beat_the_winner (2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attempt to refactor/analyze winning model. See discussion on http://forums.fast.ai/t/corporacion-favorita-grocery-sales-forecasting/8359/81"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: CUDA_DEVICE_ORDER=PCI_BUS_ID\n",
      "env: CUDA_VISIBLE_DEVICES=0\n"
     ]
    }
   ],
   "source": [
    "gpu=0\n",
    "%env CUDA_DEVICE_ORDER=PCI_BUS_ID\n",
    "%env CUDA_VISIBLE_DEVICES=$gpu\n",
    "%matplotlib inline\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "import os,sys\n",
    "sys.path.insert(1, '/home/kevin/fastai') #make a simlink to the code folder\n",
    "from fastai.structured import *\n",
    "from fastai.column_data import *\n",
    "from IPython.display import HTML\n",
    "np.set_printoptions(threshold=50, edgeitems=20)\n",
    "from datetime import date, timedelta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#paths\n",
    "data_path = '/home/kevin/nbs/kaggle/GrocerySales/data/'\n",
    "slowdata = data_path + 'slowdisk/'; slowdata\n",
    "simple = slowdata + 'replicate_winner/'\n",
    "#leaves duplicate field names on left and appends with _y on right\n",
    "def join_df(left, right, left_on, right_on=None):\n",
    "    if right_on is None: right_on=left_on\n",
    "    return left.merge(right, how='left', left_on=left_on, right_on=right_on, suffixes=(\"\", \"_y\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#normalize and denormalize targets\n",
    "def normy(y):\n",
    "    meany=np.mean(y)\n",
    "    stdevy = np.std(y)\n",
    "    ynorm = (y-meany)/stdevy\n",
    "    return meany, stdevy, ynorm\n",
    "\n",
    "def denorm(y, meany, stdevy, r=3):\n",
    "    return np.round(y*stdevy+meany, r)\n",
    "\n",
    "#\n",
    "def trainable_params(m):\n",
    "    model_parameters = filter(lambda p: p.requires_grad, m.model.parameters())\n",
    "    return sum([np.prod(p.size()) for p in model_parameters])\n",
    "\n",
    "def mae(inp, targ):\n",
    "    inp = inp.view(inp.numel())\n",
    "    targ = targ.view(targ.numel())\n",
    "    l = len(inp)\n",
    "    return torch.sum(torch.abs(inp-targ))/l\n",
    "\n",
    "#custom function I wrote to add a null check\n",
    "def apply_cats(df, trn, null_check=False):\n",
    "    for n,c in df.items():\n",
    "        if (n in trn.columns) and (trn[n].dtype.name=='category'):\n",
    "            df[n] = pd.Categorical(c, categories=trn[n].cat.categories, ordered=True)\n",
    "            if null_check and df[n].isnull().values.any():\n",
    "                raise ValueError(f'Target dataframe has null values for column {n}. This can occur if the target dataframe has category values not present in original.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and clean data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# specify datatypes before loading the data will save you a ton of memory.\n",
    "dtypes = {'id': np.uint32,\n",
    "          'store_nbr': np.uint8, \n",
    "          'item_nbr': np.uint32, \n",
    "          'unit_sales': np.float32,\n",
    "          'class': np.uint16,\n",
    "          'onpromotion': np.bool,\n",
    "          'dcoilwtico':np.float16,\n",
    "          'transactions':np.uint16,\n",
    "          'cluster': np.uint32}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv(slowdata + 'train.csv', dtype=dtypes, usecols=[1,2,3,4,5], parse_dates=['date'],\n",
    "                    skiprows=range(1, 86672217) #Skip dates before 2016-08-01, and id column\n",
    "                   )\n",
    "test = pd.read_csv(slowdata + 'test.csv', dtype=dtypes, parse_dates=['date'])\n",
    "items = pd.read_csv(slowdata + 'items.csv', dtype=dtypes)\n",
    "stores = pd.read_csv(slowdata + 'stores.csv', dtype=dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# data older than march 1 showed no improvement. Setting to Jan so code works.\n",
    "train = train[train['date'] >= '2017-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train['unit_sales'] = train['unit_sales'].as_matrix().clip(min=0) # get rid of negs\n",
    "train['unit_sales'] =  train['unit_sales'].apply(pd.np.log1p) # transform for smoother training\n",
    "train['onpromotion'].fillna(False, inplace=True) # fill in the missing promotion data with 'False'\n",
    "test['onpromotion'].fillna(False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "items.set_index('item_nbr', inplace=True)\n",
    "stores.set_index('store_nbr', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save and load cleaned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train.to_pickle(simple + 'train_processed.pkl')\n",
    "test.to_pickle(simple + 'test_processed.pkl')\n",
    "items.to_pickle(simple + 'items_processed.pkl')\n",
    "stores.to_pickle(simple + 'stores_processed.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lastdate = test.iloc[test.shape[0]-1].date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build DFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_pickle(simple + 'train_processed.pkl')\n",
    "test = pd.read_pickle(simple + 'test_processed.pkl')\n",
    "items = pd.read_pickle(simple + 'items_processed.pkl')\n",
    "stores = pd.read_pickle(simple + 'stores_processed.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Sales DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sales_df = train.set_index(\n",
    "    ['store_nbr', 'item_nbr', 'date'])[['unit_sales']].unstack(level=-1).fillna(0)\n",
    "sales_df.columns = sales_df.columns.get_level_values(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Items and Stores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#now items and stores are dataframes of sales_df length\n",
    "items_df = items.reindex(sales_df.index.get_level_values(1))\n",
    "stores_df = stores.reindex(sales_df.index.get_level_values(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Promo dataframe prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "promo_train_df = train.set_index(\n",
    "    ['store_nbr', 'item_nbr', 'date'])[['onpromotion']].unstack(level=-1).fillna(False)\n",
    "promo_train_df.columns = promo_train_df.columns.get_level_values(1)\n",
    "promo_test_df = test.set_index(\n",
    "    ['store_nbr', 'item_nbr', 'date'])[['onpromotion']].unstack(level=-1).fillna(False)\n",
    "promo_test_df.columns = promo_test_df.columns.get_level_values(1)\n",
    "tr_idx = promo_train_df.index\n",
    "promo_test_df = promo_test_df.reindex(tr_idx).fillna(False)\n",
    "promo_df = pd.concat([promo_train_df, promo_test_df], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Sales item and promo item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sales_item_df = sales_df.groupby('item_nbr').sum() #sum of unit sales for each item across all stores\n",
