514\n",
      "[ -1.58924831 130.74046399  36.88570607  22.69030881   6.54279664\n",
      "   9.73476589  29.93690918   7.91814368 105.02884388  67.22115623\n",
      "  15.75243246  30.75079683  21.19428844  60.81632229  23.57693598\n",
      " 117.0460035   65.99448715  51.26204056  50.28710037  52.31741046\n",
      "  68.837283    43.84174427 108.75640147  87.98083207  63.9792876 ]\n"
     ]
    }
   ],
   "source": [
    "error = mean_squared_error(test_ar, predictions)\n",
    "print('Testing Mean Squared Error: %.3f' % error)\n",
    "predictions = np.array(predictions).ravel()\n",
    "#print(predictions.shape)\n",
    "error2 = smape_kun(Y_Test, predictions)\n",
    "print('Symmetric mean absolute percentage error: %.3f' % error2)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-58.1328402667433"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['y'][len(df)-27]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2028c31aa90>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "plt.plot(test_data.index, predictions, color='green', marker='o', linestyle='dashed', \n",
    "         label='Predicted Price')\n",
    "plt.plot(df['y'][0:len(df)-25], 'blue', label='Series output')\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}