SimpleLinearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preamble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import describe\n",
    "pd.options.display.max_columns = 12\n",
    "pd.options.display.max_rows = 24\n",
    "\n",
    "# disable warnings in Anaconda\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "# plots inisde jupyter notebook\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(style='darkgrid', palette='muted')\n",
    "color_scheme = {\n",
    "    'red': '#F1637A',\n",
    "    'green': '#6ABB3E',\n",
    "    'blue': '#3D8DEA',\n",
    "    'black': '#000000'\n",
    "}\n",
    "\n",
    "import datetime as dt\n",
    "import sklearn.linear_model as lm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def smape(y_pred, y_true):\n",
    "    # calculate error\n",
    "    denom = (abs(y_pred) + abs(y_true)) / 2\n",
    "    errors = abs(y_pred - y_true) / denom\n",
    "    return 100 * np.sum(errors) / len(y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def serie_split(s, fcast_len = 90):\n",
    "    \"\"\"\n",
    "    We split our datasets: year 2017 is used for validation and the rest is for training.\n",
    "    As our goal is to predict the first 90 days of 2018, we use only the first 90 days for validation.\n",
    "    \"\"\"\n",
    "    train = s.iloc[s.index < '2017-01-01']\n",
    "    test = s.iloc[s.index >= '2017-01-01'].iloc[:fcast_len]\n",
    "    \n",
    "    return train, test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rolling import Rolling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "In order for this to work, download all datasets from kaggle competition:  \n",
    "https://www.kaggle.com/c/demand-forecasting-kernels-only  \n",
    "and place them in `../data/` folder.  \n",
    "We could not append datasets to our repo because of copyrights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/train.csv')\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df.index = pd.DatetimeIndex(df['date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_and_predict(model, serie):\n",
    "    r = Rolling(window=365)\n",
    "\n",
    "    train, test = serie_split(serie)\n",
    "    train_X, train_y = r.make_training_data(train)\n",
    "\n",
    "    model.fit(train_X, train_y)\n",
    "\n",
    "    y = r.predict(model.predict, train)\n",
    "\n",
    "    return y, test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_all_storeitems(model):\n",
    "    errors = []\n",
    "    for store in range(1,11):\n",
    "        for item in range(1,51):\n",
    "            sales = df[(df[\"store\"] == store) & (df[\"item\"] == item)][\"sales\"]\n",
    "            predicted, real = train_and_predict(model, sales)\n",
    "            error = smape(predicted, real)\n",
    "            print(error)\n",
    "            errors.append(error)\n",
    "    return errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "19.317241879651437\n",
      "16.27222357341799\n",
      "10.795143207036578\n",
      "17.256463195152488\n",
      "22.528261952472544\n",
      "13.674426246161731\n",
      "15.561160135349205\n",
      "19.828975442288012\n",
      "16.65464363708038\n",
      "15.737437879574326\n",
      "13.289132664549681\n",
      "21.931056845864326\n",
      "15.744444449695427\n",
      "12.922082188287371\n",
      "21.276461199483048\n",
      "13.343388337131769\n",
      "16.77648460166978\n",
      "20.521739030333215\n",
      "22.978242810838143\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.958192744920996\n",
      "15.59911745304676\n",
      "20.95939716572004\n",
      "11.986693390558225\n",
      "15.706381238579185\n",
      "24.888263383219673\n",
      "18.033349119969913\n",
      "21.962061564925914\n",
      "14.250225403759636\n",
      "24.767945877491723\n",
      "14.975709458199862\n",
      "18.743714645855388\n",
      "23.163431333142828\n",
      "22.655908147916197\n",
      "12.67947669737187\n",
      "13.601927988345915\n",
      "12.521509933282154\n",
      "16.138065288969244\n",
      "14.763245727948664\n",
      "13.148626375977207\n",
      "14.809354009166478\n",
      "12.214525768187668\n",
      "14.79435396914013\n",
      "12.541125127475233\n",
      "22.720533898081793\n",
      "20.745194420686467\n",
      "11.402961157916478\n",
      "19.830300664959008\n",
      "19.12558380168076\n",
      "15.242033813275368\n",
      "12.887333873845282\n",
      "18.826278667320036\n",
      "16.486742245221\n",
      "10.919306304363104\n",
      "19.103680042951968\n",
      "20.368347355771714\n",
      "11.484350001803902\n",
      "11.902034787328832\n",
      "16.86544594766616\n",
      "16.936084995650297\n",
      "19.016397488593125\n",
      "13.327279273031111\n",
      "26.61679748882723\n",
      "16.583255712323897\n",
      "12.785121660768768\n",
      "18.55306502027465\n",
      "12.249481590567376\n",
      "16.619241654743952\n",
      "18.84350662021926\n",
      "27.51688181994342\n",
      "20.91079809766373\n",