    "promo_item_df = promo_df.groupby('item_nbr').sum() #sum of promotions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Sales store class and promo store class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sales_store_class_df = sales_df.reset_index()\n",
    "sales_store_class_df['class'] = items_df['class'].values\n",
    "sales_store_class_index = sales_store_class_df[['class', 'store_nbr']]\n",
    "#train.columns to ensure only date columns returned\n",
    "sales_store_class_df = sales_store_class_df.groupby(['class', 'store_nbr'])[sales_df.columns].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#repeat for promos\n",
    "promo_store_class_df = promo_df.reset_index()\n",
    "promo_store_class_df['class'] = items_df['class'].values\n",
    "promo_store_class_index = promo_store_class_df[['class', 'store_nbr']] #for ...?\n",
    "promo_store_class_df = promo_store_class_df.groupby(['class', 'store_nbr'])[promo_df.columns].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sales_df.to_pickle(simple + 'sales_df.pkl')\n",
    "promo_df.to_pickle(simple + 'promo_df.pkl')\n",
    "items_df.to_pickle(simple + 'items_df.pkl')\n",
    "stores_df.to_pickle(simple + 'stores_df.pkl')\n",
    "sales_store_class_index.to_pickle(simple + 'sales_store_class_index.pkl')\n",
    "sales_item_df.to_pickle(simple + 'sales_item_df.pkl')\n",
    "promo_item_df.to_pickle(simple + 'promo_item_df.pkl')\n",
    "sales_store_class_df.to_pickle(simple + 'sales_store_class_df.pkl')\n",
    "promo_store_class_df.to_pickle(simple + 'promo_store_class_df.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_pickle(simple + 'train_processed.pkl')\n",
    "test = pd.read_pickle(simple + 'test_processed.pkl')\n",
    "items = pd.read_pickle(simple + 'items_processed.pkl')\n",
    "stores = pd.read_pickle(simple + 'stores_processed.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train = pd.read_pickle(simple + 'train_processed.pkl')\n",
    "sales_df = pd.read_pickle(simple + 'sales_df.pkl')\n",
    "promo_df = pd.read_pickle(simple + 'promo_df.pkl')\n",
    "items_df = pd.read_pickle(simple + 'items_df.pkl')\n",
    "stores_df = pd.read_pickle(simple + 'stores_df.pkl')\n",
    "sales_store_class_index = pd.read_pickle(simple + 'sales_store_class_index.pkl')\n",
    "sales_item_df = pd.read_pickle(simple + 'sales_item_df.pkl')\n",
    "promo_item_df = pd.read_pickle(simple + 'promo_item_df.pkl')\n",
    "sales_store_class_df = pd.read_pickle(simple + 'sales_store_class_df.pkl')\n",
    "promo_store_class_df = pd.read_pickle(simple + 'promo_store_class_df.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_days = (test.iloc[-1].date - test.iloc[0].date).days + 1; prediction_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_items_df = items_df.reset_index()\n",
    "m_stores_df= stores_df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save mem\n",
    "del train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_df_timespan(df, dt, minus, periods, freq='D'):\n",
    "    r = pd.date_range(dt - timedelta(days=minus), periods=periods, freq=freq)\n",
    "    return df[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_dataset(df, promo_df, start_date, is_train=True, name_prefix=None, prediction_days=prediction_days):\n",
    "    X=pd.DataFrame()\n",
    "    \n",
    "    for i in [1, 7, 14, 40]:\n",
    "        X[f'promos_{i}_before'] = get_df_timespan(promo_df, start_date, i, i).sum(axis=1).values\n",
    "    \n",
    "    for i in [3, 7, 14]:\n",
    "        X[f'promo_{i}_after'] = get_df_timespan(promo_df, start_date + timedelta(prediction_days), \n",
    "                                                prediction_days-1, i).sum(axis=1).values\n",
    "    \n",
    "    for i in [3, 7, 14, 30, 40]:\n",
    "        tmp = get_df_timespan(df, start_date, i, i)\n",
    "        X[f'mean_{i}_before'] = tmp.mean(axis=1).values\n",
    "        X[f'mean_{i}_diff_before'] = tmp.diff(axis=1).mean(axis=1).values\n",
    "        X[f'min_{i}_before'] = tmp.min(axis=1).values\n",
    "        X[f'max_{i}_before'] = tmp.max(axis=1).values\n",
    "        X[f'std_{i}_before'] = tmp.std(axis=1).values\n",
    "        X[f'sum_{i}_before'] = tmp.sum(axis=1).values\n",
    "        \n",
    "    for i in [3, 7, 14, 30, 40]:\n",
    "        tmp = get_df_timespan(df, start_date, i, i)        \n",
    "        X[f'has_sales_days_in_last_{i}'] = (tmp > 0).sum(axis=1).values\n",
    "        X[f'last_has_sales_day_in_last_{i}'] = i - ((tmp > 0) * np.arange(i)).max(axis=1).values\n",
    "        X[f'first_has_sales_day_in_last_{i}'] = ((tmp > 0) * np.arange(i, 0, -1)).max(axis=1).values\n",
    "        \n",
    "        tmp = get_df_timespan(promo_df, start_date, i, i)\n",
    "        X[f'has_promo_days_in_last_{i}'] = (tmp > 0).sum(axis=1).values\n",
    "        X[f'last_has_promo_day_in_last_{i}'] = i - ((tmp > 0) * np.arange(i)).max(axis=1).values\n",
    "        X[f'first_has_promo_day_in_last_{i}'] = ((tmp > 0) * np.arange(i, 0, -1)).max(axis=1).values\n",
    "\n",
    "    for i in range(1, 16):\n",
    "        X['sales_{i}_days_ago'] = get_df_timespan(df, start_date, i, 1).values.ravel()\n",
    "    \n",
    "    #Day of week\n",
    "    for i in range(7):\n",
    "        for j in [14, 7, 3, 1]:\n",
    "            X[f'mean_{j}_dow_{i}_bfore'] =\\\n",
    "            get_df_timespan(df, start_date, j*7-i, j, freq='7D').mean(axis=1).values\n",
    "    \n",
    "    #date categories. Put all categoricals below.\n",
    "    X['day'] = start_date.day\n",
    "    X['dow'] = start_date.weekday() \n",
    "    X['month'] = start_date.month\n",
    "    \n",
    "    for i in range(-16,16):\n",
    "        X[f'onpromotion_at_{i}_days'] = \\\n",
    "        promo_df[start_date + timedelta(days=i)].values.astype(np.uint8)\n",
    "    \n",
    "    if is_train:\n",
    "        prediction_range = pd.date_range(start_date, periods=prediction_days)\n",
    "        y = df[prediction_range].values\n",
    "        return X, y\n",
    "\n",
    "    if name_prefix is not None:\n",
    "        X.columns = [f'{name_prefix}_{c}' for c in X.columns]\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_training_from = date(2017, 4, 29)\n",
    "valid_date = date(2017, 7, 16)\n",
    "training_weeks = 10 #ends June 28\n",
    "dates = [start_training_from + timedelta(days=6*d) for d in range(training_weeks+1)]\n",
    "dates.extend((date(2017, 7, 31), valid_date)) #last date is validation set\n",
    "X_l, y_l = [], []\n",
    "\n",
    "for start_date in dates:\n",
    "    X_tmp, y_tmp = prepare_dataset(sales_df, promo_df, start_date)\n",
    "\n",
    "    X_tmp2 = prepare_dataset(sales_item_df, promo_item_df, start_date, \n",
