Stoch Opt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# Setting seed for reproducability\n",
    "np.random.seed(1234)  \n",
    "PYTHONHASHSEED = 0\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix, recall_score, precision_score\n",
    "from keras import models\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from keras.models import Sequential,model_from_json\n",
    "from keras.layers import Dense, Dropout, LSTM, Activation\n",
    "%matplotlib inline\n",
    "from decimal import Decimal\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pickle\n",
    "from sklearn.preprocessing import OneHotEncoder,LabelEncoder,Normalizer, MinMaxScaler\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from numpy.random import seed\n",
    "seed(1)\n",
    "from tensorflow import set_random_seed\n",
    "set_random_seed(2)\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn import ensemble\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "import xgboost as xgb\n",
    "\n",
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "from bayes_opt import BayesianOptimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('C:\\\\Users\\\\lengada1\\\\NCSU\\\\ten_skus.csv')\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df['Year'] = df['Date'].dt.year\n",
    "df['Month'] = df['Date'].dt.month\n",
    "df['Day'] = df['Date'].dt.weekday_name\n",
    "day_dummy=pd.get_dummies(df.Day)\n",
    "df=pd.concat([df,day_dummy],axis=1)\n",
    "df.drop(['Day','Date'],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_df(df):\n",
    "    d={}\n",
    "    split_by_list=np.unique(df.id.values)\n",
    "    for sku in split_by_list:\n",
    "        d[sku]=df[df.id==sku]\n",
    "    return d\n",
    "df_dict=split_df(df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y={}\n",
    "for sku in list(df_dict.keys()):\n",
    "    y[sku]=df_dict[sku]['Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X={}\n",
    "for sku in list(df_dict.keys()):\n",
    "    X[sku]=df_dict[sku]\n",
    "    X[sku]=X[sku].drop(['id','DayOfWeek','Customers','High_Var','Luxury','Sales'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sku in list(df_dict.keys()):\n",
    "    for obs in range(1,8):\n",
    "        X[sku][\"Sales_T\"+str(obs)]=df_dict[sku]['Sales'].shift(obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sku in list(df_dict.keys()):\n",
    "    X[sku][\"Mov_avg\"]=df_dict[sku]['Sales'].rolling( window=7).mean().shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cut lagged vars NAs off top \n",
    "cut_lag=7;\n",
    "for sku in list(df_dict.keys()):\n",
    "    y[sku]=y[sku][cut_lag:]\n",
    "    y[sku].reset_index(drop=True, inplace=True)\n",
    "    \n",
    "for sku in list(df_dict.keys()):\n",
    "    X[sku]=X[sku][cut_lag:]\n",
    "    X[sku].reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Sales_T1</th>\n",
       "      <th>Sales_T2</th>\n",
       "      <th>Sales_T3</th>\n",
       "      <th>Sales_T4</th>\n",
       "      <th>Sales_T5</th>\n",
       "      <th>Sales_T6</th>\n",
       "      <th>Sales_T7</th>\n",
       "      <th>Mov_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7176.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4997.0</td>\n",
       "      <td>4486.0</td>\n",
       "      <td>4327.0</td>\n",
       "      <td>5530.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3788.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5580.0</td>\n",
       "      <td>7176.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4997.0</td>\n",
       "      <td>4486.0</td>\n",
       "      <td>4327.0</td>\n",
       "      <td>5530.0</td>\n",
       "      <td>4585.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5471.0</td>\n",
       "      <td>5580.0</td>\n",
       "      <td>7176.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4997.0</td>\n",
       "      <td>4486.0</td>\n",
       "      <td>4327.0</td>\n",
       "      <td>4576.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4892.0</td>\n",
       "      <td>5471.0</td>\n",
       "      <td>5580.0</td>\n",
       "      <td>7176.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4997.0</td>\n",
       "      <td>4486.0</td>\n",
       "      <td>4657.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4881.0</td>\n",
       "      <td>4892.0</td>\n",
       "      <td>5471.0</td>\n",
       "      <td>5580.0</td>\n",
       "      <td>7176.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4997.0</td>\n",
       "      <td>4713.857143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Open  Promo  SchoolHoliday  Year  Month  Friday  Monday  Saturday  Sunday  \\\n",
       "0     1      1              1  2013      1       0       0         0       0   \n",
       "1     1      1              1  2013      1       0       0         0       0   \n",
       "2     1      1              1  2013      1       0       0         0       0   \n",
       "3     1      1              1  2013      1       1       0         0       0   \n",
       "4     1      0              0  2013      1       0       0         1       0   \n",
       "\n",
       "   Thursday  Tuesday  Wednesday  Sales_T1  Sales_T2  Sales_T3  Sales_T4  \\\n",
       "0         0        1          0    7176.0       0.0    4997.0    4486.0   \n",
       "1         0        0          1    5580.0    7176.0       0.0    4997.0   \n",
       "2         1        0          0    5471.0    5580.0    7176.0       0.0   \n",
       "3         0        0          0    4892.0    5471.0    5580.0    7176.0   \n",
       "4         0        0          0    4881.0    4892.0    5471.0    5580.0   \n",
       "\n",
       "   Sales_T5  Sales_T6  Sales_T7      Mov_avg  \n",
       "0    4327.0    5530.0       0.0  3788.000000  \n",
       "1    4486.0    4327.0    5530.0  4585.142857  \n",
       "2    4997.0    4486.0    4327.0  4576.714286  \n",
       "3       0.0    4997.0    4486.0  4657.428571  \n",
       "4    7176.0       0.0    4997.0  4713.857143  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "935"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "std={}\n",
    "for sku in list(df_dict.keys()):\n",
    "    std[sku] = preprocessing.StandardScaler().fit(X[sku])\n",
    "    X[sku] = std[sku].transform(X[sku])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv=2\n",
    "\n",
    "def RF(n_est,max_d,max_f):\n",
    "    RF=RandomForestRegressor( n_estimators=int(n_est),max_depth=int(max_d),max_features=int(max_f) )\n",
    "    rf_models=[];\n",
    "    for sku in list(df_dict.keys()):\n",
    "        rf_models.append(cross_val_score(RF,X[sku],y[sku],cv=cv, scoring ='mean_absolute_error').mean().round(0))\n",
    "    return np.array(rf_models).mean()\n",
    "\n",
    "\n",
    "def GB(n_est,max_d,max_f):\n",
    "    GB=ensemble.GradientBoostingRegressor(n_estimators=int(n_est),max_depth=int(max_d),max_features=int(max_f));\n",
    "    gb_models=[];\n",
    "    for sku in list(df_dict.keys()):\n",
    "        gb_models.append(cross_val_score(GB,X[sku],y[sku],cv=cv, scoring ='mean_absolute_error').mean().round(0))\n",
    "    return np.array(gb_models).mean()   \n",
    "    \n",
    "def XG(n_est,lr,max_d,g):\n",
    "    XG=xgb.XGBRegressor(n_estimators= int(n_est),learning_rate =lr, max_depth=int(max_d),gamma=int(g))\n",
    "    xg_models=[]\n",
    "    for sku in list(df_dict.keys()):\n",
    "        xg_models.append(cross_val_score(XG,X[sku],y[sku],cv=cv, scoring ='mean_absolute_error').mean().round(0) ) \n",
    "    return np.array(xg_models).mean()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfBO = BayesianOptimization(RF,\n",
    "        {'n_est': (500,1000), 'max_d': (10,40),'max_f':(12,20)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mInitialization\u001b[0m\n",
      "\u001b[94m-----------------------------------------------------------------\u001b[0m\n",
      " Step |   Time |      Value |     max_d |     max_f |     n_est | \n",
      "    1 | 00m34s | \u001b[35m-522.70000\u001b[0m | \u001b[32m  16.0925\u001b[0m | \u001b[32m  16.7370\u001b[0m | \u001b[32m 829.0479\u001b[0m | \n",
      "    2 | 00m25s | -525.60000 |   11.6789 |   13.0861 |  759.9960 | \n",
      "    3 | 00m41s | -523.40000 |   29.9533 |   16.5665 |  958.5360 | \n",
      "    4 | 00m45s | -524.00000 |   38.1253 |   19.0583 |  997.3825 | \n",
      "    5 | 00m28s | -524.90000 |   35.9087 |   12.0598 |  790.3777 | \n",
      "\u001b[31mBayesian Optimization\u001b[0m\n",
      "\u001b[94m-----------------------------------------------------------------\u001b[0m\n",
      " Step |   Time |      Value |     max_d |     max_f |     n_est | \n",
      "    6 | 00m28s | -525.30000 |   40.0000 |   20.0000 |  500.0000 | \n",
      "    7 | 00m38s | -526.00000 |   10.0000 |   20.0000 |  886.0821 | \n",
      "    8 | 00m16s | -528.40000 |   10.0000 |   12.0000 |  500.0000 | \n",
      "    9 | 00m27s | -528.90000 |   10.0000 |   12.0000 |  820.1148 | \n",