      "14.076630798537943\n",
      "20.450538018827874\n",
      "14.024285750074347\n",
      "14.354707404136546\n",
      "23.338315323398017\n",
      "16.467667818035395\n",
      "19.0910361781983\n",
      "12.975129067904001\n"
     ]
    }
   ],
   "source": [
    "errors = run_all_storeitems(lm.LinearRegression())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMAPE = 17.96796983104712\n"
     ]
    }
   ],
   "source": [
    "print(f\"SMAPE = {np.mean(errors)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kowal/anaconda3/envs/tf/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(errors)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ElasticNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22.908055039879113\n",
      "14.242268093428457\n",
      "15.83362433442583\n",
      "19.837191680545388\n",
      "25.514251459261686\n",
      "14.880375153557582\n",
      "15.790438270373329\n",
      "11.826767892682048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.634460728137732\n",
      "13.729657473479596\n",
      "13.494506283608361\n",
      "14.190822149071701\n",
      "11.386803535755766\n",
      "14.772761432411027\n",
      "12.163245766066995\n",
      "20.402368915966523\n",
      "17.419302646385\n",
      "14.7389596179587\n",
      "15.960622506312683\n",
      "15.125635840790707\n",
      "18.73342649206429\n",
      "15.893620013079662\n",
      "18.690209173375308\n",
      "14.316002916863022\n",
      "11.9673120738425\n",
      "15.932692124997354\n",
      "23.445990930181853\n",
      "15.047599200866545\n",
      "16.381993595296382\n",
      "15.763744936831891\n",
      "16.824618924348158\n",
      "16.592651415542548\n",
      "13.70779607091122\n",
      "18.85062642039606\n",
      "14.44718489449568\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.159756844401864\n",
      "19.673318304274446\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.177937260366773\n",
      "16.009998577179445\n",
      "19.76886437848395\n",
      "21.225187693745713\n",
      "16.992931116481483\n",
      "16.486183598062876\n",
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      "18.097729878272812\n",
      "13.042283747838885\n",
      "17.406569200550177\n",
      "12.077988313318142\n",
      "14.952641835747686\n",
      "18.0259380750861\n",
      "23.066244302951933\n",
      "12.884724536275776\n",
      "11.408373314461583\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.801486733950632\n",
      "13.939369235178301\n",
      "10.541247492831078\n",
      "10.73622684161302\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.055395470687632\n",
      "11.032252887726335\n",
      "12.749743828880982\n",
      "13.870296289000699\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.31182114559879\n",
      "16.87510674758307\n",
      "10.214945142665393\n",
      "13.211212959692132\n",
      "14.197895053717115\n",
      "13.982774243013818\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.489251637245628\n",
      "18.709869501277026\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.834065962243518\n",
      "10.009566233140852\n",
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      "16.85491643273881\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.799693292910277\n",
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      "17.753590873973295\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.721510928357914\n",
      "12.037600440093213\n",
      "20.172797067615694\n",
      "10.88114516032931\n",
      "18.34369688093941\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.257332916270416\n",
      "16.34778002177003\n",
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      "12.055956097226336\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.738793416483936\n",
      "10.584417884731801\n",
      "14.50962667945162\n",
      "15.326882449264957\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.744486330939157\n",
      "15.746755463034914\n",
      "10.51432082052611\n",
      "16.416393986638646\n",
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      "11.921237527472288\n",
      "14.59727407380608\n",
      "17.448809183963707\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.71995640792511\n",
      "12.860429152023084\n",
      "19.411417384675254\n",
      "13.482604085147482\n",
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      "12.784051890455139\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.825313431637333\n",
      "11.83899905571337\n",
      "11.97927733651998\n",
      "17.15693506132592\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.011074029577673\n",
      "16.580016876686507\n",
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      "10.812148235371003\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21.967719252908378\n",
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      "12.25704908120273\n",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.956706101598709\n",
      "13.688431976890572\n",