    "                             is_train=False, name_prefix='item')\n",
    "    X_tmp2.index = sales_item_df.index #changes back to items\n",
    "    X_tmp2 = X_tmp2.reindex(sales_df.index.get_level_values(1)).reset_index(drop=True)\n",
    "    #now len, index same as X_tmp.\n",
    "    \n",
    "    X_tmp3 = prepare_dataset(sales_store_class_df, promo_store_class_df, start_date, \n",
    "                             is_train=False, name_prefix='store_class')\n",
    "    X_tmp3.index = sales_store_class_df.index\n",
    "    X_tmp3 = X_tmp3.reindex(sales_store_class_index).reset_index(drop=True)\n",
    "    \n",
    "    X_tmp = pd.concat([X_tmp, X_tmp2, X_tmp3, m_items_df, m_stores_df], axis=1)\n",
    "    X_tmp['date_index'] = dates.index(start_date)\n",
    "    \n",
    "    X_l.append(X_tmp)\n",
    "    y_l.append(y_tmp)\n",
    "\n",
    "X_train = pd.concat(X_l, axis=0)\n",
    "y_train = np.concatenate(y_l, axis=0)\n",
    "del X_l; del y_l; del X_tmp; del X_tmp2; del X_tmp3\n",
    "\n",
    "train_size = int((len(X_train)*(len(dates)-1))/len(dates))\n",
    "val_idx = list(range(train_size, len(X_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.to_feather(simple + 'X_train.ftr')\n",
    "pickle.dump(y_train, open(simple + 'y_train.pkl', 'wb')) \n",
    "pickle.dump(val_idx, open(simple + 'val_idx.pkl', 'wb')) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Restart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start here after kernel dies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: CUDA_DEVICE_ORDER=PCI_BUS_ID\n",
      "env: CUDA_VISIBLE_DEVICES=0\n"
     ]
    }
   ],
   "source": [
    "gpu=0\n",
    "%env CUDA_DEVICE_ORDER=PCI_BUS_ID\n",
    "%env CUDA_VISIBLE_DEVICES=$gpu\n",
    "%matplotlib inline \n",
    "# %reload_ext autoreload\n",
    "# %autoreload 2\n",
    "import os,sys\n",
    "sys.path.insert(1, '/home/kevin/fastai')\n",
    "from fastai.structured import *\n",
    "from fastai.column_data import *\n",
    "from IPython.display import HTML\n",
    "from datetime import date, timedelta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#paths\n",
    "data_path = '/home/kevin/nbs/kaggle/GrocerySales/data/'\n",
    "slowdata = data_path + 'slowdisk/'; slowdata\n",
    "simple = slowdata + 'replicate_winner/'\n",
    "#leaves duplicate field names on left and appends with _y on right\n",
    "def join_df(left, right, left_on, right_on=None):\n",
    "    if right_on is None: right_on=left_on\n",
    "    return left.merge(right, how='left', left_on=left_on, right_on=right_on, suffixes=(\"\", \"_y\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#normalize and denormalize targets\n",
    "def normy(y):\n",
    "    meany=np.mean(y)\n",
    "    stdevy = np.std(y)\n",
    "    ynorm = (y-meany)/stdevy\n",
    "    return meany, stdevy, ynorm\n",
    "\n",
    "def denorm(y, meany, stdevy, r=3):\n",
    "    return np.round(y*stdevy+meany, r)\n",
    "\n",
    "#\n",
    "def trainable_params(m):\n",
    "    model_parameters = filter(lambda p: p.requires_grad, m.model.parameters())\n",
    "    return sum([np.prod(p.size()) for p in model_parameters])\n",
    "\n",
    "def mae(inp, targ):\n",
    "    inp = inp.view(inp.numel())\n",
    "    targ = targ.view(targ.numel())\n",
    "    l = len(inp)\n",
    "    return torch.sum(torch.abs(inp-targ))/l\n",
    "\n",
    "#custom function I wrote to add a null check\n",
    "def apply_cats(df, trn, null_check=False):\n",
    "    for n,c in df.items():\n",
    "        if (n in trn.columns) and (trn[n].dtype.name=='category'):\n",
    "            df[n] = pd.Categorical(c, categories=trn[n].cat.categories, ordered=True)\n",
    "            if null_check and df[n].isnull().values.any():\n",
    "                raise ValueError(f'Target dataframe has null values for column {n}. This can occur if the target dataframe has category values not present in original.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.read_feather(simple + 'X_train.ftr')\n",
    "y_train = pickle.load(open(simple + 'y_train.pkl', 'rb'))\n",
    "val_idx = pickle.load(open(simple + 'val_idx.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cat and conv vars\n",
    "first_cat = X_train.columns.values.tolist().index('day')\n",
    "cat_vars = X_train.columns[first_cat:].values.tolist()\n",
    "for v in cat_vars: \n",
    "    X_train[v] = X_train[v].astype('category').cat.as_ordered()\n",
    "contin_vars = X_train.columns[:first_cat].values.tolist(); contin_vars\n",
    "for v in contin_vars:\n",
    "    X_train[v] = X_train[v].astype('float32')\n",
    "cat_sz = [(c, len(X_train[c].cat.categories)+1) for c in cat_vars]\n",
    "emb_szs = [(c, min(50, (c+1)//2)) for _,c in cat_sz]\n",
    "n_cont = len(contin_vars)\n",
    "#normalize target\n",
    "meany, stdevy, yl = normy(y_train)\n",
    "max_log_y = np.max(yl)\n",
    "min_log_y = np.min(yl)\n",
    "y_range = (min_log_y*1.2, max_log_y*1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['ignore'] = 0 #dummy variable because prod_df requires response variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gen model dataframes\n",
    "df, _, nas, mapper = proc_df(X_train, 'ignore', do_scale=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "del X_train; del y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# df.to_feather(simple + 'df.ftr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_feather(simple + 'df.ftr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs = int(1e3)\n",
    "md = ColumnarModelData.from_data_frame(path=simple, val_idxs=val_idx, df=df, y=yl,\n",
    "                                      cat_flds=cat_vars, bs=bs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "for i in range(2):\n",
    "    m = md.get_learner(emb_szs=emb_szs, n_cont=n_cont, emb_drop=0.1, \n",
    "                   out_sz=16, szs=[512,256,128,64], drops=[.1,.1,.1,.1], y_range=y_range)\n",
    "    m.crit = mae\n",
    "    print(f'==============={i}=====================')\n",
    "    m.warm_up(1e-3)\n",
    "    m.fit(2e-4,4)\n",
    "    m.fit(2e-5,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = md.get_learner(emb_szs=emb_szs, n_cont=n_cont, emb_drop=0.5, \n",
    "                   out_sz=16, szs=[512,256,128,64], drops=[.5,.5,.5,.5], y_range=y_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "635881ca5c63423d9f0312c9631da162",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=8), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch      trn_loss   val_loss                                 \n",
      "    0      0.572404   0.374506  \n",
      "    1      0.518312   0.382381                                 \n",
      "    2      0.429057   0.40045                                  \n",