      "   10 | 00m43s | -525.70000 |   40.0000 |   20.0000 |  852.8734 | \n",
      "   11 | 00m40s | -525.60000 |   40.0000 |   20.0000 |  728.3276 | \n",
      "   12 | 00m44s | -525.90000 |   10.0000 |   20.0000 | 1000.0000 | \n",
      "   13 | 00m38s | -525.40000 |   40.0000 |   20.0000 |  788.5615 | \n",
      "   14 | 00m25s | -527.30000 |   10.0000 |   12.0000 |  673.3592 | \n",
      "   15 | 00m45s | -524.90000 |   17.0782 |   20.0000 |  839.0485 | \n",
      "   16 | 00m36s | -526.20000 |   40.0000 |   12.0000 |  823.6282 | \n",
      "   17 | 00m47s | -524.90000 |   40.0000 |   20.0000 |  921.8869 | \n",
      "   18 | 00m31s | -527.80000 |   10.0000 |   12.0000 |  939.8532 | \n",
      "   19 | 00m47s | -525.40000 |   40.0000 |   20.0000 |  959.1544 | \n",
      "   20 | 00m39s | -526.30000 |   40.0000 |   12.0000 | 1000.0000 | \n",
      "   21 | 00m24s | -525.40000 |   40.0000 |   12.0000 |  627.3321 | \n",
      "   22 | 00m39s | -525.30000 |   23.2138 |   20.0000 |  820.9545 | \n",
      "   23 | 00m29s | -526.30000 |   40.0000 |   12.0000 |  759.9959 | \n",
      "   24 | 00m33s | -526.00000 |   40.0000 |   12.0000 |  885.4092 | \n",
      "   25 | 00m31s | -525.80000 |   10.0000 |   20.0000 |  725.6443 | \n"
     ]
    }
   ],
   "source": [
    "n_iter=20\n",
    "gp_params = {\"alpha\": 1e-5}\n",
    "rfBO.maximize(n_iter=n_iter,**gp_params,acq='ei')\n",
    "\n",
    "#print('-', * 53)\n",
    "#print('RF: %f' % rfBO.res['max']['max_val'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbBO = BayesianOptimization(GB,\n",
    "        {'n_est': (100,500),  #default=100\n",
    "         'max_d': (3,10),  #default = 3\n",
    "         'max_f':(12,20)})  #default =None\n",
    "\n",
    "gbBO.maximize(n_iter=n_iter,**gp_params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgBO = BayesianOptimization(XG,\n",
    "        {'n_est': (100,500), #default=100\n",
    "         'lr':(.001,.1),  #default=0.1\n",
    "         'max_d': (3,10), #default=3\n",
    "         'g':(0,2)})  #default=0\n",
    "\n",
    "xgBO.explore({'n_est': (100,250,500), 'lr':(.001,.01,.1),'max_d': (5,7,10),'g':(0,2,5)})\n",
    "xgBO.maximize(n_iter=n_iter,**gp_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_MLP():\n",
    "    mlp=models.Sequential()\n",
    "    mlp.add(Dense(20, input_dim=X[1].shape[1], activation='relu'))\n",
    "    mlp.add(Dense(30, activation='relu'))\n",
    "    #mlp.add(Dense(30, activation='relu'))\n",
    "    mlp.add(Dense(10, activation='relu'))\n",
    "    mlp.add(Dense(1))\n",
    "    mlp.compile(loss='mae', optimizer='adam', metrics=['mae'])\n",
    "    return mlp\n",
    "\n",
    "MLP = KerasRegressor(build_fn=create_MLP,\n",
    "                               epochs=400,\n",
    "                               batch_size=20,\n",
    "                               verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp_models=[]\n",
    "for sku in list(df_dict.keys()):\n",
    "    mlp_models.append(cross_val_score(MLP,X[sku],y[sku],cv=cv).mean().round(0)  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MLP</th>\n",
       "      <th>RF</th>\n",
       "      <th>GB</th>\n",
       "      <th>XG</th>\n",
       "      <th>Best</th>\n",
       "      <th>range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>419.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>453.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>737.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>624.0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>791.0</td>\n",
       "      <td>747.0</td>\n",
       "      <td>801.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>416.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>651.0</td>\n",
       "      <td>552.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>762.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>751.0</td>\n",
       "      <td>742.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>646.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>586.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>698.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>MLP</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>442.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>590.3</td>\n",
       "      <td>524.7</td>\n",
       "      <td>552.0</td>\n",
       "      <td>549.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        MLP     RF     GB     XG Best  range\n",
       "1     419.0  360.0  380.0  361.0   RF   59.0\n",
       "2     453.0  384.0  410.0  386.0   RF   69.0\n",
       "3     737.0  584.0  624.0  597.0   RF  153.0\n",
       "4     791.0  747.0  801.0  793.0   RF   54.0\n",
       "5     416.0  369.0  386.0  384.0   RF   47.0\n",
       "6     651.0  552.0  574.0  601.0   RF   99.0\n",
       "7     762.0  726.0  751.0  742.0   RF   36.0\n",
       "8     646.0  485.0  485.0  506.0   RF  161.0\n",
       "9     586.0  655.0  698.0  727.0  MLP  141.0\n",
       "10    442.0  385.0  411.0  401.0   RF   57.0\n",
       "mean  590.3  524.7  552.0  549.8  NaN   65.6"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae=-pd.DataFrame(np.column_stack((mlp_models,rf_models,gb_models,xg_models)) )\n",
    "mae=mae.rename(columns = {0:'MLP',1:'RF',2:'GB',3:'XG'})\n",
    "best=mae.idxmin(axis=1)\n",
    "best.astype('category')\n",
    "mae[\"Best\"]=best\n",
    "mae.index = mae.index + 1\n",
    "mae.loc['mean'] = mae.mean()\n",
    "mae['range']=mae.max(axis=1)-mae.min(axis=1)\n",
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Need lots of rank and counts of how many times models came in 1st  vs 2nd.\n",
    "def rank_model(df,rank):\n",
    "    coln = 'Best' + str(rank) \n",
    "    sortID = np.argpartition(df[['MLP','RF','GB','XG']].values,rank,axis=1)[:,rank]\n",
    "    df[coln] = df.columns[sortID]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MLP</th>\n",
       "      <th>RF</th>\n",
       "      <th>GB</th>\n",
       "      <th>XG</th>\n",
       "      <th>Best</th>\n",
       "      <th>range</th>\n",
       "      <th>Best0</th>\n",
       "      <th>Best1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>419.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>59.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>XG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>453.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>69.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>XG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>737.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>624.0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>153.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>XG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>791.0</td>\n",
       "      <td>747.0</td>\n",
       "      <td>801.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>MLP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>416.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>47.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>XG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>651.0</td>\n",
       "      <td>552.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>99.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>GB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>762.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>751.0</td>\n",
       "      <td>742.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>36.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>XG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>646.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>161.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>GB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>586.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>698.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>MLP</td>\n",
       "      <td>141.0</td>\n",
       "      <td>MLP</td>\n",
       "      <td>RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>442.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>57.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>XG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>590.3</td>\n",
       "      <td>524.7</td>\n",
       "      <td>552.0</td>\n",
       "      <td>549.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.6</td>\n",
       "      <td>RF</td>\n",
       "      <td>XG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        MLP     RF     GB     XG Best  range Best0 Best1\n",
       "1     419.0  360.0  380.0  361.0   RF   59.0    RF    XG\n",
       "2     453.0  384.0  410.0  386.0   RF   69.0    RF    XG\n",
       "3     737.0  584.0  624.0  597.0   RF  153.0    RF    XG\n",
       "4     791.0  747.0  801.0  793.0   RF   54.0    RF   MLP\n",
       "5     416.0  369.0  386.0  384.0   RF   47.0    RF    XG\n",
       "6     651.0  552.0  574.0  601.0   RF   99.0    RF    GB\n",
       "7     762.0  726.0  751.0  742.0   RF   36.0    RF    XG\n",
       "8     646.0  485.0  485.0  506.0   RF  161.0    RF    GB\n",