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      "11.04231500128041\n",
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      "21.660311203672137\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.521266881924625\n",
      "12.454943864833623\n",
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      "13.412232368116046\n",
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      "13.189439244215984\n",
      "17.935080399400203\n",
      "14.363647502793524\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.32757726994728\n",
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      "16.699445065051027\n",
      "14.383511616604103\n",
      "21.181567901111162\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.12680437034122\n",
      "12.941883815840372\n",
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      "15.996209023647722\n",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.785176255381872\n",
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      "17.640721655131212\n",
      "25.343470492736895\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.748311975218483\n",
      "14.513887976782083\n",
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      "19.781568111255424\n",
      "15.342127902182856\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.56132522799678\n",
      "16.07903675018193\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.862342138978347\n",
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      "20.414442400591124\n",
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      "19.80496893831636\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.00492333958035\n",
      "18.437047768625437\n",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.552975685513058\n",
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      "13.9156302189571\n",
      "16.96320239353472\n",
      "18.522943011794464\n",
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      "13.664834113552168\n",
      "14.997554535628923\n",
      "20.503267711394646\n",
      "12.994142267624596\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.81513019426733\n",
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      "13.912876809586434\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.418355421842687\n",
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      "17.21721617615336\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.934651706415115\n",
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      "19.839325133613883\n",
      "13.058256999003968\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.935751867162564\n",
      "12.192560435242813\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.49716980492156\n",
      "13.383191661511464\n",
      "10.440416514913817\n",
      "13.11259691393416\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.320916362740832\n",
      "13.806332172993805\n",
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      "18.699161067512964\n",
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      "10.900731898022151\n",
      "14.137739905760688\n",
      "14.13967700698493\n",
      "13.13857123505909\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.286554499022857\n",
      "12.94654380632331\n",
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      "11.116775856081183\n",
      "10.647371517355944\n",
      "17.62614525280131\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.78192890516689\n",
      "14.323680753143925\n",
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      "19.134389493386752\n",
      "16.72812472191907\n",
      "12.965650638344725\n",
      "19.988390667627137\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.1222661117833\n",
      "10.685284660951963\n",
      "17.565997822882967\n",
      "12.648434374499335\n",
      "17.702149377854603\n",
      "13.427571273733742\n",
      "20.5188034795975\n",
      "13.631466672436922\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.965902603841357\n",
      "23.191690094829493\n",
      "23.797648452130534\n",
      "12.921171791812316\n",
      "13.945539933853395\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.253836198237494\n",
      "13.947075717423463\n",
      "10.950717543160087\n",
      "13.769015011759107\n",
      "12.868778754162673\n",
      "14.349954813033694\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.998854378177516\n",
      "12.403467701370605\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.060828631659174\n",
      "17.147067877887753\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.626570873101608\n",
      "18.2444885525437\n",
      "15.592491303337995\n",
      "13.756051752612926\n",
      "11.344815516363642\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.31195842863537\n",
      "15.024081914422117\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.923449606341698\n",
      "14.743959635828352\n",
      "20.40016618166033\n",
      "12.975318693623727\n",