      "    3      0.389929   0.389182                                 \n",
      "    4      0.372068   0.386812                                 \n",
      "    5      0.365212   0.382646                                 \n",
      "    6      0.361541   0.385794                                 \n",
      "    7      0.35853    0.386254                                 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.38625354]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.fit(1e-3,2, cycle_len=2, cycle_mult=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fefe7bbd710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef5a038470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fee597facc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is m.fit(2e-4,2, cycle_len=2, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RYa5MRaS+X/nZIlIkInPd676K2opEg3Ibs3TzHhZttOXSS5NIWLviJJzZKpXh57fi/bkbbcYHEzKClpREJBp4DugL5ABDRCSn1GE3ADtVtQXwJPCoq5sDDAbaAX2AkSISXUGbU4HzgbL+7/pGVTu71wMnEF/QVdXvwwEd04mLibIlLcwP3HZOC85r04AHP1zM7LU7vQ7HmKDeKXUH8lV1laoeBsYAA0sdMxB4zW2PA84T35+tA4ExqnpIVVcD+a69cttU1TmquqaS44sYyYmx9G7XiPHzNnKo+KjX4ZgQERUl/OPnnUlLrsFtb37P9r22BpPxVjCTUgbg/2d5gSsr8xhVLQaKgJTj1A2kzbKcJiLzRORjEWl3AvFFlEGnZLJr/xE+X7LV61BMCElOjGXklV3Zse8wv3t7nj1YazxVHQY6fA80VdVOwDPA+yfagIjcJCJ5IpJXWFhY6QFWlV4t6pOWnMC42TYKz/xQ+4xk/ti/LV8sK2T0t6u9DsdUY8FMShuAxn7vM11ZmceISAyQDGw/Tt1A2vwBVd2tqnvd9kQg1g2ECLgtVR2lqrmqmpuamnq804W06Cjh0q4ZfLlsK1t3H/Q6HBNirj61KX3aNeLRSUuZs86+XzLeCGZSmgW0FJFsEYnDN3BhQqljJgBD3fYgYIr6xqZOAAa70XnZQEtgZoBt/oCINHLfUyEi3fFd8/aTaSsSXNY1k2MK79okraYUEeHRQR1plJzAsP/MsYetjSeClpTcd0TDgE+AJcBYVV0kIg+IyAB32GggRUTygeHACFd3ETAWWAxMAm5T1aPltQkgIreLSAG+O575IvKSO8cgYKGIzAOeBgarT7ltRbJmqTXJbVqXcbML7NkU8yPJNWJ5ZkgXtuw+yD3vzLfPiKlyMcFs3HWXTSxVdp/f9kHg8nLqPgw8HEibrvxpfEmndPmzwLOBxlcdDDolkxHvLmB+QRGdGtfxOhwTYro0qcvdfVrz14lL+ff0tVxzWpbXIZlqpDoMdDCl9O2QRlx0FO/PtS48U7YbezXjnNapPPThEhZuKPI6HFONWFKqhpJrxHJumwZ8MG8TxUePeR2OCUFRUcITP+9M3aRYbh8zx+bHM1XGklI1dXGXDLbtPcS0ldu9DsWEqHpJcTz5886s3raPhz5a4nU4ppqwpFRNndMmldoJMbxvo/DMcZzeoj43ndGM/8xYx+TFW7wOx1QDlpSqqfiYaPp1SOOTRZs5cNimHTLlG35hK3LSanPPO/PZuseebzPBZUmpGhvYOYN9h48yeUn1/AvYBjsHJj4mmqeHdGbfoWJ+9/Z8m4bIBJUlJQ95/QxIj+x6pCUnMN668EwFWjSoxR8vasvXywt57bs1XodjIpglJY95uZJPVJQwoHM6Xy0vZMe+wx5GYsLBVac25dw2Dfjbx0tZtnmP1+GYCGVJqZq7uHMGxceUjxZs8joUE+JEhMcGdaR2Qgy/GTOHg0fsu0hT+SwpVXNt02rTumEtG4VnAlK/ZjyPD+rE0s17eGzSMq/DMRHIkpJhYJd0Zq/dyfod+70OxYSBc9o0YOhpTXl56mqm5W/zOhwTYSwpGX7WMR3AuvBMwEb0bUuz+kncNW4+ew7abOKm8lhSMjSul0inxnX4cP5Gr0MxYaJGXDR//3knNhUd4MEPF3sdjokgASclEUkMZiDGWz/rmMbCDbtZs22f16GYMNG1SV1uObs5Y/MK+MxmezCVpMKkJCKni8hiYKl730lERgY9MlOl+nVIA7C7JXNCbj+vJW0a1WLEuwvssQJTKQK5U3oS6I1vtVZUdR5wZjCDMlUvvU4NcpvW5cP59r2SCVx8TDRP/qIzRQcO86f3F3r+QLgJfwF136nq+lJFAT2gICJ9RGSZiOSLyIgy9seLyFtu/wwRyfLbd68rXyYivStqU0SGuTIVkfp+5VeKyHwRWSAi00Skk9++Na58rojkBXJNkax/xzSWbt5D/lZ7MNIErm1abX57fis+WrCJCfPsTtv8NIEkpfUicjqgIhIrIr/Dt3z4cYlINPAc0BfIAYaISE6pw24AdqpqC3x3ZI+6ujnAYKAd0AcYKSLRFbQ5FTgfWFvqHKuBs1S1A/AgMKrU/nNUtbOq5lZ0TZGuX4c0RLC7JXPCbj6zGV2a1OG+8YvYstsmbTUnL5Ck9CvgNiAD2AB0Bm4NoF53IF9VV6nqYWAMMLDUMQOB19z2OOA8ERFXPkZVD6nqaiDftVdum6o6R1XXlA5CVaep6k73djqQGUDs1VKD2gl0z6rHh/M3WTeMOSEx0VE8cXknDhUf5Z535tvnx5y0QJJSa1W9UlUbqmoDVb0KaBtAvQzAv9uvwJWVeYyqFgNFQMpx6gbS5vHcAHzs916BT0VktojcVF4lEblJRPJEJK+wsPAEThd++ndKJ3/rXpZtsS48c2KapdZkRJ82fLmskDGzSvf4GxOYQJLSMwGWhTQROQdfUrrHr7iXqnbF1x14m4iUOYBDVUepaq6q5qamplZaTKH4t2Tf9o2IEvhwXjXowrO/5ivdNadlcXrzFB76cLHNEGJOSrlJSUROE5E7gVQRGe73uh+IDqDtDUBjv/eZrqzMY0QkBkjGN8qvvLqBtFnWtXQEXgIGqur/1v9W1Q3u363Ae/i6B6u1+jXjOb15fT6cv9G6YMwJi4oSHr+8EyJi3XjmpBzvTikOqAnEALX8XruBQQG0PQtoKSLZIhKHb+DChFLHTACGuu1BwBT1fYonAIPd6LxsoCUwM8A2f0BEmgDvAler6nK/8iQRqVWyDVwILAzguiqVeLl2RTn6d0xjzfb9LNq42+tQgi4Uf/7hLqNODX7fry3TVm7nPzPXeR2OCTMx5e1Q1a+Ar0TkVVUtPaKtQqpaLCLDgE/w3Vm9rKqLROQBIE9VJwCjgX+LSD6wA1+SwR03FlgMFAO3qepR8A39Lt2mK78duBtoBMwXkYmqeiNwH77vqUb6xlBQ7EbaNQTec2UxwH9UddKJXmck6t2uEX98fyEfzt9E+4xkr8MxYWhI98ZMXLCJv360hLNapZJZ1yaEMYEpNyn52S8ij+Mbnp1QUqiq51ZUUVUnAhNLld3nt30QuLycug8DDwfSpit/Gni6jPIbgRvLKF8FdCpdbqBuUhw9W/i68O7p0xqx2wlzgkSEv13agT5Pfc2Idxbw7xu62+fIBCSQgQ5v4ptiKBv4C7AGXzeaiWD9O6ZRsPMA8wqKvA7FhKnG9RIZ0a8t3+Zvs9F4JmCBJKUUVR0NHFHVr1T1eqDCuyQT3i7MaURMlPDxwmowCs8EzZXdm3BasxQe/mgJG3Yd8DocEwYCSUoli6VsEpGLRKQLUC+IMZkQkJwYS88W9fl4wWYbQWVOWlSUbwn1Y6rc++4C+yyZCgWSlB4SkWTgTuB3+IZW3xHUqExI6NehEet2VI9ReCZ4GtdL5J4+bfh6eSFv5xV4HY4JccdNSm6uuZaqWqSqC1X1HFU9xY2cMxHugpxGRFsXnqkEV5/alO7Z9Xjwo8VsKrJuPFO+4yYlNwx7SBXFYkJMvaQ4TmuWwkTrwjM/UVSU8Pigjhw5eozfWzeeOY5Auu+misizInKGiHQteQU9MhMS+nZoxOpt+2wuPPOTNU1J4u7ebfhiWSHvfF/hRCymmgokKXXG94zSA8AT7vX3YAZlQseFOb658CYu2Ox1KCYCXHt6Ft2y6vLAB7bEhSlbhUnJfY9U+mVDwquJ1FrxdM+ux8cL7Hsl89P5RuN14lCxdeOZsgW08qyp3vp1SGPF1r2ssC48Uwmy6ydxV+/WfL50K+/NsW4880OWlDwULn8k9mnXCBH4eKF14ZnKcV3PbLo2qcNfPljMVuvGM34sKZkKNaidQLem9ZgYYV14YfI3QUSKdt14B44c5Y/vL7RuPPM/FSYlEbm0jNd5ItKgKgKMdEJ4TFLZt0Mjlm7ew6rCvV6HUqnC46cfmVo0qMnwC1rx6eItfDA/sv7gMScvkDulG/DN4nCle/0L3+qtU0Xk6iDGZkJIn/aNAOvCM5Xrxl7ZdMpM5v4Ji9i+95DX4ZgQEEhSigHaquplqnoZkIOv56MHP1xa3ESwtOQadG1Sx2Z3MJUqJjqKxy/vxJ6DR7hvwiKvwzEhIJCk1FhVt/i93+rKdvD/k7WWSUT6iMgyEckXkRFl7I8Xkbfc/hkikuW3715XvkxEelfUpogMc2UqIvX9ykVEnnb75vs/+CsiQ0VkhXuVrIBrytGvQxoLN+xm3fb9XodiIkirhrW4/dyWfDR/E5Psj55qL5Ck9KWIfOh+gQ8FxruyJGBXeZXcvHnPAX3x3V0NEZGcUofdAOxU1RbAk8Cjrm4OvlVo2wF98K0aG11Bm1OB84HSq+T2xbecekvgJuB5d456wJ/x3fF1B/4sInUD+HlUW//fhWe/OEzl+tXZzWmXXps/vr+QnfsOex2O8VAgSek24FV8Mzt0Bl7Htzz5PlU95zj1ugP5qrpKVQ8DY4CBpY4ZCLzmtscB54lvecqBwBhVPaSqq4F81165barqHFVdU0YcA4HX1Wc6UEdE0oDewGRV3aGqO4HJ+BKgKUdm3UQ6ZSYz0b5XMpUsNjqKxwd1Ytf+I/zlA+vGq84CmdFBVXWcqt7hXuM0sPGbGYD/cpMFrqzMY1S1GCgCUo5TN5A2A43jZNqq9vp2SGPe+l0U7LQuPFO5ctJrc+s5LXh/7kY+W7yl4gomIgU6JHyFiBSJyG4R2SMi1WqBHRG5SUTyRCSvsLDQ63A81dd14U2yuyUTBMPOaUGbRrX4/XsLKNp/3K+sTYQKpPvuMWCAqiaram1VraWqtQOotwFo7Pc+05WVeYyIxADJwPbj1A2kzUDjCLgtVR2lqrmqmpuamlrB6SJb05Qk2qXXjrgHaU1oiIvxdeNt33eYBz9a7HU4xgOBJKUtqrrkJNqeBbQUkWwRicM3cKH04oATgJJRb4OAKa5rcAIw2I3Oy8Y3SGFmgG2WNgG4xo3COxUoUtVNwCfAhSJS1w1wuNCVmQr065DG9+t22WJtJig6ZCZz85nNGDe7gC+WbfU6HFPFAklKeW7Y9hD/WR0qquS+IxqG7xf9EmCsqi4SkQdEZIA7bDSQIiL5wHBghKu7CBgLLAYm4RtYcbS8NgFE5HYRKcB3xzNfRF5y55gIrMI3WOJfwK3uHDuAB/ElulnAA67MVMC68Eyw3X5eS1o0qMnv313A7oPWjVedxARwTG1gP747iRIKvFtRRVWdiC8p+Jfd57d9ELi8nLoPAw8H0qYrfxp4uoxyxTeCsKxzvAy8fNyLMD/SLLUmbRrV4uMFm7muZ7bX4ZgIlBAbzeODOnLZ89P428Ql/O3Sjl6HZKpIhUlJVa+rikBMeOnbPo2nPl/O1t0HaVA7wetwTATq0qQuN57RjFFfr+KiDun0alm/4kom7JXbfScid7t/n3EzIvzgVXUhRi4N43mq+3VohCp8ssi68EzwDL+gFc3qJ3HPO/PZe6jY63BMFTjed0olgxvygNllvEw11rJhLVo0qMlHYTwKz1ZLCH0JsdE8NqgjG4sO8NikpV6HY6pAud13qvqB+/e18o4xP52E8doJ/Tqk8eyUFRTuOURqrXivwzkpEs7/AaqJ3Kx6XHt6Fq9MXUO/Dmmc2izF65BMEAXy8GwrERklIp+KyJSSV1UEZ0LbRR3SOKYwybrwTJDd1bs1Teolcs878zlw+KjX4ZggCmRI+NvAHOCPwF1+L1PNtWpYkxYNajLRFmgzQZYYF8Ojl3Vk7fb9PP7JMq/DMUEUSFIqVtXnVXWmqs4ueQU9MhPyRIR+HdKYsXo7hXtsgTYTXKc1T+GqU5vwyrTV5K2xRwojVSBJ6QMRuVVE0kSkXskr6JGZsGBdeKYqjejblvTkGtw9bj4Hj1g3XiQKJCkNxdddN43/H3mXF8ygTPho1bAmzVOTrAvPVIma8TE8clkHVm3bx5OTl3sdjgmC4yYlEYkCrlLV7FKvZlUUnwlxIsJFHdOtC89UmTNapjK4W2P+9c0q5qzb6XU4ppIdNymp6jHg2SqKxYQp68IzVe33F7WlYe0E7h43n0PF1o0XSQLpvvtcRC4Te6DDlMO68ExVq50Qy18v7cCKrXt5+vMVXodQ+M92AAAeG0lEQVRjKlEgSelmfMPCD1XXRf7M8VkXnvHCOa0bcFnXTF74ahULCoq8DsdUkkCWQ6+lqlGqGneCi/yZasS68IwX7uufQ0pSHHeNm8fh4mNeh2MqQSB3SriF8LqLyJklr2AHZsKLdeEZLyQnxvLwJR1YunkPI7/M9zocUwkCmWboRuBrfAvr/cX9e39wwzLhRkS4yB6kNR64IKchAzun8+yUfJZssm8Wwl0gd0q/AboBa1X1HKALsCuQxkWkj4gsE5F8ERlRxv54t6ptvojMEJEsv333uvJlItK7ojbdEukzXPlbbrl0RORJEZnrXstFZJdfnaN++ypaVr3SRdos1Rd1TLcuPOOJ+3/WjjqJsdw1bh5Hjlo3XjgLJCkddCvEIiLxqroUaF1RJRGJBp4D+gI5wBARySl12A3ATlVtATwJPOrq5gCDgXZAH2CkiERX0OajwJOurZ2ubVT1DlXtrKqdgWf44Yq5B0r2qeoAzE8Sbl144byelfmhuklxPDCwPQs37GbU16u8Dsf8BIEkpQIRqQO8D0wWkfHA2gDqdQfyVXWVqh4GxgADSx0zEChZGmMccJ4bej4QGKOqh1R1NZDv2iuzTVfnXNcGrs2Ly4hpCPDfAGKvMpE00D4cu/Ai6Mdf7fXrkEa/Do3452crWL5lj9fhmJMUyOi7S1R1l6reD/wJGE3Zv/BLywDW+70vcGVlHqOqxUARkHKcuuWVpwC7XBtlnktEmgLZgP+yGwkikici00UkkGsyFejX0UbhGe88MLA9SfHR3DVuPsXWjReWAh1910tErlPVr4Dv+HFyCQeDgXGq6v/4d1NVzQWuAJ4SkeZlVRSRm1zyyissLKyKWMNW64a1wqoLz0SW+jXjuX9AO+at38Xob1d7HY45CYGMvvszcA9wryuKBd4IoO0NQGO/95murMxjRCQGSAa2H6dueeXbgTqujfLONZhSXXequsH9uwr4Et8gjh9R1VGqmququampqWVfrQHCswvPRJYBndK5IKchT0xezgrrxgs7gdwpXQIMAPYBqOpGoFYA9WYBLd2ouDh8SaH0CLcJ+GYhBxgETFFVdeWD3ei8bKAlMLO8Nl2dL1wbuDbHl5xERNoAdfHd5ZWU1RWReLddH+gJLA7gukwFrAvPeElEePiS9iTFRXPH2Ln2UG2YCSQpHXa/9BVARJICadh9vzMM33NNS4CxqrpIRB4QkZKRbqOBFBHJB4YDI1zdRcBYfEliEnCbqh4tr03X1j3AcNdWimu7xGB8Ayf8h1u1BfJEZB6+hPaIqlpSqgQlXXgfzd/odSimmmpQK4G/XdqBhRt288wUmxsvnMRUfAhjReRFfN1jvwSuB/4VSOOqOhGYWKrsPr/tg8Dl5dR9GHg4kDZd+Sp8o/PKauv+MsqmAR2OewHmpIgI/Tum8/SUFWzZfZCGtRO8DslUQ33ap3Fp1wye+yKfc9o0oGuTul6HZAIQyOi7v+Mbav0OvueT7lPVZ4IdmAlvAzqnowofzLO7JeOd+we0Iy25BneOncf+w8UVVzCeC2j0napOVtW7VPV3qjo52EGZ8Nc8tSbtM2pbUjKeqp0Qy+OXd2T1tn38beJSr8MxASg3KZUsUVHGy5auMAEZ2CmDeQVFrN62z+tQTDV2evP63NArm39PX8tXy+2RjlBXblIqWaKijJctXWEC0r9TGiIwYa7dLRlv3dW7NS0b1OSut+exa/9hr8MxxxFQ950xJyMtuQbds+oxft4GNNJmnzVhJSE2mid/0Zkd+w7zp/GLKq5gPGNJyQTVwM4ZrCrcx6KN1uNrvNU+I5nfnNeSD+ZtZIJ91xmyLCl5qDrcO/Rt34jYaLFfAiYk3HJ2c7o0qcMf31vA5qKDXodjymBJyQRV3aQ4zmqVygfzNnLsWGilYetRrH5ioqP4x887c+SoMnzsXI6G2GfSWFIKAZG/eMLPOqWzqeggs9bs8DoUY8iun8T9A3KYtnK7rb0UgiwpmaC7IKchNWKjGR+CXXiRtJ6VCdzPcxtzUYc0nvh0GXPXB7SQtqkilpRM0CXGxXBhu4ZMXLDJJsc0IUFE+OslHWhYO4HfjJnD3kM220OosKRkqsSATuns2n+Eb1bYw4smNCQnxvLU4M6s37Gf+8Yv9Doc41hSMlXijJap1EuK4905pZe5MsY73bLq8etzW/Lu9xsYP9c+m6HAkpKpEnExUQzolM7kRVso2n/E63CM+Z9fn9uC3KZ1+cN7C1m3fb/X4VR7lpRMlbmsayaHjx7jA1tnyYSQmOgonhrcGRH4zVtzOHLUvvf0kiUlU2XaZ9SmdcNavPN9gdehGPMDmXUT+eslHZizbhdPfbbc63CqtaAmJRHpIyLLRCRfREaUsT9eRN5y+2eISJbfvntd+TIR6V1Rm26J9Bmu/C23XDoicq2IFIrIXPe60a/OUBFZ4V4ly7KbIBERLjslgznrdrGycK/X4RjzAz/rlM7PczMZ+eVKm03cQ0FLSiISDTwH9AVygCEiklPqsBuAnaraAngSeNTVzcG3hHk7oA8wUkSiK2jzUeBJ19ZO13aJt1S1s3u95M5RD/gz0APfirV/FhFbmjLILu6cQZTAO7PtbsmEnr8MaE+rBrW44625bCo64HU41VIw75S6A/mqukpVDwNjgIGljhkIvOa2xwHniYi48jGqekhVVwP5rr0y23R1znVt4Nq8uIL4egOTVXWHqu4EJuNLgCaIGtRO4KxWqbw3Z4NN8WJCTo24aEZe1ZVDR47y6//Y90teCGZSygDW+70vcGVlHqOqxUARkHKcuuWVpwC7XBtlnesyEZkvIuNEpPEJxAeAiNwkInkikldYaLf1P9Vlp2Syqegg01Zu8zoUY36keWpN/nppB/LW7uTvny7zOpxqpzoMdPgAyFLVjvjuhl6r4PgfUdVRqpqrqrmpqamVHmB1c37bhtROiLEuPBOyBnbO4MoeTXjxq1V8tniL1+FUK8FMShuAxn7vM11ZmceISAyQDGw/Tt3yyrcDdVwbPziXqm5X1UOu/CXglBOIL6iq6yzVCbHR/KxTOpMWbWbPQXtmyYSmP/XPoV16be58ex7rd9jzS1UlmElpFtDSjYqLwzdwYUKpYyYAJaPeBgFT1LdE6QRgsBudlw20BGaW16ar84VrA9fmeAARSfM73wBgidv+BLhQROq6AQ4XujJTBS47JZODR47xwbxNnsVQTf8mMAFKiI1m5JVdOXZMGfbfOTZvYxUJWlJy3+8Mw/eLfgkwVlUXicgDIjLAHTYaSBGRfGA4MMLVXQSMBRYDk4DbVPVoeW26tu4Bhru2UlzbALeLyCIRmQfcDlzrzrEDeBBfopsFPODKqlR1naW6S+M6tGpYkzGz1nkdijHlapqSxOOXd2Te+l088KEto14VYio+5OSp6kRgYqmy+/y2DwKXl1P3YeDhQNp05avwjc4rXX4vcG8553gZePm4F2GCQkS4onsT7v9gMQs3FNE+I9mbOKrBelbmp+nTPo2bz2zGi1+vomNGHX7erXHFlcxJqw4DHUyIuqRLJvExUfx3pt0tmdB2V+/W9GpRnz++v9DWXwoyS0rGM8mJsVzUMY3xczeyz9azMSEsJjqKZ4Z0oUHteH7179kU7jlUcSVzUiwpGU9d2aMJew8V80EIrkprjL+6SXG8ePUp7DpwmNve/N4erA0SS0rGU12b1KVVw5rWhWfCQrv0ZB69rCMz1+zgoQ8Xex1ORLKkZDwlIgzp3oR5BUUs3FDkdTjGVGhg5wx+eUY2r323lrF56yuuYE6IJSXjuUttwIMJM/f0aUOvFvX5w3sLmLFqu9fhRBRLSsZzyYmx/KxTOu/N2UDRAZvhwYS+mOgonruyK03qJXLzG7NZs22f1yFFDEtKJiRce3oW+w8f5W3rDjFhIrlGLC9f2w0Brn91FkX77Q+qymBJyYSE9hnJdMuqy2vfrbElLUzYaJqSxItX57J+535ueXO2jcirBJaUTMi4rmc263cc4PMlNiuzCR/ds+vxyKUdmbZyO396fyFaXWdariSWlEzIuDCnIenJCbw6bY3XoRhzQi47JZNh57RgzKz1PP/VSq/DCWuWlDxlf1H5i4mO4urTspi2cjtLN+/2OhxjTsjwC1oxoFM6j01aZt+N/gSWlExIGdK9MQmxUbw6dU3Qz2W9LKYyRUUJf7+8E71a1GfEuwuYstS6oU+GJSWP2RzVP1QnMY5Lu2by7pwNbN1z0OtwjDkhcTFRvHD1KeSk1ebWN7/n+3U7vQ4p7FhSMiHnpjOaUXz0GC9/uyb4J7O/CkwlqxkfwyvXdaNR7QSuf3UW+Vv3eB1SWLGkZEJOVv0k+nZI443pa+1hWhOW6teM5/XrexATFcU1o2dSsNOWUw9UUJOSiPQRkWUiki8iI8rYHy8ib7n9M0Qky2/fva58mYj0rqhNt0T6DFf+llsuHREZLiKLRWS+iHwuIk396hwVkbnuVXqpduOhW85qzt5Dxbwxfa3XoRhzUpqkJPLa9d3Ye6iYK/41g81F1h0diKAlJRGJBp4D+gI5wBARySl12A3ATlVtATwJPOrq5gCDgXZAH2CkiERX0OajwJOurZ2ubYA5QK6qdgTGAY/5nf+AqnZ2rwGYkNE+I5mzWqXyytTVHDxy1OtwjDkp7dKTef2GHuzYd5gr/jXdvicNQDDvlLoD+aq6SlUPA2OAgaWOGQi85rbHAeeJiLjyMap6SFVXA/muvTLbdHXOdW3g2rwYQFW/UNWSe+fpQGYQrtUEwS1nN2fb3sM2E7MJa50b1+HV67qxefdBrvzXDLbvtQUCjyeYSSkD8P9tUuDKyjxGVYuBIiDlOHXLK08Bdrk2yjsX+O6ePvZ7nyAieSIyXUQuLu9CROQmd1xeYWFheYeZStYjux6nNK3L81+utLslE9Zys+rx0tBc1u3Yz1WjZ7Jr/2GvQwpZ1Wagg4hcBeQCj/sVN1XVXOAK4CkRaV5WXVUdpaq5qpqbmppaBdEa8K21dOcFrdhUdJD/zLBlLUx4O715fUZdk8vKrXsZPGq6LalejmAmpQ1AY7/3ma6szGNEJAZIBrYfp2555duBOq6NH51LRM4H/gAMUNX/fRJUdYP7dxXwJdDlxC/TBNPpLepzevMURn6Zz/7DxRVXMCaEndUqlZev7cba7fv5xYvfsanogNchhZxgJqVZQEs3Ki4O38CF0iPcJgBD3fYgYIr6ZjOcAAx2o/OygZbAzPLadHW+cG3g2hwPICJdgBfxJaStJScWkboiEu+26wM9AVvfOATdeWFrtu09zCtVMMuDMcHWq2V9Xr+hO4V7DnH5C9+xbrsNF/cXtKTkvt8ZBnwCLAHGquoiEXlAREpGuo0GUkQkHxgOjHB1FwFj8SWJScBtqnq0vDZdW/cAw11bKa5t8HXX1QTeLjX0uy2QJyLz8CW0R1TVklIIOqVpXc5t04AXv1ppzy2ZiNAtqx5v/rIHew8Vc/mL01ixxR6wLRFT8SEnT1UnAhNLld3nt30QuLycug8DDwfSpitfhW90Xuny88tpfxrQ4fhXYELFnRe2ov8z3/LcF/n8vl9br8Mx5ifrmFmHt246jatGz2DQC98x6upT6NEsxeuwPFdtBjqY8NYuPZnLT8nklamrWW1LT5sI0bpRLd695XTq14zj6tEz+WDeRq9D8pwlJQ/ZLNUn5ne9WxMfE83DH1kvq4kcjesl8s4tp9O5cR1+/d85jPp6ZbVeKNCSksfEJgQNWINaCQw7twWfLdnK18t/+vNiautZmRBRJzGO12/ozkUd0/jrxKXc++4CDhVXz2fzLCmZsHJdzyyapiRy/4RF9kCtiSgJsdE8M7gLt53TnDGz1jN41HS27K5+0xJZUjJhJT4mmgcHtmfVtn08M2XFT27PblRNKImKEu7q3YaRV3Zl2eY99H/mW2av3eF1WFXKkpIJO2e2SuWyrpm8+NUqFm+0ZdNN5OnXIY33bu1JjdhoBo+azkvfrOLYserR3WxJyYSlP/VvS53EWO5+Zx5Hjh7zOhxjKl3rRrWYMKwnZ7VqwEMfLeH612axrRpM5mpJyYSlOolxPHRxexZu2M0Tny73OhxjgqJOYhz/uuYUHhzYjmkrt9PnqW/4qhIG+YQyS0ombPVpn8YVPZrwwlcrK2U0njGhSES4+rQsJgzrSb2kWIa+PJO7x82L2NlNLCmZsHZf/xxaN6zF8LFzbWVPE9HaNKrNhGG9uOXs5rzz/QYu+MdXfLJos9dhVTpLSiasJcRG8+wVXThw+Cg3vDbLZhI3ES0hNpp7+rTh/Vt7klIznpv/PZsbXp3FqsK9XodWaSwpmbDXsmEtnr2iK0s27ea3Y+ZytJqMUjLVV4fMZCYM68mIvm2YsXoHvZ/6moc+XBwRXXqWlExEOKdNA/7UP4dPF29hxDvzq83wWVN9xUZH8auzmjPld2dxaZdMRk9dzZmPfcEzn69gz8HwTU6WlEzEuK5nNr89vyVvzy5gxLvz7Y7JVAsNaiXw6KCOfDCsF7lN6/LE5OX0evQLnv58BTv2hd+y60FdusKYqvbb81txTOHpz1ewbe9h/jm4M7USYr0Oy5iga5+RzOhru7