{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dg/anaconda3/envs/fastai/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "from fastai.text import *\n",
    "from fastai.imports import *\n",
    "from fastai.structured import *\n",
    "from fastai.column_data import *\n",
    "from torch.nn import functional as F\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc\n",
    "import pdb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def scale_vars(df, mapper, scale_col_exc):\n",
    "    warnings.filterwarnings('ignore', category=sklearn.exceptions.DataConversionWarning)\n",
    "    if mapper is None:\n",
    "        map_f = [([n],StandardScaler()) for n in df.columns if is_numeric_dtype(df[n]) and n not in scale_col_exc]\n",
    "        mapper = DataFrameMapper(map_f).fit(df)\n",
    "    df[mapper.transformed_names_] = mapper.transform(df)\n",
    "    return mapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def proc_df2(df, y_fld = None, skip_flds=None, do_scale=True, scale_col_exc = None, na_dict=None,\n",
    "             preproc_fn=None, max_n_cat=10, subset=None, mapper=None):\n",
    "    if not skip_flds: skip_flds=[]\n",
    "    if subset: df = get_sample(df,subset)\n",
    "    df = df.copy()\n",
    "    if preproc_fn: preproc_fn(df)\n",
    "    if y_fld is not None: \n",
    "        y = df[y_fld].values\n",
    "        df.drop(skip_flds+[y_fld], axis=1, inplace=True)\n",
    "\n",
    "    if na_dict is None: na_dict = {}\n",
    "    for n,c in df.items(): na_dict = fix_missing(df, c, n, na_dict)\n",
    "    if do_scale: mapper = scale_vars(df, mapper, scale_col_exc)\n",
    "    for n,c in df.items(): numericalize(df, c, n, max_n_cat)\n",
    "    if y_fld is not None: \n",
    "        res = [pd.get_dummies(df, dummy_na=True), y, na_dict]\n",
    "    else:\n",
    "        res = [pd.get_dummies(df, dummy_na=True), na_dict]\n",
    "    if do_scale: res = res + [mapper]\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## app train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('data/application_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "sk_id = set(df_train.SK_ID_CURR.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "sk2tg = { k:v for k,v in df_train[['SK_ID_CURR', 'TARGET']].values}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cc train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_train = pd.read_csv('data/credit_card_balance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_train = cc_train[cc_train['SK_ID_CURR'].isin(sk_id)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_train.drop(['SK_ID_PREV'], axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_cats(cc_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_sk_id = set(cc_train['SK_ID_CURR'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_y = [sk2tg[i] for i in cc_sk_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_sk_id = list(cc_sk_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_valid, y_train, y_valid = train_test_split(cc_sk_id, cc_y, test_size=0.2, stratify = cc_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_train_proc, _, _ = proc_df2(cc_train, do_scale = True, scale_col_exc = ['SK_ID_CURR', 'MONTHS_BALANCE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_to_maxtrix(df, cols): return df[cols].values.astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_train_proc = cc_train_proc.sort_values(by = ['SK_ID_CURR', 'MONTHS_BALANCE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_train = cc_train.sort_values(by = ['SK_ID_CURR', 'MONTHS_BALANCE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cols = [i for i in cc_train_proc.columns if i not in ['SK_ID_CURR','MONTHS_BALANCE']]\n",
    "cc_train_group = cc_train_proc.groupby(['SK_ID_CURR']).apply(lambda x: df_to_maxtrix(x, cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "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>SK_ID_CURR</th>\n",
       "      <th>MONTHS_BALANCE</th>\n",
       "      <th>AMT_BALANCE</th>\n",
       "      <th>AMT_CREDIT_LIMIT_ACTUAL</th>\n",
       "      <th>AMT_DRAWINGS_ATM_CURRENT</th>\n",
       "      <th>AMT_DRAWINGS_CURRENT</th>\n",
       "      <th>AMT_DRAWINGS_OTHER_CURRENT</th>\n",
       "      <th>AMT_DRAWINGS_POS_CURRENT</th>\n",
       "      <th>AMT_INST_MIN_REGULARITY</th>\n",
       "      <th>AMT_PAYMENT_CURRENT</th>\n",
       "      <th>...</th>\n",
       "      <th>AMT_RECIVABLE</th>\n",
       "      <th>AMT_TOTAL_RECEIVABLE</th>\n",
       "      <th>CNT_DRAWINGS_ATM_CURRENT</th>\n",