       "9     586.0  655.0  698.0  727.0  MLP  141.0   MLP    RF\n",
       "10    442.0  385.0  411.0  401.0   RF   57.0    RF    XG\n",
       "mean  590.3  524.7  552.0  549.8  NaN   65.6    RF    XG"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_model(mae,0)\n",
    "rank_model(mae,1)\n",
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RF_final(X, y):\n",
    "    tree=RF\n",
    "    tree.fit(X,y)\n",
    "    pred=tree.predict(X)\n",
    "    pred=pd.DataFrame(pred)\n",
    "    pred.reset_index(drop=True, inplace=True)\n",
    "    pred=pred.rename(columns = {0:'RF'})\n",
    "    return pred\n",
    "\n",
    "def MLP_final(X,y):\n",
    "    model = MLP\n",
    "    model.fit(X,y)\n",
    "    pred=model.predict(X)\n",
    "    pred=pd.DataFrame(pred)\n",
    "    pred.reset_index(drop=True, inplace=True)\n",
    "    pred=pred.rename(columns = {0:'MLP'})\n",
    "    return pred\n",
    "\n",
    "def GB_final(X,y):\n",
    "    model = GB\n",
    "    model.fit(X,y)\n",
    "    prediction=model.predict(X)\n",
    "    pred=pd.DataFrame(prediction)\n",
    "    pred.reset_index(drop=True, inplace=True)\n",
    "    pred=pred.rename(columns = {0:'GB'})\n",
    "    return pred\n",
    "\n",
    "def XG_final(X,y):\n",
    "    model=XG\n",
    "    model.fit(X,y)\n",
    "    prediction=model.predict(X)\n",
    "    pred=pd.DataFrame(prediction)\n",
    "    pred.reset_index(drop=True, inplace=True)\n",
    "    pred=pred.rename(columns = {0:'XG'})\n",
    "    return pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#NOTE, because we are retraining models here, a bad initiatlization could lead to different results. \n",
    "\n",
    "def best_model(scores,X,y):\n",
    "    d={}\n",
    "    for sku in list(df_dict.keys()):\n",
    "        \n",
    "        if scores.loc[sku][\"Best\"]==\"RF\":     \n",
    "            d[sku]=RF_final(X[sku],y[sku])\n",
    "                    \n",
    "        elif scores.loc[sku][\"Best\"]==\"MLP\":\n",
    "            d[sku]=MLP_final(X[sku],y[sku])                 \n",
    "            \n",
    "        elif scores.loc[sku][\"Best\"]==\"GB\":\n",
    "            d[sku]=GB_final(X[sku],y[sku])\n",
    "            \n",
    "        elif scores.loc[sku][\"Best\"]==\"XG\":\n",
    "            d[sku]=XG_final(X[sku],y[sku])\n",
    "      \n",
    "    return d     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "bm_dict=best_model(mae,X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{1:               RF\\n0    5717.818209\\n1    5510.298604\\n2    5079.047790\\n3    4855.066339\\n4    4848.817431\\n5       0.000000\\n6    4364.450817\\n7    4031.301953\\n8    3944.842050\\n9    3894.189293\\n10   4245.188187\\n11   4983.633181\\n12      0.000000\\n13   5744.192988\\n14   5223.222718\\n15   5362.314867\\n16   5163.339575\\n17   5257.620845\\n18   5379.716069\\n19      0.000000\\n20   4208.318142\\n21   3888.350691\\n22   4108.486962\\n23   4515.580422\\n24   5273.183988\\n25   5711.538596\\n26      0.000000\\n27   6906.761140\\n28   6097.723153\\n29   5936.196322\\n..           ...\\n905  5213.389228\\n906  5014.941636\\n907  4685.789839\\n908     0.000000\\n909  4154.652278\\n910  3857.897641\\n911  3824.348997\\n912  3772.552890\\n913  3924.595310\\n914  4097.525466\\n915     0.000000\\n916  5324.903650\\n917  4992.422573\\n918  4899.171080\\n919  4479.264016\\n920  4586.322775\\n921  4446.348342\\n922     0.000000\\n923  3906.095211\\n924  3786.133486\\n925  3776.057640\\n926  3701.543351\\n927  3782.033448\\n928  4206.353433\\n929     0.000000\\n930  5777.072901\\n931  5231.826641\\n932  4871.522631\\n933  4616.802657\\n934  5083.616595\\n\\n[935 rows x 1 columns], 2:               RF\\n0    6301.653071\\n1    6512.066582\\n2    5460.737801\\n3    4907.510546\\n4    2609.258951\\n5       0.000000\\n6    4004.262165\\n7    3947.826898\\n8    4649.660839\\n9    3881.791308\\n10   3914.496199\\n11   2532.131341\\n12      0.000000\\n13   6577.690528\\n14   5930.723979\\n15   6181.119323\\n16   5152.720556\\n17   4781.019769\\n18   2459.275038\\n19      0.000000\\n20   3930.218580\\n21   3846.602754\\n22   4484.417434\\n23   4041.144875\\n24   4190.369657\\n25   2832.103219\\n26      0.000000\\n27   7134.246191\\n28   6251.397279\\n29   6573.185582\\n..           ...\\n905  6035.497232\\n906  4886.428074\\n907  4120.382805\\n908     0.000000\\n909  4267.333932\\n910  4103.857473\\n911  5349.621996\\n912  4108.950937\\n913  4459.873069\\n914  2835.989755\\n915     0.000000\\n916  7330.428833\\n917  6269.335765\\n918  6614.322677\\n919  5320.681697\\n920  4782.608744\\n921  2934.398875\\n922     0.000000\\n923  4092.670546\\n924  4017.805717\\n925  5017.327983\\n926  4155.381621\\n927  4001.507758\\n928  2679.429521\\n929     0.000000\\n930  6950.855119\\n931  6019.527033\\n932  6332.774044\\n933  5389.226644\\n934  5374.480205\\n\\n[935 rows x 1 columns], 3:                RF\\n0     9865.725643\\n1     8556.524297\\n2     7735.243498\\n3     8600.125698\\n4     4299.631053\\n5        0.000000\\n6     5161.439033\\n7     5158.895142\\n8     5124.796593\\n9     5029.780984\\n10    5753.720775\\n11    4116.094082\\n12       0.000000\\n13    7323.494665\\n14    8106.196403\\n15    8337.048882\\n16    7665.836171\\n17    7538.104661\\n18    4088.689027\\n19       0.000000\\n20    5446.562407\\n21    5183.907182\\n22    5163.045673\\n23    5416.667457\\n24    6189.838469\\n25    4612.137004\\n26       0.000000\\n27   10929.090479\\n28    9665.616068\\n29    8697.226316\\n..            ...\\n905   7902.289963\\n906   7988.196339\\n907   4410.733220\\n908      0.000000\\n909   6583.588947\\n910   6274.687520\\n911   5927.730414\\n912   6039.235329\\n913   6377.259414\\n914   4145.209935\\n915      0.000000\\n916   9599.505054\\n917   9091.569615\\n918   8387.929174\\n919   7859.467803\\n920   7360.707932\\n921   4038.903843\\n922      0.000000\\n923   5717.155088\\n924   5744.375256\\n925   5609.368631\\n926   5621.820537\\n927   5706.721259\\n928   4132.804131\\n929      0.000000\\n930   9053.594415\\n931   8550.100507\\n932   7920.723981\\n933   7996.062017\\n934   8217.142946\\n\\n[935 rows x 1 columns], 4:                RF\\n0    10153.291251\\n1     9330.420316\\n2     9324.782816\\n3     9873.201129\\n4     9935.107310\\n5        0.000000\\n6     7940.504973\\n7     7462.205307\\n8     7195.689326\\n9     7370.564596\\n10    7714.182408\\n11    9505.201476\\n12       0.000000\\n13   11638.913468\\n14    9930.915505\\n15    9310.429981\\n16    9095.553007\\n17    9534.606713\\n18    9704.708709\\n19       0.000000\\n20    8014.219914\\n21    7436.751298\\n22    7559.677321\\n23    8834.882136\\n24    9313.044036\\n25   10422.648988\\n26       0.000000\\n27   13571.475418\\n28   11371.915256\\n29   10430.999778\\n..            ...\\n905  10766.941045\\n906  10946.982574\\n907  10041.923116\\n908      0.000000\\n909   9549.101372\\n910   8255.779199\\n911   8264.068984\\n912   8437.337135\\n913   9022.846871\\n914  10039.210983\\n915      0.000000\\n916  13243.130272\\n917  11481.103485\\n918  10864.947607\\n919  11059.489289\\n920  10792.118165\\n921   9906.893338\\n922      0.000000\\n923   9777.113586\\n924   8620.478162\\n925   8601.116822\\n926   8256.533655\\n927   8108.298185\\n928   9580.301873\\n929      0.000000\\n930  11887.306002\\n931  10547.283171\\n932  10113.888283\\n933  10665.673813\\n934  12010.950993\\n\\n[935 rows x 1 columns], 5:               RF\\n0    5602.367520\\n1    5765.830248\\n2    4944.436392\\n3    5069.712080\\n4    1835.048437\\n5       0.000000\\n6    3824.709259\\n7    3727.959016\\n8    3997.489338\\n9    3713.124132\\n10   4080.789181\\n11   1778.062763\\n12      0.000000\\n13   6713.288646\\n14   5657.912371\\n15   5723.656718\\n16   4884.909327\\n17   4964.245910\\n18   1711.137049\\n19      0.000000\\n20   3658.144368\\n21   3669.813283\\n22   3926.307934\\n23   4046.507613\\n24   4176.387447\\n25   1799.584141\\n26      0.000000\\n27   7298.005995\\n28   5685.897410\\n29   5646.041251\\n..           ...\\n905  5617.949743\\n906  5277.449924\\n907  2067.371890\\n908     0.000000\\n909  4201.345590\\n910  3924.272999\\n911  4497.518631\\n912  4343.472950\\n913  4471.560799\\n914  2045.552030\\n915     0.000000\\n916  7616.076761\\n917  6135.664120\\n918  5994.740921\\n919  5316.122152\\n920  4994.822482\\n921  2012.750604\\n922     0.000000\\n923  3977.545251\\n924  3852.728501\\n925  4104.832942\\n926  3849.910591\\n927  4077.971237\\n928  2027.489989\\n929     0.000000\\n930  7348.909395\\n931  5916.736549\\n932  5961.897794\\n933  5061.086620\\n934  5000.425974\\n\\n[935 rows x 1 columns], 6:               RF\\n0    7176.216977\\n1    6921.244257\\n2    6314.656802\\n3    6927.614117\\n4    3765.943200\\n5       0.000000\\n6    5972.879755\\n7    5616.944218\\n8    5247.771443\\n9    5554.787497\\n10   5715.102832\\n11   3646.093786\\n12      0.000000\\n13   7280.665828\\n14   7011.165911\\n15   6988.189735\\n16   6215.657925\\n17   6586.677830\\n18   3568.620140\\n19      0.000000\\n20   5887.256261\\n21   5565.052033\\n22   5685.134009\\n23   5951.143558\\n24   6331.763843\\n25   3951.130302\\n26      0.000000\\n27   7995.748875\\n28   7163.679662\\n29   6959.841448\\n..           ...\\n905  5380.294432\\n906  5175.286138\\n907  3076.216048\\n908     0.000000\\n909  4434.751308\\n910  4177.535324\\n911  4214.410605\\n912  4023.589826\\n913  4318.281577\\n914  3117.880892\\n915     0.000000\\n916  6735.486192\\n917  6087.241839\\n918  5053.072887\\n919  5073.856439\\n920  4938.491404\\n921  3030.731841\\n922     0.000000\\n923  4196.594408\\n924  4003.904757\\n925  3873.525437\\n926  3857.727704\\n927  3945.741744\\n928  2983.708094\\n929     0.000000\\n930  6058.022162\\n931  5122.596222\\n932  5162.963689\\n933  5021.390646\\n934  5325.109168\\n\\n[935 rows x 1 columns], 7:                RF\\n0    10341.561739\\n1     8090.852354\\n2     8066.825115\\n3     8099.700803\\n4     4538.281949\\n5        0.000000\\n6     6844.887051\\n7     6807.330886\\n8     6372.850673\\n9     6780.353349\\n10    7574.897096\\n11    4818.921211\\n12       0.000000\\n13   10721.421378\\n14    9260.975655\\n15    8047.107928\\n16    7918.644043\\n17    8398.576603\\n18    4415.173737\\n19       0.000000\\n20    6806.083194\\n21    6627.681069\\n22    6225.985349\\n23    6834.920104\\n24    8366.987540\\n25    5680.325548\\n26       0.000000\\n27   11304.371647\\n28    9967.504627\\n29    8315.717458\\n..            ...\\n905  10822.738936\\n906  11382.761888\\n907   6953.899715\\n908      0.000000\\n909  10547.962700\\n910   9569.668532\\n911   8689.177902\\n912   9130.450870\\n913   9451.756835\\n914   6371.202761\\n915      0.000000\\n916  13563.049386\\n917  11890.357178\\n918  10397.356752\\n919  10183.213753\\n920  10843.655809\\n921   6663.896457\\n922      0.000000\\n923   9900.887575\\n924   9247.850161\\n925   8520.033795\\n926   8971.163264\\n927   9296.166432\\n928   6570.800437\\n929      0.000000\\n930  14462.460051\\n931  12411.801620\\n932  11500.157129\\n933  11695.859036\\n934  13403.592858\\n\\n[935 rows x 1 columns], 8:               RF\\n0    6505.311482\\n1    5673.728950\\n2    6234.547054\\n3    5206.362083\\n4    2510.754229\\n5       0.000000\\n6    4286.980709\\n7    4192.341320\\n8    3895.344289\\n9    4827.428066\\n10   3990.749414\\n11   2425.080131\\n12      0.000000\\n13   7750.308633\\n14   6376.083705\\n15   5940.518205\\n16   6436.209779\\n17   5148.807489\\n18   2419.843968\\n19      0.000000\\n20   4142.699459\\n21   4038.122900\\n22   3921.820116\\n23   5078.175921\\n24   4269.046174\\n25   2466.148751\\n26      0.000000\\n27   8116.772377\\n28   6986.286160\\n29   6040.836447\\n..           ...\\n905  8741.049105\\n906  6928.788711\\n907  3766.389294\\n908     0.000000\\n909  6609.404624\\n910  5484.447680\\n911  5288.193240\\n912  5723.362419\\n913  5687.426422\\n914  3582.391384\\n915     0.000000\\n916  9226.678566\\n917  8292.151928\\n918  7908.693575\\n919  8451.363564\\n920  6967.373759\\n921  3839.242942\\n922     0.000000\\n923  5865.544546\\n924  5392.265065\\n925  5224.913094\\n926  5890.586562\\n927  5104.082701\\n928  3381.496860\\n929     0.000000\\n930  9011.301879\\n931  7410.610427\\n932  6719.584642\\n933  7922.431776\\n934  7637.432271\\n\\n[935 rows x 1 columns], 9:               MLP\\n0     6279.433594\\n1     5267.011719\\n2     5368.332520\\n3     5712.226562\\n4     4950.395996\\n5       -0.028284\\n6     4488.551758\\n7     4160.208984\\n8     3787.561279\\n9     4408.336914\\n10    4940.951172\\n11    5140.651855\\n12      -0.053045\\n13    6982.203125\\n14    5334.626953\\n15    4900.089844\\n16    5052.441895\\n17    5570.834961\\n18    5017.503906\\n19      -0.043465\\n20    4208.080078\\n21    4126.518555\\n22    4042.392578\\n23    4492.574707\\n24    4952.820801\\n25    5156.205078\\n26      -0.056809\\n27    7480.227051\\n28    6306.048828\\n29    5669.838379\\n..            ...\\n905   8682.273438\\n906   8371.239258\\n907   7388.477539\\n908     -0.312140\\n909   7200.276367\\n910   6700.878418\\n911   5747.479980\\n912   6279.598633\\n913   5784.897949\\n914   6730.434082\\n915     -0.007689\\n916  10629.220703\\n917   8306.646484\\n918   7187.606445\\n919   7877.063477\\n920   7837.359375\\n921   6768.887695\\n922     -0.103678\\n923   7160.478516\\n924   6488.474609\\n925   5598.430664\\n926   6183.783691\\n927   5932.015625\\n928   6903.274414\\n929     -0.029949\\n930  10662.451172\\n931   8412.494141\\n932   7479.281250\\n933   7917.981445\\n934   8125.443359\\n\\n[935 rows x 1 columns], 10:               RF\\n0    5714.657222\\n1    5539.029505\\n2    5616.178584\\n3    5606.811352\\n4    4541.323872\\n5       0.000000\\n6    4817.428118\\n7    4569.376346\\n8    4551.669918\\n9    4520.857199\\n10   4764.123412\\n11   4502.695003\\n12      0.000000\\n13   6657.925012\\n14   5405.284997\\n15   5270.986395\\n16   4981.103498\\n17   5377.504432\\n18   4662.733382\\n19      0.000000\\n20   4759.075375\\n21   4564.614294\\n22   4400.597774\\n23   4499.291151\\n24   4736.435904\\n25   4463.062521\\n26      0.000000\\n27   7523.753491\\n28   5686.595199\\n29   5353.215461\\n..           ...\\n905  6677.128553\\n906  5984.225171\\n907  5283.045256\\n908     0.000000\\n909  5968.697339\\n910  5512.063899\\n911  5213.497878\\n912  5006.810095\\n913  5446.198215\\n914  4832.357916\\n915     0.000000\\n916  7090.862208\\n917  6744.458716\\n918  6433.046021\\n919  6144.978505\\n920  5878.499621\\n921  5316.732486\\n922     0.000000\\n923  5860.148945\\n924  5814.462640\\n925  5406.251321\\n926  5830.192415\\n927  5772.662940\\n928  5014.186982\\n929     0.000000\\n930  7200.924160\\n931  6305.517632\\n932  5842.721550\\n933  5974.443251\\n934  6551.357368\\n\\n[935 rows x 1 columns]}'"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>191.9</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>215.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>304.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>361.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>197.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>189.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>352.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>201.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>392.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>198.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0\n",
       "1   191.9\n",
       "2   215.9\n",
       "3   304.6\n",
       "4   361.3\n",
       "5   197.2\n",
       "6   189.9\n",
       "7   352.6\n",
       "8   201.5\n",
       "9   392.5\n",
       "10  198.8"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#MAE of Best Model forecasting over all 935 days.  NN do very poorly here!\n",
    "mae2=[]\n",
    "for sku in list(df_dict.keys()):\n",
    "    mae2.append( mean_absolute_error(y[sku], bm_dict[sku])   )\n",
    "mae2=pd.DataFrame(mae2)\n",
    "mae2.index = mae2.index + 1\n",
    "mae2.round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5717.818209</td>\n",
       "      <td>6301.653071</td>\n",
       "      <td>9865.725643</td>\n",
       "      <td>10153.291251</td>\n",
       "      <td>5602.367520</td>\n",
       "      <td>7176.216977</td>\n",
       "      <td>10341.561739</td>\n",
       "      <td>6505.311482</td>\n",
       "      <td>6279.433594</td>\n",
       "      <td>5714.657222</td>\n",
       "      <td>73658.036707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5510.298604</td>\n",
       "      <td>6512.066582</td>\n",
       "      <td>8556.524297</td>\n",
       "      <td>9330.420316</td>\n",
       "      <td>5765.830248</td>\n",
       "      <td>6921.244257</td>\n",
       "      <td>8090.852354</td>\n",
       "      <td>5673.728950</td>\n",
       "      <td>5267.011719</td>\n",
       "      <td>5539.029505</td>\n",
       "      <td>67167.006832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5079.047790</td>\n",
       "      <td>5460.737801</td>\n",
       "      <td>7735.243498</td>\n",
       "      <td>9324.782816</td>\n",
       "      <td>4944.436392</td>\n",
       "      <td>6314.656802</td>\n",
       "      <td>8066.825115</td>\n",
       "      <td>6234.547054</td>\n",
       "      <td>5368.332520</td>\n",
       "      <td>5616.178584</td>\n",
       "      <td>64144.788373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4855.066339</td>\n",
       "      <td>4907.510546</td>\n",
       "      <td>8600.125698</td>\n",
       "      <td>9873.201129</td>\n",
       "      <td>5069.712080</td>\n",
       "      <td>6927.614117</td>\n",
       "      <td>8099.700803</td>\n",
       "      <td>5206.362083</td>\n",
       "      <td>5712.226562</td>\n",
       "      <td>5606.811352</td>\n",
       "      <td>64858.330710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4848.817431</td>\n",
       "      <td>2609.258951</td>\n",
       "      <td>4299.631053</td>\n",
       "      <td>9935.107310</td>\n",
       "      <td>1835.048437</td>\n",
       "      <td>3765.943200</td>\n",
       "      <td>4538.281949</td>\n",
       "      <td>2510.754229</td>\n",
       "      <td>4950.395996</td>\n",
       "      <td>4541.323872</td>\n",
       "      <td>43834.562428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.028284</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.028284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4364.450817</td>\n",
       "      <td>4004.262165</td>\n",
       "      <td>5161.439033</td>\n",
       "      <td>7940.504973</td>\n",
       "      <td>3824.709259</td>\n",
       "      <td>5972.879755</td>\n",
       "      <td>6844.887051</td>\n",
       "      <td>4286.980709</td>\n",
       "      <td>4488.551758</td>\n",
       "      <td>4817.428118</td>\n",
       "      <td>51706.093638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4031.301953</td>\n",
       "      <td>3947.826898</td>\n",
       "      <td>5158.895142</td>\n",
       "      <td>7462.205307</td>\n",
       "      <td>3727.959016</td>\n",
       "      <td>5616.944218</td>\n",
       "      <td>6807.330886</td>\n",
       "      <td>4192.341320</td>\n",
       "      <td>4160.208984</td>\n",
       "      <td>4569.376346</td>\n",
       "      <td>49674.390071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3944.842050</td>\n",
       "      <td>4649.660839</td>\n",
       "      <td>5124.796593</td>\n",
       "      <td>7195.689326</td>\n",
       "      <td>3997.489338</td>\n",
       "      <td>5247.771443</td>\n",
       "      <td>6372.850673</td>\n",
       "      <td>3895.344289</td>\n",
       "      <td>3787.561279</td>\n",
       "      <td>4551.669918</td>\n",
       "      <td>48767.675748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3894.189293</td>\n",
       "      <td>3881.791308</td>\n",
       "      <td>5029.780984</td>\n",
       "      <td>7370.564596</td>\n",
       "      <td>3713.124132</td>\n",
       "      <td>5554.787497</td>\n",
       "      <td>6780.353349</td>\n",
       "      <td>4827.428066</td>\n",
       "      <td>4408.336914</td>\n",
       "      <td>4520.857199</td>\n",
       "      <td>49981.213338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4245.188187</td>\n",
       "      <td>3914.496199</td>\n",
       "      <td>5753.720775</td>\n",
       "      <td>7714.182408</td>\n",
       "      <td>4080.789181</td>\n",
       "      <td>5715.102832</td>\n",
       "      <td>7574.897096</td>\n",
       "      <td>3990.749414</td>\n",
       "      <td>4940.951172</td>\n",
       "      <td>4764.123412</td>\n",
       "      <td>52694.200677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4983.633181</td>\n",
       "      <td>2532.131341</td>\n",
       "      <td>4116.094082</td>\n",
       "      <td>9505.201476</td>\n",
       "      <td>1778.062763</td>\n",
       "      <td>3646.093786</td>\n",
       "      <td>4818.921211</td>\n",
       "      <td>2425.080131</td>\n",
       "      <td>5140.651855</td>\n",
       "      <td>4502.695003</td>\n",
       "      <td>43448.564827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.053045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.053045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5744.192988</td>\n",
       "      <td>6577.690528</td>\n",
       "      <td>7323.494665</td>\n",
       "      <td>11638.913468</td>\n",
       "      <td>6713.288646</td>\n",
       "      <td>7280.665828</td>\n",
       "      <td>10721.421378</td>\n",
       "      <td>7750.308633</td>\n",
       "      <td>6982.203125</td>\n",
       "      <td>6657.925012</td>\n",
       "      <td>77390.104271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5223.222718</td>\n",
       "      <td>5930.723979</td>\n",
       "      <td>8106.196403</td>\n",
       "      <td>9930.915505</td>\n",
       "      <td>5657.912371</td>\n",
       "      <td>7011.165911</td>\n",
       "      <td>9260.975655</td>\n",
       "      <td>6376.083705</td>\n",
       "      <td>5334.626953</td>\n",
       "      <td>5405.284997</td>\n",
       "      <td>68237.108196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5362.314867</td>\n",
       "      <td>6181.119323</td>\n",
       "      <td>8337.048882</td>\n",
       "      <td>9310.429981</td>\n",
       "      <td>5723.656718</td>\n",
       "      <td>6988.189735</td>\n",
       "      <td>8047.107928</td>\n",
       "      <td>5940.518205</td>\n",
       "      <td>4900.089844</td>\n",
       "      <td>5270.986395</td>\n",
       "      <td>66061.461878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5163.339575</td>\n",
       "      <td>5152.720556</td>\n",
       "      <td>7665.836171</td>\n",
       "      <td>9095.553007</td>\n",
       "      <td>4884.909327</td>\n",
       "      <td>6215.657925</td>\n",
       "      <td>7918.644043</td>\n",
       "      <td>6436.209779</td>\n",
       "      <td>5052.441895</td>\n",
       "      <td>4981.103498</td>\n",
       "      <td>62566.415776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5257.620845</td>\n",
       "      <td>4781.019769</td>\n",
       "      <td>7538.104661</td>\n",
       "      <td>9534.606713</td>\n",
       "      <td>4964.245910</td>\n",
       "      <td>6586.677830</td>\n",
       "      <td>8398.576603</td>\n",
       "      <td>5148.807489</td>\n",
       "      <td>5570.834961</td>\n",
       "      <td>5377.504432</td>\n",
       "      <td>63157.999214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5379.716069</td>\n",
       "      <td>2459.275038</td>\n",
       "      <td>4088.689027</td>\n",
       "      <td>9704.708709</td>\n",
       "      <td>1711.137049</td>\n",
       "      <td>3568.620140</td>\n",
       "      <td>4415.173737</td>\n",
       "      <td>2419.843968</td>\n",
       "      <td>5017.503906</td>\n",
       "      <td>4662.733382</td>\n",
       "      <td>43427.401026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.043465</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.043465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4208.318142</td>\n",
       "      <td>3930.218580</td>\n",
       "      <td>5446.562407</td>\n",
       "      <td>8014.219914</td>\n",
       "      <td>3658.144368</td>\n",
       "      <td>5887.256261</td>\n",
       "      <td>6806.083194</td>\n",
       "      <td>4142.699459</td>\n",
       "      <td>4208.080078</td>\n",
       "      <td>4759.075375</td>\n",
       "      <td>51060.657780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3888.350691</td>\n",
       "      <td>3846.602754</td>\n",
       "      <td>5183.907182</td>\n",
       "      <td>7436.751298</td>\n",
       "      <td>3669.813283</td>\n",
       "      <td>5565.052033</td>\n",
       "      <td>6627.681069</td>\n",
       "      <td>4038.122900</td>\n",
       "      <td>4126.518555</td>\n",
       "      <td>4564.614294</td>\n",
       "      <td>48947.414060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4108.486962</td>\n",
       "      <td>4484.417434</td>\n",
       "      <td>5163.045673</td>\n",
       "      <td>7559.677321</td>\n",
       "      <td>3926.307934</td>\n",
       "      <td>5685.134009</td>\n",
       "      <td>6225.985349</td>\n",
       "      <td>3921.820116</td>\n",
       "      <td>4042.392578</td>\n",
       "      <td>4400.597774</td>\n",
       "      <td>49517.865151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4515.580422</td>\n",
       "      <td>4041.144875</td>\n",
       "      <td>5416.667457</td>\n",
       "      <td>8834.882136</td>\n",
       "      <td>4046.507613</td>\n",
       "      <td>5951.143558</td>\n",
       "      <td>6834.920104</td>\n",
       "      <td>5078.175921</td>\n",
       "      <td>4492.574707</td>\n",
       "      <td>4499.291151</td>\n",
       "      <td>53710.887945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5273.183988</td>\n",
       "      <td>4190.369657</td>\n",
       "      <td>6189.838469</td>\n",
       "      <td>9313.044036</td>\n",
       "      <td>4176.387447</td>\n",
       "      <td>6331.763843</td>\n",
       "      <td>8366.987540</td>\n",
       "      <td>4269.046174</td>\n",
       "      <td>4952.820801</td>\n",
       "      <td>4736.435904</td>\n",
       "      <td>57799.877859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5711.538596</td>\n",
       "      <td>2832.103219</td>\n",
       "      <td>4612.137004</td>\n",
       "      <td>10422.648988</td>\n",
       "      <td>1799.584141</td>\n",
       "      <td>3951.130302</td>\n",
       "      <td>5680.325548</td>\n",
       "      <td>2466.148751</td>\n",
       "      <td>5156.205078</td>\n",
       "      <td>4463.062521</td>\n",
       "      <td>47094.884149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.056809</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.056809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>6906.761140</td>\n",
       "      <td>7134.246191</td>\n",
       "      <td>10929.090479</td>\n",