GgoIh/fr6Cf0xezrNf5DOgUzrXnp5F+4xkr0MMSFDvlESkj4gsE5F8ERlRxv54EXnL7Z8hIll+++515ctEpHdFbbrVaGe48rfcyrQndQ4T3oZf0IqHL2nPV8sLuWTkNBZuKPI6JGOqTIfMZF4amsund5zJ5adk8tH8TfR/5lv6/fMbXvhqJRt2hfYS7EG7UxKRaOA54AKgAJglIhNKre56A7BTVVuIyGDgUeAXIpKDb6nzdkA68JmItHJ1ymvzUeBJVR0jIi+4tp8/0XOoalBm+bxk5FQOHfnhzAO7IuBLyVB1ZY+mZKckccfYuVz83FRu6JXNLWc3p05inNehGVMlWjWsxcOXdODuPm147/sC3p+7kUc+XsojHy+lU+M6nNmyPme0TKVLkzrERofONznB7L7rDuS7FWERkTHAQHxLnJcYCNzvtscBz4qIuPIxqnoIWO2WOC9ZVfZHbYrIEuBc4Ap3zGuu3edP4hzfVdYPwF96cg0OFf8wKWXUrUH/junBOJ0BTm9Rn09/exYPfrSYUd+s4j8z1jGwSzr9O6bTKbOO1+EZUyWSa8Rybc9sru2Zzdrt+/hg3kamLN3Kc1/k88yUfBJio2ibVpsOGcnkpNWmSUoijesmkpacQIwHySqYSSkDWO/3vgDoUd4xqlosIkVAiiufXqpuhtsuq80UYJeqFpdx/Mmco9I9d2XXYDVtjiM5MZa/X96JG8/I5vkvV/J2XgFvTF9HdJQQG21zhJvqpWlKEsPObcmwc1tSdOAI363cxszVO1m4oYh3Zhfw+uEfdhQlxUVTMyGGpLgY6teMZ+yvTgt6jDbQIQAichNwE0CTJk08jsacjDaNavPPwV3Yc/AI01ftYH7BLrbsPkjTlCSvQzPGE8k1YunTPo0+7dMAOHpM2bDzAAU797N+5342FR1k78Fi9h7yvWolVE26COZZNgCN/d5nurKyjikQkRggGdheQd2yyrcDdUQkxt0t+R9/Muf4AVUdBYwCyM3NtXHGYaxWQiwX5DTkgpyGXodiTEiJjhKapCTSJCXR0ziC2WE4C2jpRsXF4RtUMKHUMROAoW57EDBFfYvTTwAGu5Fz2UBLYGZ5bbo6X7g2cG2OP8lzGGOM8UjQ7pTc9zfDgE+AaOBlVV0kIg8Aeao6ARgN/NsNMtiBL8ngjhuLb1BEMXBbyai4stp0p7wHGCMiDwFzXNuczDmMMcZ4Q3w3DSZQubm5mpeX53UYxhgTNkRktqrmBnJs6AxON8YYU+1ZUjLGGBMyLCkZY4wJGZaUjDHGhAxLSsYYY0KGjb47QSJSCKw9yer1gW2VGI5XIuU6IHKuxa4j9ETKtVTGdTRV1dRADrSkVIVEJC/QYZGhLFKuAyLnWuw6Qk+kXEtVX4d13xljjAkZlpSMMcaEDEtKVWuU1wFUkki5Doica7HrCD2Rci1Veh32nZIxxpiQYXdKxhhjQoYlpSogIn1EZJmI5IvICK/jKYuIvCwiW0VkoV9ZPRGZLCIr3L91XbmIyNPueuaLSFe/OkPd8StEZGhZ5wrydTQWkS9EZLGILBKR34TjtYhIgojMFJF57jr+4sqzRWSGi/ctt4QLbgmWt1z5DBHJ8mvrXle+TER6V+V1+MUQLSJzROTDML+ONSKyQETmikieKwurz5ZfDHVEZJyILBWRJSJyWkhci6raK4gvfEtsrASaAXHAPCDH67jKiPNMoCuw0K/sMWCE2x4BPOq2+wEfAwKcCsxw5fWAVe7fum67bhVfRxrQ1W3XApYDOeF2LS6emm47Fpjh4hsLDHblLwC3uO1bgRfc9mDgLbed4z5z8UC2+yxGe/D5Gg78B/jQvQ/X61gD1C9VFlafLb+4XwNudNtxQJ1QuJYq/SFUxxdwGvCJ3/t7gXu9jqucWLP4YVJaBqS57TRgmdt+ERhS+jhgCPCiX/kPjvPomsYDF4TztQCJwPdAD3wPMcaU/mzhW2PsNLcd446T0p83/+OqMP5M4HPgXOBDF1fYXYc77xp+nJTC7rOFbwXu1bhxBaF0LdZ9F3wZwHq/9wWuLBw0VNVNbnszULKGeHnXFFLX6rp+uuC7ywi7a3FdXnOBrcBkfHcHu1S1uIyY/hev218EpBAC1wE8BdwNHHPvUwjP6wBQ4FMRmS0iN7mysPts4bvbLARecd2qL4lIEiFwLZaUTEDU92dQ2AzVFJGawDvAb1V1t/++cLkWVT2qqp3x3Wl0B9p4HNIJE5H+wFZVne11LJWkl6p2BfoCt4nImf47w+Wzhe8utCvwvKp2Afbh6677H6+uxZJS8G0AGvu9z3Rl4WCLiKQBuH+3uvLyrikkrlVEYvElpDdV9V1XHJbXAqCqu4Av8HVz1RGRmDJi+l+8bn8ysB3vr6MnMEBE1gBj8HXh/ZPwuw4AVHWD+3cr8B6+PxbC8bNVABSo6gz3fhy+JOX5tVhSCr5ZQEs32igO35e3EzyOKVATgJLRNEPxfT9TUn6NG5FzKlDkbvk/AS4Ukbpu1M6FrqzKiIgAo4ElqvoPv11hdS0ikioiddx2DXzfiy3Bl5wGlXMdJdc3CJji/tKdAAx2o9qygZbAzKq5ClDVe1U1U1Wz8H32p6jqlYTZdQCISJKI1CrZxveZWEiYfbYAVHUzsF5EWrui84DFhMK1VOWXa9X1hW/kynJ83wn8wet4yonxv8Am4Ai+v6JuwNeX/zmwAvgMqOeOFeA5dz0LgFy/dq4H8t3rOg+uoxe+Lof5wFz36hdu1wJ0BOa461gI3OfKm+H7ZZwPvA3Eu/IE9z7f7W/m19Yf3PUtA/p6+Bk7m/8ffRd21+Finudei0r+Xw63z5ZfDJ2BPPcZex/f6DnPr8VmdDDGGBMyrPvOGGNMyLCkZIwxJmRYUjLGGBMyLCkZY4wJGZaUjDHGhAxLSsZUIRGZ5v7NEpErKrnt35d1LmPCiQ0JN8YDInI28DtV7X8CdWL0/+eLK2v/XlWtWRnxGeMVu1MypgqJyF63+QhwhluX5w43+erjIjLLrVdzszv+bBH5RkQm4HviHhF5300IuqhkUlAReQSo4dp70/9c7in8x0VkofjWAvqFX9tf+q2p86abEQMReUR8a1LNF5G/V+XPyFRvMRUfYowJghH43Sm55FKkqt1EJB6YKiKfumO7Au1VdbV7f72q7nDTD80SkXdUdYSIDFPfBK6lXYrv6f1OQH1X52u3rwvQDtgITAV6isgS4BKgjapqyXRHxlQFu1MyJjRciG9usbn4ltpIwTe/G8BMv4QEcLuIzAOm45sMsyXH1wv4r/pmHd8CfAV082u7QFWP4ZuSKQvfchEHgdEicimw/ydfnTEBsqRkTGgQ4Neq2tm9slW15E5p3/8O8n0XdT6+Be464ZsfL+EnnPeQ3/ZRfAvvFeOb/Xoc0B+Y9BPaN+aEWFIyxht78C3XXuIT4Ba37AYi0srNRF1aMrBTVfeLSBt8S1OXOFJSv5RvgF+4761SgTM5zgzbbi2qZFWdCNyBr9vPmCph3ykZ4435wFHXDfcqvjWGsoDv3WCDQuDiMupNAn7lvvdZhq8Lr8QoYL6IfK++5SFKvIdvLaZ5+GZQv1tVN7ukVpZawHgRScB3Bzf85C7RmBNnQ8KNMcaEDOu+M8YYEzIsKRljjAkZlpSMMcaEDEtKxhhjQoYlJWOMMSHDkpIxxpiQYUnJGGNMyLCkZIwxJmT8H2MgVww+ZpRLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd983ecfa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### m.warm_up??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fd67d33006834cbfbf27cbd760c97cf6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch      trn_loss   val_loss                                \n",
      "    0      1.258426   2.262941  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "m.crit = mae\n",
    "m.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd982c55630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.sched.plot(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Notes\n",
    "+ Increasing number of [dates] improved.\n",
    "+ Increasing the range of mean improved.\n",
    "+ Increasing bs from 1000 to 10,000 decreased\n",
    "+ Decreasing bs from 1000 to 100 decreased\n",
    "+ Increasing szs to 20,10 did nothing.\n",
    "+ Increasing szs to 10,5,5 did nothing.\n",
    "+ Adding promos_i_before might help.\n",
    "+ Adding promos_i_ after might help.\n",
    "+ Adding more training dates with dropout seems to prevent the overfitting.\n",
    "+ Batch size of 2e3 seems to work the best."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "###### Large dataset and model\n",
    "+ Trying .25 dropout to reduce overfitting. Still overfits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "((val_df, trn_df), (val_y, trn_y)) = split_by_idx(val_idx, df, yl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_rf = RandomForestRegressor(n_estimators = 20, min_samples_leaf=1000, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#speeds up since scikt-learn code calls this function.\n",
    "x = np.array(trn_df, dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3h 29min 41s, sys: 1.13 s, total: 3h 29min 43s\n",
      "Wall time: 22min 17s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1000, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=20, n_jobs=-1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time m_rf.fit(x, trn_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6575779499725225"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_rf.score(trn_df, trn_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.65008719278136762"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_rf.score(val_df, val_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_imp2 = pd.DataFrame({'score': m_rf.feature_importances_, 'feature': df.columns })\n",
    "feat_imp2.sort_values('score', axis=0, ascending=False, inplace=True)\n",
    "top_features = feat_imp2[feat_imp2['score'] >= .0001]; \n",
    "top_features.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>feature</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sum_14_before</td>\n",
       "      <td>0.454851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean_14_before</td>\n",
       "      <td>0.314681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mean_30_before</td>\n",
       "      <td>0.061810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sum_30_before</td>\n",