       "      <th>CNT_DRAWINGS_CURRENT</th>\n",
       "      <th>CNT_DRAWINGS_OTHER_CURRENT</th>\n",
       "      <th>CNT_DRAWINGS_POS_CURRENT</th>\n",
       "      <th>CNT_INSTALMENT_MATURE_CUM</th>\n",
       "      <th>NAME_CONTRACT_STATUS</th>\n",
       "      <th>SK_DPD</th>\n",
       "      <th>SK_DPD_DEF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Active</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1347528</th>\n",
       "      <td>100006</td>\n",
       "      <td>-3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>270000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520387</th>\n",
       "      <td>100006</td>\n",
       "      <td>-2</td>\n",
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       "      <td>270000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Active</td>\n",
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       "      <td>100006</td>\n",
       "      <td>-1</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         SK_ID_CURR  MONTHS_BALANCE  AMT_BALANCE  AMT_CREDIT_LIMIT_ACTUAL  \\\n",
       "1636141      100006              -6          0.0                   270000   \n",
       "655566       100006              -5          0.0                   270000   \n",
       "1399895      100006              -4          0.0                   270000   \n",
       "1347528      100006              -3          0.0                   270000   \n",
       "520387       100006              -2          0.0                   270000   \n",
       "584804       100006              -1          0.0                   270000   \n",
       "\n",
       "         AMT_DRAWINGS_ATM_CURRENT  AMT_DRAWINGS_CURRENT  \\\n",
       "1636141                       NaN                   0.0   \n",
       "655566                        NaN                   0.0   \n",
       "1399895                       NaN                   0.0   \n",
       "1347528                       NaN                   0.0   \n",
       "520387                        NaN                   0.0   \n",
       "584804                        NaN                   0.0   \n",
       "\n",
       "         AMT_DRAWINGS_OTHER_CURRENT  AMT_DRAWINGS_POS_CURRENT  \\\n",
       "1636141                         NaN                       NaN   \n",
       "655566                          NaN                       NaN   \n",
       "1399895                         NaN                       NaN   \n",
       "1347528                         NaN                       NaN   \n",
       "520387                          NaN                       NaN   \n",
       "584804                          NaN                       NaN   \n",
       "\n",
       "         AMT_INST_MIN_REGULARITY  AMT_PAYMENT_CURRENT    ...      \\\n",
       "1636141                      0.0                  NaN    ...       \n",
       "655566                       0.0                  NaN    ...       \n",
       "1399895                      0.0                  NaN    ...       \n",
       "1347528                      0.0                  NaN    ...       \n",
       "520387                       0.0                  NaN    ...       \n",
       "584804                       0.0                  NaN    ...       \n",
       "\n",
       "         AMT_RECIVABLE  AMT_TOTAL_RECEIVABLE  CNT_DRAWINGS_ATM_CURRENT  \\\n",
       "1636141            0.0                   0.0                       NaN   \n",
       "655566             0.0                   0.0                       NaN   \n",
       "1399895            0.0                   0.0                       NaN   \n",
       "1347528            0.0                   0.0                       NaN   \n",
       "520387             0.0                   0.0                       NaN   \n",
       "584804             0.0                   0.0                       NaN   \n",
       "\n",
       "         CNT_DRAWINGS_CURRENT  CNT_DRAWINGS_OTHER_CURRENT  \\\n",
       "1636141                     0                         NaN   \n",
       "655566                      0                         NaN   \n",
       "1399895                     0                         NaN   \n",
       "1347528                     0                         NaN   \n",
       "520387                      0                         NaN   \n",
       "584804                      0                         NaN   \n",
       "\n",
       "         CNT_DRAWINGS_POS_CURRENT  CNT_INSTALMENT_MATURE_CUM  \\\n",
       "1636141                       NaN                        0.0   \n",