      "14.62590286042633\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.19199060350268\n",
      "14.975446243330303\n",
      "14.293184608609211\n",
      "12.278024181103754\n",
      "18.632445429237052\n",
      "14.75694870438326\n",
      "11.979173868046121\n",
      "17.890186904617117\n",
      "12.758846076008558\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.744699360634137\n",
      "18.2155037089624\n",
      "20.78744023815406\n",
      "16.001856034542953\n",
      "13.669280376385837\n",
      "19.167707734702766\n",
      "11.007818161032203\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.885227411473236\n",
      "20.502944848166553\n",
      "16.294392913660968\n",
      "17.66798507087139\n",
      "13.024276460397124\n",
      "21.067064810658835\n",
      "13.759803626057355\n",
      "17.075111830695143\n",
      "19.558582138138004\n",
      "19.963973846426\n",
      "12.001284165278076\n",
      "12.51653925938499\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.858675126103973\n",
      "14.481922660457855\n",
      "13.826572972993967\n",
      "12.496282210995284\n",
      "13.595026963075592\n",
      "11.369433365525534\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.267534722415947\n",
      "11.820184089169924\n",
      "19.239559102480882\n",
      "18.534031399135696\n",
      "10.798213456627805\n",
      "17.80467514371138\n",
      "17.439816629135017\n",
      "14.57846118392824\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.092266861458333\n",
      "17.086766881288987\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.782715564891426\n",
      "10.425834230919115\n",
      "17.43456032174984\n",
      "17.31738012953047\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.885179667384028\n",
      "11.110621786148178\n",
      "15.4417847965141\n",
      "14.976453071579787\n",
      "16.966563904223754\n",
      "12.48698367362857\n",
      "22.973501313676298\n",
      "15.08863663804948\n",
      "12.007065680576208\n",
      "16.92316982127015\n",
      "11.471942068638143\n",
      "14.41356816146541\n",
      "16.1277600800965\n",
      "23.10139678522042\n",
      "17.769128625704408\n",
      "13.060735788776633\n",
      "17.432829544871854\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radek/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.261010381430467\n",
      "13.508304636407633\n",
      "19.01799818841698\n",
      "14.49697623612207\n",
      "16.782461530702978\n",
      "11.909451036631438\n"
     ]
    }
   ],
   "source": [
    "errors = run_all_storeitems(lm.ElasticNet())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMAPE = 15.871759767225456\n"
     ]
    }
   ],
   "source": [
    "print(f\"SMAPE = {np.mean(errors)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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   "source": [
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  {
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   "metadata": {},
   "source": [
    "## Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
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   "source": [
    "errors = run_all_storeitems(lm.Ridge())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMAPE = 17.96775001813665\n"
     ]
    }
   ],
   "source": [
    "print(f\"SMAPE = {np.mean(errors)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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   ],
   "source": [
    "sns.distplot(errors)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bayesian Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
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    "errors = run_all_storeitems(lm.BayesianRidge())"
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  {
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   "execution_count": 17,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMAPE = 14.653786342514707\n"
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    "print(f\"SMAPE = {np.mean(errors)}\")"
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   "execution_count": 18,
   "metadata": {
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      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot sample forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "store, item = 1,1\n",
    "plot_lengths = [7, 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales = df[(df[\"store\"] == store) & (df[\"item\"] == item)][\"sales\"]\n",
    "predicted, real = train_and_predict(lm.BayesianRidge(), sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for pl in plot_lengths:\n",
    "    plt.plot(predicted,\n",
    "             color_scheme['blue'],\n",
    "             lw=2,\n",
    "             label='Prediction')\n",
    "    plt.plot(real.values,\n",
    "             color_scheme['green'],\n",
    "             lw=2, \n",
    "             label='Real values')\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.savefig(f'bayesian_ridge_{pl}.svg')\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}