       "      <td>0.054516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mean_40_before</td>\n",
       "      <td>0.029909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sum_40_before</td>\n",
       "      <td>0.028628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>promo_14_after</td>\n",
       "      <td>0.008888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>store_class_dow</td>\n",
       "      <td>0.005143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>dow</td>\n",
       "      <td>0.005110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>item_dow</td>\n",
       "      <td>0.004356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sum_7_before</td>\n",
       "      <td>0.003793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mean_7_before</td>\n",
       "      <td>0.003151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mean_14_dow_0_bfore</td>\n",
       "      <td>0.002191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mean_14_dow_1_bfore</td>\n",
       "      <td>0.002027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mean_14_dow_6_bfore</td>\n",
       "      <td>0.001125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>first_has_sales_day_in_last_30</td>\n",
       "      <td>0.000973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>day</td>\n",
       "      <td>0.000899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>item_day</td>\n",
       "      <td>0.000871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>mean_14_dow_4_bfore</td>\n",
       "      <td>0.000864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>store_class_day</td>\n",
       "      <td>0.000776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>mean_3_before</td>\n",
       "      <td>0.000719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>max_3_before</td>\n",
       "      <td>0.000716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>promos_14_before</td>\n",
       "      <td>0.000688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>has_promo_days_in_last_14</td>\n",
       "      <td>0.000669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>mean_14_dow_5_bfore</td>\n",
       "      <td>0.000661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>sum_3_before</td>\n",
       "      <td>0.000643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>item_mean_14_diff_before</td>\n",
       "      <td>0.000623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>item_mean_1_dow_6_bfore</td>\n",
       "      <td>0.000588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>mean_14_dow_3_bfore</td>\n",
       "      <td>0.000522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>mean_14_dow_2_bfore</td>\n",
       "      <td>0.000497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>max_14_before</td>\n",
       "      <td>0.000424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>last_has_sales_day_in_last_40</td>\n",
       "      <td>0.000422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>date_index</td>\n",
       "      <td>0.000417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>promo_7_after</td>\n",
       "      <td>0.000368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>item_mean_30_diff_before</td>\n",
       "      <td>0.000338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>onpromotion_at_14_days</td>\n",
       "      <td>0.000303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>item_month</td>\n",
       "      <td>0.000302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>store_class_month</td>\n",
       "      <td>0.000299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>std_40_before</td>\n",
       "      <td>0.000296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>onpromotion_at_13_days</td>\n",
       "      <td>0.000295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>month</td>\n",
       "      <td>0.000276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>item_mean_40_diff_before</td>\n",
       "      <td>0.000276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>onpromotion_at_11_days</td>\n",
       "      <td>0.000260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>onpromotion_at_12_days</td>\n",
       "      <td>0.000252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>has_promo_days_in_last_30</td>\n",
       "      <td>0.000247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>first_has_sales_day_in_last_40</td>\n",
       "      <td>0.000244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>last_has_sales_day_in_last_30</td>\n",
       "      <td>0.000217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>onpromotion_at_15_days</td>\n",
       "      <td>0.000205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>promo_3_after</td>\n",
       "      <td>0.000190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>item_max_3_before</td>\n",
       "      <td>0.000161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>std_14_before</td>\n",
       "      <td>0.000158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>item_promo_14_after</td>\n",
       "      <td>0.000151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>item_has_sales_days_in_last_40</td>\n",
       "      <td>0.000140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>item_nbr</td>\n",
       "      <td>0.000122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>mean_7_dow_1_bfore</td>\n",
       "      <td>0.000118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>promos_40_before</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>has_promo_days_in_last_40</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>store_class_mean_40_diff_before</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>item_promos_40_before</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>onpromotion_at_7_days</td>\n",
       "      <td>0.000101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            feature     score\n",
       "0                     sum_14_before  0.454851\n",
       "1                    mean_14_before  0.314681\n",
       "2                    mean_30_before  0.061810\n",
       "3                     sum_30_before  0.054516\n",
       "4                    mean_40_before  0.029909\n",
       "5                     sum_40_before  0.028628\n",
       "6                    promo_14_after  0.008888\n",
       "7                   store_class_dow  0.005143\n",
       "8                               dow  0.005110\n",
       "9                          item_dow  0.004356\n",
       "10                     sum_7_before  0.003793\n",
       "11                    mean_7_before  0.003151\n",
       "12              mean_14_dow_0_bfore  0.002191\n",
       "13              mean_14_dow_1_bfore  0.002027\n",