       "      <td>13571.475418</td>\n",
       "      <td>7298.005995</td>\n",
       "      <td>7995.748875</td>\n",
       "      <td>11304.371647</td>\n",
       "      <td>8116.772377</td>\n",
       "      <td>7480.227051</td>\n",
       "      <td>7523.753491</td>\n",
       "      <td>88260.452664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>6097.723153</td>\n",
       "      <td>6251.397279</td>\n",
       "      <td>9665.616068</td>\n",
       "      <td>11371.915256</td>\n",
       "      <td>5685.897410</td>\n",
       "      <td>7163.679662</td>\n",
       "      <td>9967.504627</td>\n",
       "      <td>6986.286160</td>\n",
       "      <td>6306.048828</td>\n",
       "      <td>5686.595199</td>\n",
       "      <td>75182.663642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5936.196322</td>\n",
       "      <td>6573.185582</td>\n",
       "      <td>8697.226316</td>\n",
       "      <td>10430.999778</td>\n",
       "      <td>5646.041251</td>\n",
       "      <td>6959.841448</td>\n",
       "      <td>8315.717458</td>\n",
       "      <td>6040.836447</td>\n",
       "      <td>5669.838379</td>\n",
       "      <td>5353.215461</td>\n",
       "      <td>69623.098443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>5213.389228</td>\n",
       "      <td>6035.497232</td>\n",
       "      <td>7902.289963</td>\n",
       "      <td>10766.941045</td>\n",
       "      <td>5617.949743</td>\n",
       "      <td>5380.294432</td>\n",
       "      <td>10822.738936</td>\n",
       "      <td>8741.049105</td>\n",
       "      <td>8682.273438</td>\n",
       "      <td>6677.128553</td>\n",
       "      <td>75839.551675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>5014.941636</td>\n",
       "      <td>4886.428074</td>\n",
       "      <td>7988.196339</td>\n",
       "      <td>10946.982574</td>\n",
       "      <td>5277.449924</td>\n",
       "      <td>5175.286138</td>\n",
       "      <td>11382.761888</td>\n",
       "      <td>6928.788711</td>\n",
       "      <td>8371.239258</td>\n",
       "      <td>5984.225171</td>\n",
       "      <td>71956.299711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>907</th>\n",
       "      <td>4685.789839</td>\n",
       "      <td>4120.382805</td>\n",
       "      <td>4410.733220</td>\n",
       "      <td>10041.923116</td>\n",
       "      <td>2067.371890</td>\n",
       "      <td>3076.216048</td>\n",
       "      <td>6953.899715</td>\n",
       "      <td>3766.389294</td>\n",
       "      <td>7388.477539</td>\n",
       "      <td>5283.045256</td>\n",
       "      <td>51794.228721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.312140</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.312140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>4154.652278</td>\n",
       "      <td>4267.333932</td>\n",
       "      <td>6583.588947</td>\n",
       "      <td>9549.101372</td>\n",
       "      <td>4201.345590</td>\n",
       "      <td>4434.751308</td>\n",
       "      <td>10547.962700</td>\n",
       "      <td>6609.404624</td>\n",
       "      <td>7200.276367</td>\n",
       "      <td>5968.697339</td>\n",
       "      <td>63517.114457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>3857.897641</td>\n",
       "      <td>4103.857473</td>\n",
       "      <td>6274.687520</td>\n",
       "      <td>8255.779199</td>\n",
       "      <td>3924.272999</td>\n",
       "      <td>4177.535324</td>\n",
       "      <td>9569.668532</td>\n",
       "      <td>5484.447680</td>\n",
       "      <td>6700.878418</td>\n",
       "      <td>5512.063899</td>\n",
       "      <td>57861.088683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>3824.348997</td>\n",
       "      <td>5349.621996</td>\n",
       "      <td>5927.730414</td>\n",
       "      <td>8264.068984</td>\n",
       "      <td>4497.518631</td>\n",
       "      <td>4214.410605</td>\n",
       "      <td>8689.177902</td>\n",
       "      <td>5288.193240</td>\n",
       "      <td>5747.479980</td>\n",
       "      <td>5213.497878</td>\n",
       "      <td>57016.048627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912</th>\n",
       "      <td>3772.552890</td>\n",
       "      <td>4108.950937</td>\n",
       "      <td>6039.235329</td>\n",
       "      <td>8437.337135</td>\n",
       "      <td>4343.472950</td>\n",
       "      <td>4023.589826</td>\n",
       "      <td>9130.450870</td>\n",
       "      <td>5723.362419</td>\n",
       "      <td>6279.598633</td>\n",
       "      <td>5006.810095</td>\n",
       "      <td>56865.361084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>3924.595310</td>\n",
       "      <td>4459.873069</td>\n",
       "      <td>6377.259414</td>\n",
       "      <td>9022.846871</td>\n",
       "      <td>4471.560799</td>\n",
       "      <td>4318.281577</td>\n",
       "      <td>9451.756835</td>\n",
       "      <td>5687.426422</td>\n",
       "      <td>5784.897949</td>\n",
       "      <td>5446.198215</td>\n",
       "      <td>58944.696459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>4097.525466</td>\n",
       "      <td>2835.989755</td>\n",
       "      <td>4145.209935</td>\n",
       "      <td>10039.210983</td>\n",
       "      <td>2045.552030</td>\n",
       "      <td>3117.880892</td>\n",
       "      <td>6371.202761</td>\n",
       "      <td>3582.391384</td>\n",
       "      <td>6730.434082</td>\n",
       "      <td>4832.357916</td>\n",
       "      <td>47797.755205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.007689</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.007689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>5324.903650</td>\n",
       "      <td>7330.428833</td>\n",
       "      <td>9599.505054</td>\n",
       "      <td>13243.130272</td>\n",
       "      <td>7616.076761</td>\n",
       "      <td>6735.486192</td>\n",
       "      <td>13563.049386</td>\n",
       "      <td>9226.678566</td>\n",
       "      <td>10629.220703</td>\n",
       "      <td>7090.862208</td>\n",
       "      <td>90359.341626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>4992.422573</td>\n",
       "      <td>6269.335765</td>\n",
       "      <td>9091.569615</td>\n",
       "      <td>11481.103485</td>\n",
       "      <td>6135.664120</td>\n",
       "      <td>6087.241839</td>\n",
       "      <td>11890.357178</td>\n",
       "      <td>8292.151928</td>\n",
       "      <td>8306.646484</td>\n",
       "      <td>6744.458716</td>\n",
       "      <td>79290.951703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>4899.171080</td>\n",
       "      <td>6614.322677</td>\n",
       "      <td>8387.929174</td>\n",
       "      <td>10864.947607</td>\n",
       "      <td>5994.740921</td>\n",
       "      <td>5053.072887</td>\n",
       "      <td>10397.356752</td>\n",
       "      <td>7908.693575</td>\n",
       "      <td>7187.606445</td>\n",
       "      <td>6433.046021</td>\n",
       "      <td>73740.887140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>4479.264016</td>\n",
       "      <td>5320.681697</td>\n",
       "      <td>7859.467803</td>\n",
       "      <td>11059.489289</td>\n",
       "      <td>5316.122152</td>\n",
       "      <td>5073.856439</td>\n",
       "      <td>10183.213753</td>\n",
       "      <td>8451.363564</td>\n",
       "      <td>7877.063477</td>\n",
       "      <td>6144.978505</td>\n",
       "      <td>71765.500694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>4586.322775</td>\n",
       "      <td>4782.608744</td>\n",
       "      <td>7360.707932</td>\n",
       "      <td>10792.118165</td>\n",
       "      <td>4994.822482</td>\n",
       "      <td>4938.491404</td>\n",
       "      <td>10843.655809</td>\n",
       "      <td>6967.373759</td>\n",
       "      <td>7837.359375</td>\n",
       "      <td>5878.499621</td>\n",
       "      <td>68981.960067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>4446.348342</td>\n",
       "      <td>2934.398875</td>\n",
       "      <td>4038.903843</td>\n",
       "      <td>9906.893338</td>\n",
       "      <td>2012.750604</td>\n",
       "      <td>3030.731841</td>\n",
       "      <td>6663.896457</td>\n",
       "      <td>3839.242942</td>\n",
       "      <td>6768.887695</td>\n",
       "      <td>5316.732486</td>\n",
       "      <td>48958.786423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.103678</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.103678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>3906.095211</td>\n",
       "      <td>4092.670546</td>\n",
       "      <td>5717.155088</td>\n",
       "      <td>9777.113586</td>\n",
       "      <td>3977.545251</td>\n",
       "      <td>4196.594408</td>\n",
       "      <td>9900.887575</td>\n",
       "      <td>5865.544546</td>\n",
       "      <td>7160.478516</td>\n",
       "      <td>5860.148945</td>\n",
       "      <td>60454.233673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>3786.133486</td>\n",