       "14              mean_14_dow_6_bfore  0.001125\n",
       "15   first_has_sales_day_in_last_30  0.000973\n",
       "16                              day  0.000899\n",
       "17                         item_day  0.000871\n",
       "18              mean_14_dow_4_bfore  0.000864\n",
       "19                  store_class_day  0.000776\n",
       "20                    mean_3_before  0.000719\n",
       "21                     max_3_before  0.000716\n",
       "22                 promos_14_before  0.000688\n",
       "23        has_promo_days_in_last_14  0.000669\n",
       "24              mean_14_dow_5_bfore  0.000661\n",
       "25                     sum_3_before  0.000643\n",
       "26         item_mean_14_diff_before  0.000623\n",
       "27          item_mean_1_dow_6_bfore  0.000588\n",
       "28              mean_14_dow_3_bfore  0.000522\n",
       "29              mean_14_dow_2_bfore  0.000497\n",
       "30                    max_14_before  0.000424\n",
       "31    last_has_sales_day_in_last_40  0.000422\n",
       "32                       date_index  0.000417\n",
       "33                    promo_7_after  0.000368\n",
       "34         item_mean_30_diff_before  0.000338\n",
       "35           onpromotion_at_14_days  0.000303\n",
       "36                       item_month  0.000302\n",
       "37                store_class_month  0.000299\n",
       "38                    std_40_before  0.000296\n",
       "39           onpromotion_at_13_days  0.000295\n",
       "40                            month  0.000276\n",
       "41         item_mean_40_diff_before  0.000276\n",
       "42           onpromotion_at_11_days  0.000260\n",
       "43           onpromotion_at_12_days  0.000252\n",
       "44        has_promo_days_in_last_30  0.000247\n",
       "45   first_has_sales_day_in_last_40  0.000244\n",
       "46    last_has_sales_day_in_last_30  0.000217\n",
       "47           onpromotion_at_15_days  0.000205\n",
       "48                    promo_3_after  0.000190\n",
       "49                item_max_3_before  0.000161\n",
       "50                    std_14_before  0.000158\n",
       "51              item_promo_14_after  0.000151\n",
       "52   item_has_sales_days_in_last_40  0.000140\n",
       "53                         item_nbr  0.000122\n",
       "54               mean_7_dow_1_bfore  0.000118\n",
       "55                 promos_40_before  0.000113\n",
       "56        has_promo_days_in_last_40  0.000111\n",
       "57  store_class_mean_40_diff_before  0.000109\n",
       "58            item_promos_40_before  0.000103\n",
       "59            onpromotion_at_7_days  0.000101"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = int(0.1 * len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "samp_df = df[n:].copy()\n",
    "samp_yl = yl[n:].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "samp_m = RandomForestRegressor(n_estimators=1, max_depth=4, min_samples_leaf=50, bootstrap=False, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "samp_df = np.array(samp_df, dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=False, criterion='mse', max_depth=4,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=50, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=1, n_jobs=-1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samp_m.fit(samp_df, samp_yl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.61597269369871965"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samp_m.score(samp_df, samp_yl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<title>Tree</title>\n",
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       "<text text-anchor=\"start\" x=\"617.833\" y=\"-2490.3\" font-family=\"Times,serif\" font-size=\"14.00\">mean_14_before ≤ 0.34</text>\n",
       "<text text-anchor=\"start\" x=\"666.833\" y=\"-2475.3\" font-family=\"Times,serif\" font-size=\"14.00\">mse = 1.0</text>\n",
       "<text text-anchor=\"start\" x=\"631.833\" y=\"-2460.3\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 1959926</text>\n",
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       "<text text-anchor=\"start\" x=\"2281.33\" y=\"-1899.3\" font-family=\"Times,serif\" font-size=\"14.00\">sum_14_before ≤ 1.54</text>\n",
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       "<text text-anchor=\"start\" x=\"3949.83\" y=\"-3962.3\" font-family=\"Times,serif\" font-size=\"14.00\">mean_14_before ≤ &#45;0.96</text>\n",
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       "<text text-anchor=\"start\" x=\"7232.33\" y=\"-3799.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 122001</text>\n",
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       "<text text-anchor=\"start\" x=\"5662.33\" y=\"-3237.3\" font-family=\"Times,serif\" font-size=\"14.00\">promo_14_after ≤ 3.07</text>\n",
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       "<text text-anchor=\"start\" x=\"5664.33\" y=\"-2054.3\" font-family=\"Times,serif\" font-size=\"14.00\">sum_30_before ≤ 0.57</text>\n",
       "<text text-anchor=\"start\" x=\"5704.33\" y=\"-2039.3\" font-family=\"Times,serif\" font-size=\"14.00\">mse = 0.53</text>\n",
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       "<text text-anchor=\"start\" x=\"5659.83\" y=\"-1743.3\" font-family=\"Times,serif\" font-size=\"14.00\">mean_14_before ≤ 1.17</text>\n",
       "<text text-anchor=\"start\" x=\"5704.33\" y=\"-1728.3\" font-family=\"Times,serif\" font-size=\"14.00\">mse = 0.52</text>\n",
       "<text text-anchor=\"start\" x=\"5678.33\" y=\"-1713.3\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 190286</text>\n",
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       "<text text-anchor=\"start\" x=\"5723.83\" y=\"-1623.3\" font-family=\"Times,serif\" font-size=\"14.00\">[0.93]</text>\n",
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       "<text text-anchor=\"start\" x=\"7258.33\" y=\"-2334.8\" font-family=\"Times,serif\" font-size=\"14.00\">mse = 0.54</text>\n",
       "<text text-anchor=\"start\" x=\"7232.33\" y=\"-2319.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 134629</text>\n",
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       "<text text-anchor=\"start\" x=\"7262.83\" y=\"-2038.8\" font-family=\"Times,serif\" font-size=\"14.00\">mse = 0.5</text>\n",
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       "<text text-anchor=\"start\" x=\"5664.33\" y=\"-1018.3\" font-family=\"Times,serif\" font-size=\"14.00\">sum_40_before ≤ 1.98</text>\n",
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       "<text text-anchor=\"start\" x=\"5664.33\" y=\"-566.3\" font-family=\"Times,serif\" font-size=\"14.00\">mean_40_before ≤ 3.4</text>\n",
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