       "      <td>4017.805717</td>\n",
       "      <td>5744.375256</td>\n",
       "      <td>8620.478162</td>\n",
       "      <td>3852.728501</td>\n",
       "      <td>4003.904757</td>\n",
       "      <td>9247.850161</td>\n",
       "      <td>5392.265065</td>\n",
       "      <td>6488.474609</td>\n",
       "      <td>5814.462640</td>\n",
       "      <td>56968.478354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>3776.057640</td>\n",
       "      <td>5017.327983</td>\n",
       "      <td>5609.368631</td>\n",
       "      <td>8601.116822</td>\n",
       "      <td>4104.832942</td>\n",
       "      <td>3873.525437</td>\n",
       "      <td>8520.033795</td>\n",
       "      <td>5224.913094</td>\n",
       "      <td>5598.430664</td>\n",
       "      <td>5406.251321</td>\n",
       "      <td>55731.858328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>3701.543351</td>\n",
       "      <td>4155.381621</td>\n",
       "      <td>5621.820537</td>\n",
       "      <td>8256.533655</td>\n",
       "      <td>3849.910591</td>\n",
       "      <td>3857.727704</td>\n",
       "      <td>8971.163264</td>\n",
       "      <td>5890.586562</td>\n",
       "      <td>6183.783691</td>\n",
       "      <td>5830.192415</td>\n",
       "      <td>56318.643391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>3782.033448</td>\n",
       "      <td>4001.507758</td>\n",
       "      <td>5706.721259</td>\n",
       "      <td>8108.298185</td>\n",
       "      <td>4077.971237</td>\n",
       "      <td>3945.741744</td>\n",
       "      <td>9296.166432</td>\n",
       "      <td>5104.082701</td>\n",
       "      <td>5932.015625</td>\n",
       "      <td>5772.662940</td>\n",
       "      <td>55727.201329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>928</th>\n",
       "      <td>4206.353433</td>\n",
       "      <td>2679.429521</td>\n",
       "      <td>4132.804131</td>\n",
       "      <td>9580.301873</td>\n",
       "      <td>2027.489989</td>\n",
       "      <td>2983.708094</td>\n",
       "      <td>6570.800437</td>\n",
       "      <td>3381.496860</td>\n",
       "      <td>6903.274414</td>\n",
       "      <td>5014.186982</td>\n",
       "      <td>47479.845734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.029949</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.029949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>5777.072901</td>\n",
       "      <td>6950.855119</td>\n",
       "      <td>9053.594415</td>\n",
       "      <td>11887.306002</td>\n",
       "      <td>7348.909395</td>\n",
       "      <td>6058.022162</td>\n",
       "      <td>14462.460051</td>\n",
       "      <td>9011.301879</td>\n",
       "      <td>10662.451172</td>\n",
       "      <td>7200.924160</td>\n",
       "      <td>88412.897257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>5231.826641</td>\n",
       "      <td>6019.527033</td>\n",
       "      <td>8550.100507</td>\n",
       "      <td>10547.283171</td>\n",
       "      <td>5916.736549</td>\n",
       "      <td>5122.596222</td>\n",
       "      <td>12411.801620</td>\n",
       "      <td>7410.610427</td>\n",
       "      <td>8412.494141</td>\n",
       "      <td>6305.517632</td>\n",
       "      <td>75928.493945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>4871.522631</td>\n",
       "      <td>6332.774044</td>\n",
       "      <td>7920.723981</td>\n",
       "      <td>10113.888283</td>\n",
       "      <td>5961.897794</td>\n",
       "      <td>5162.963689</td>\n",
       "      <td>11500.157129</td>\n",
       "      <td>6719.584642</td>\n",
       "      <td>7479.281250</td>\n",
       "      <td>5842.721550</td>\n",
       "      <td>71905.514993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>4616.802657</td>\n",
       "      <td>5389.226644</td>\n",
       "      <td>7996.062017</td>\n",
       "      <td>10665.673813</td>\n",
       "      <td>5061.086620</td>\n",
       "      <td>5021.390646</td>\n",
       "      <td>11695.859036</td>\n",
       "      <td>7922.431776</td>\n",
       "      <td>7917.981445</td>\n",
       "      <td>5974.443251</td>\n",
       "      <td>72260.957905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>5083.616595</td>\n",
       "      <td>5374.480205</td>\n",
       "      <td>8217.142946</td>\n",
       "      <td>12010.950993</td>\n",
       "      <td>5000.425974</td>\n",
       "      <td>5325.109168</td>\n",
       "      <td>13403.592858</td>\n",
       "      <td>7637.432271</td>\n",
       "      <td>8125.443359</td>\n",
       "      <td>6551.357368</td>\n",
       "      <td>76729.551737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>935 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               1            2             3             4            5  \\\n",
       "0    5717.818209  6301.653071   9865.725643  10153.291251  5602.367520   \n",
       "1    5510.298604  6512.066582   8556.524297   9330.420316  5765.830248   \n",
       "2    5079.047790  5460.737801   7735.243498   9324.782816  4944.436392   \n",
       "3    4855.066339  4907.510546   8600.125698   9873.201129  5069.712080   \n",
       "4    4848.817431  2609.258951   4299.631053   9935.107310  1835.048437   \n",
       "5       0.000000     0.000000      0.000000      0.000000     0.000000   \n",
       "6    4364.450817  4004.262165   5161.439033   7940.504973  3824.709259   \n",
       "7    4031.301953  3947.826898   5158.895142   7462.205307  3727.959016   \n",
       "8    3944.842050  4649.660839   5124.796593   7195.689326  3997.489338   \n",
       "9    3894.189293  3881.791308   5029.780984   7370.564596  3713.124132   \n",
       "10   4245.188187  3914.496199   5753.720775   7714.182408  4080.789181   \n",
       "11   4983.633181  2532.131341   4116.094082   9505.201476  1778.062763   \n",
       "12      0.000000     0.000000      0.000000      0.000000     0.000000   \n",
       "13   5744.192988  6577.690528   7323.494665  11638.913468  6713.288646   \n",
       "14   5223.222718  5930.723979   8106.196403   9930.915505  5657.912371   \n",
       "15   5362.314867  6181.119323   8337.048882   9310.429981  5723.656718   \n",
       "16   5163.339575  5152.720556   7665.836171   9095.553007  4884.909327   \n",
       "17   5257.620845  4781.019769   7538.104661   9534.606713  4964.245910   \n",
       "18   5379.716069  2459.275038   4088.689027   9704.708709  1711.137049   \n",
       "19      0.000000     0.000000      0.000000      0.000000     0.000000   \n",
       "20   4208.318142  3930.218580   5446.562407   8014.219914  3658.144368   \n",
       "21   3888.350691  3846.602754   5183.907182   7436.751298  3669.813283   \n",
       "22   4108.486962  4484.417434   5163.045673   7559.677321  3926.307934   \n",
       "23   4515.580422  4041.144875   5416.667457   8834.882136  4046.507613   \n",
       "24   5273.183988  4190.369657   6189.838469   9313.044036  4176.387447   \n",
       "25   5711.538596  2832.103219   4612.137004  10422.648988  1799.584141   \n",
       "26      0.000000     0.000000      0.000000      0.000000     0.000000   \n",
       "27   6906.761140  7134.246191  10929.090479  13571.475418  7298.005995   \n",
       "28   6097.723153  6251.397279   9665.616068  11371.915256  5685.897410   \n",
       "29   5936.196322  6573.185582   8697.226316  10430.999778  5646.041251   \n",
       "..           ...          ...           ...           ...          ...   \n",
       "905  5213.389228  6035.497232   7902.289963  10766.941045  5617.949743   \n",
       "906  5014.941636  4886.428074   7988.196339  10946.982574  5277.449924   \n",
       "907  4685.789839  4120.382805   4410.733220  10041.923116  2067.371890   \n",
       "908     0.000000     0.000000      0.000000      0.000000     0.000000   \n",
       "909  4154.652278  4267.333932   6583.588947   9549.101372  4201.345590   \n",
       "910  3857.897641  4103.857473   6274.687520   8255.779199  3924.272999   \n",
       "911  3824.348997  5349.621996   5927.730414   8264.068984  4497.518631   \n",
       "912  3772.552890  4108.950937   6039.235329   8437.337135  4343.472950   \n",
       "913  3924.595310  4459.873069   6377.259414   9022.846871  4471.560799   \n",
       "914  4097.525466  2835.989755   4145.209935  10039.210983  2045.552030   \n",
       "915     0.000000     0.000000      0.000000      0.000000     0.000000   \n",
       "916  5324.903650  7330.428833   9599.505054  13243.130272  7616.076761   \n",
       "917  4992.422573  6269.335765   9091.569615  11481.103485  6135.664120   \n",
       "918  4899.171080  6614.322677   8387.929174  10864.947607  5994.740921   \n",
       "919  4479.264016  5320.681697   7859.467803  11059.489289  5316.122152   \n",
       "920  4586.322775  4782.608744   7360.707932  10792.118165  4994.822482   \n",
       "921  4446.348342  2934.398875   4038.903843   9906.893338  2012.750604   \n",
       "922     0.000000     0.000000      0.000000      0.000000     0.000000   \n",
       "923  3906.095211  4092.670546   5717.155088   9777.113586  3977.545251   \n",
       "924  3786.133486  4017.805717   5744.375256   8620.478162  3852.728501   \n",
       "925  3776.057640  5017.327983   5609.368631   8601.116822  4104.832942   \n",
       "926  3701.543351  4155.381621   5621.820537   8256.533655  3849.910591   \n",
       "927  3782.033448  4001.507758   5706.721259   8108.298185  4077.971237   \n",
       "928  4206.353433  2679.429521   4132.804131   9580.301873  2027.489989   \n",
       "929     0.000000     0.000000      0.000000      0.000000     0.000000   \n",
       "930  5777.072901  6950.855119   9053.594415  11887.306002  7348.909395   \n",
       "931  5231.826641  6019.527033   8550.100507  10547.283171  5916.736549   \n",
       "932  4871.522631  6332.774044   7920.723981  10113.888283  5961.897794   \n",
       "933  4616.802657  5389.226644   7996.062017  10665.673813  5061.086620   \n",
       "934  5083.616595  5374.480205   8217.142946  12010.950993  5000.425974   \n",
       "\n",
       "               6             7            8             9           10  \\\n",
       "0    7176.216977  10341.561739  6505.311482   6279.433594  5714.657222   \n",
       "1    6921.244257   8090.852354  5673.728950   5267.011719  5539.029505   \n",
       "2    6314.656802   8066.825115  6234.547054   5368.332520  5616.178584   \n",
       "3    6927.614117   8099.700803  5206.362083   5712.226562  5606.811352   \n",
       "4    3765.943200   4538.281949  2510.754229   4950.395996  4541.323872   \n",
       "5       0.000000      0.000000     0.000000     -0.028284     0.000000   \n",
       "6    5972.879755   6844.887051  4286.980709   4488.551758  4817.428118   \n",
       "7    5616.944218   6807.330886  4192.341320   4160.208984  4569.376346   \n",
       "8    5247.771443   6372.850673  3895.344289   3787.561279  4551.669918   \n",
       "9    5554.787497   6780.353349  4827.428066   4408.336914  4520.857199   \n",
       "10   5715.102832   7574.897096  3990.749414   4940.951172  4764.123412   \n",
       "11   3646.093786   4818.921211  2425.080131   5140.651855  4502.695003   \n",
       "12      0.000000      0.000000     0.000000     -0.053045     0.000000   \n",
       "13   7280.665828  10721.421378  7750.308633   6982.203125  6657.925012   \n",
       "14   7011.165911   9260.975655  6376.083705   5334.626953  5405.284997   \n",
       "15   6988.189735   8047.107928  5940.518205   4900.089844  5270.986395   \n",
       "16   6215.657925   7918.644043  6436.209779   5052.441895  4981.103498   \n",
       "17   6586.677830   8398.576603  5148.807489   5570.834961  5377.504432   \n",
       "18   3568.620140   4415.173737  2419.843968   5017.503906  4662.733382   \n",
       "19      0.000000      0.000000     0.000000     -0.043465     0.000000   \n",
       "20   5887.256261   6806.083194  4142.699459   4208.080078  4759.075375   \n",
       "21   5565.052033   6627.681069  4038.122900   4126.518555  4564.614294   \n",
       "22   5685.134009   6225.985349  3921.820116   4042.392578  4400.597774   \n",
       "23   5951.143558   6834.920104  5078.175921   4492.574707  4499.291151   \n",
       "24   6331.763843   8366.987540  4269.046174   4952.820801  4736.435904   \n",
       "25   3951.130302   5680.325548  2466.148751   5156.205078  4463.062521   \n",
       "26      0.000000      0.000000     0.000000     -0.056809     0.000000   \n",
       "27   7995.748875  11304.371647  8116.772377   7480.227051  7523.753491   \n",
       "28   7163.679662   9967.504627  6986.286160   6306.048828  5686.595199   \n",
       "29   6959.841448   8315.717458  6040.836447   5669.838379  5353.215461   \n",
       "..           ...           ...          ...           ...          ...   \n",
       "905  5380.294432  10822.738936  8741.049105   8682.273438  6677.128553   \n",
       "906  5175.286138  11382.761888  6928.788711   8371.239258  5984.225171   \n",
       "907  3076.216048   6953.899715  3766.389294   7388.477539  5283.045256   \n",
       "908     0.000000      0.000000     0.000000     -0.312140     0.000000   \n",
       "909  4434.751308  10547.962700  6609.404624   7200.276367  5968.697339   \n",
       "910  4177.535324   9569.668532  5484.447680   6700.878418  5512.063899   \n",
       "911  4214.410605   8689.177902  5288.193240   5747.479980  5213.497878   \n",
       "912  4023.589826   9130.450870  5723.362419   6279.598633  5006.810095   \n",
       "913  4318.281577   9451.756835  5687.426422   5784.897949  5446.198215   \n",
       "914  3117.880892   6371.202761  3582.391384   6730.434082  4832.357916   \n",
       "915     0.000000      0.000000     0.000000     -0.007689     0.000000   \n",
       "916  6735.486192  13563.049386  9226.678566  10629.220703  7090.862208   \n",
       "917  6087.241839  11890.357178  8292.151928   8306.646484  6744.458716   \n",
       "918  5053.072887  10397.356752  7908.693575   7187.606445  6433.046021   \n",
       "919  5073.856439  10183.213753  8451.363564   7877.063477  6144.978505   \n",
       "920  4938.491404  10843.655809  6967.373759   7837.359375  5878.499621   \n",
       "921  3030.731841   6663.896457  3839.242942   6768.887695  5316.732486   \n",
       "922     0.000000      0.000000     0.000000     -0.103678     0.000000   \n",
       "923  4196.594408   9900.887575  5865.544546   7160.478516  5860.148945   \n",
       "924  4003.904757   9247.850161  5392.265065   6488.474609  5814.462640   \n",
       "925  3873.525437   8520.033795  5224.913094   5598.430664  5406.251321   \n",
       "926  3857.727704   8971.163264  5890.586562   6183.783691  5830.192415   \n",
       "927  3945.741744   9296.166432  5104.082701   5932.015625  5772.662940   \n",
       "928  2983.708094   6570.800437  3381.496860   6903.274414  5014.186982   \n",
       "929     0.000000      0.000000     0.000000     -0.029949     0.000000   \n",
       "930  6058.022162  14462.460051  9011.301879  10662.451172  7200.924160   \n",
       "931  5122.596222  12411.801620  7410.610427   8412.494141  6305.517632   \n",
       "932  5162.963689  11500.157129  6719.584642   7479.281250  5842.721550   \n",
       "933  5021.390646  11695.859036  7922.431776   7917.981445  5974.443251   \n",
       "934  5325.109168  13403.592858  7637.432271   8125.443359  6551.357368   \n",
       "\n",
       "            Total  \n",
       "0    73658.036707  \n",
       "1    67167.006832  \n",
       "2    64144.788373  \n",
       "3    64858.330710  \n",
       "4    43834.562428  \n",
       "5       -0.028284  \n",
       "6    51706.093638  \n",
       "7    49674.390071  \n",
       "8    48767.675748  \n",
       "9    49981.213338  \n",
       "10   52694.200677  \n",
       "11   43448.564827  \n",
       "12      -0.053045  \n",
       "13   77390.104271  \n",
       "14   68237.108196  \n",
       "15   66061.461878  \n",
       "16   62566.415776  \n",
       "17   63157.999214  \n",
       "18   43427.401026  \n",
       "19      -0.043465  \n",
       "20   51060.657780  \n",
       "21   48947.414060  \n",
       "22   49517.865151  \n",
       "23   53710.887945  \n",
       "24   57799.877859  \n",
       "25   47094.884149  \n",
       "26      -0.056809  \n",
       "27   88260.452664  \n",
       "28   75182.663642  \n",
       "29   69623.098443  \n",
       "..            ...  \n",
       "905  75839.551675  \n",
       "906  71956.299711  \n",
       "907  51794.228721  \n",
       "908     -0.312140  \n",
       "909  63517.114457  \n",
       "910  57861.088683  \n",
       "911  57016.048627  \n",
       "912  56865.361084  \n",
       "913  58944.696459  \n",
       "914  47797.755205  \n",
       "915     -0.007689  \n",
       "916  90359.341626  \n",
       "917  79290.951703  \n",
       "918  73740.887140  \n",
       "919  71765.500694  \n",
       "920  68981.960067  \n",
       "921  48958.786423  \n",
       "922     -0.103678  \n",
       "923  60454.233673  \n",
       "924  56968.478354  \n",
       "925  55731.858328  \n",
       "926  56318.643391  \n",
       "927  55727.201329  \n",
       "928  47479.845734  \n",
       "929     -0.029949  \n",
       "930  88412.897257  \n",
       "931  75928.493945  \n",
       "932  71905.514993  \n",
       "933  72260.957905  \n",
       "934  76729.551737  \n",
       "\n",
       "[935 rows x 11 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sku_pred=pd.concat([ bm_dict[1],bm_dict[2],bm_dict[3],bm_dict[4],bm_dict[5],bm_dict[6],bm_dict[7],bm_dict[8],bm_dict[9],bm_dict[10],\n",
    "],axis=1) \n",
    "sku_pred['Total'] = sku_pred.sum(axis=1)\n",
    "sku_pred.columns = ['1','2','3','4','5','6','7','8','9','10','Total']\n",
    "sku_pred.to_csv('C:\\\\Users\\\\lengada1\\\\NCSU\\\\prediction_skus.csv')\n",
    "\n",
    "#Each column in this dataframe is the result of 1 of the 4 models\n",
    "sku_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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