       "655566                        NaN                        0.0   \n",
       "1399895                       NaN                        0.0   \n",
       "1347528                       NaN                        0.0   \n",
       "520387                        NaN                        0.0   \n",
       "584804                        NaN                        0.0   \n",
       "\n",
       "         NAME_CONTRACT_STATUS  SK_DPD SK_DPD_DEF  \n",
       "1636141                Active       0          0  \n",
       "655566                 Active       0          0  \n",
       "1399895                Active       0          0  \n",
       "1347528                Active       0          0  \n",
       "520387                 Active       0          0  \n",
       "584804                 Active       0          0  \n",
       "\n",
       "[6 rows x 22 columns]"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_train.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data loader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SortishSamplerRNN(Sampler):\n",
    "    def __init__(self, data_source, sk_id):\n",
    "        self.data_source, self.sk_id = data_source, sk_id\n",
    "\n",
    "    def __len__(self): return len(self.sk_id)\n",
    "\n",
    "    def __iter__(self):        \n",
    "        d={}\n",
    "        for i in range(len(self.sk_id)): d.setdefault(len(self.data_source[self.sk_id[i]]), []).append(i)        \n",
    "        result = [d[n] for n in sorted(d, reverse=True)]\n",
    "        result = [i for j in result for i in j]\n",
    "        return iter(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ColumnarRNNDataset(Dataset):\n",
    "    def __init__(self, x, y):\n",
    "        self.x, self.y = x, np.array(y, dtype=np.float32)\n",
    "\n",
    "    def __len__(self): return len(self.y)\n",
    "\n",
    "    def __getitem__(self, idx): return [self.x[idx], self.y[idx]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ColumnarRNNModelData(ModelData):\n",
    "    @classmethod\n",
    "    def from_data_frames(cls, path, trn_x, val_x, trn_y, val_y, \n",
    "                         tr_sampler = None, val_sampler = None, \n",
    "                         bs=64, shuffle=False):\n",
    "        trn_ds  = ColumnarRNNDataset(trn_x, trn_y)\n",
    "        val_ds  = ColumnarRNNDataset(val_x, val_y)\n",
    "        return cls.get_dl(path, trn_ds, val_ds,\n",
    "                          tr_sampler = tr_sampler, val_sampler = val_sampler, \n",
    "                          bs = bs, shuffle=shuffle)\n",
    "    \n",
    "    @classmethod\n",
    "    def get_dl(cls, path, trn_ds, val_ds,\n",
    "               tr_sampler = None, val_sampler = None, \n",
    "               bs = 64, shuffle=False):\n",
    "        trn_dl = DataLoader(trn_ds, bs, shuffle=shuffle, sampler = tr_sampler, num_workers=1)\n",
    "        val_dl = DataLoader(val_ds, bs, shuffle=shuffle, sampler = val_sampler, num_workers=1)\n",
    "        return cls(path, trn_dl, val_dl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## build"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs = 1024; PATH = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "trn_samp = SortishSamplerRNN(cc_train_group, x_train)\n",
    "val_samp = SortishSamplerRNN(cc_train_group, x_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "md  = ColumnarRNNModelData.from_data_frames(PATH,\n",
    "                                            trn_x = x_train, val_x = x_valid, \n",
    "                                            trn_y = y_train, val_y = y_valid,\n",
    "                                            tr_sampler = trn_samp, val_sampler = val_samp,\n",
    "                                            bs=bs, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# architechture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "class linear_block(nn.Module):\n",
    "    def __init__(self, ip_sz, op_sz, drop):\n",
    "        super().__init__()\n",
    "        self.op_sz = op_sz\n",
    "        self.ln = nn.Linear(ip_sz, op_sz)\n",
    "        kaiming_normal(self.ln.weight.data)\n",
    "        if drop is not None:\n",
    "            self.bn = nn.BatchNorm1d(op_sz)\n",
    "            self.drp = nn.Dropout(drop)\n",
    "        \n",
    "    def forward(self, x): return self.drp(self.bn(F.relu(self.ln(x)))) if self.op_sz != 1 else self.ln(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "class rnn_block(nn.Module):\n",
    "    def __init__(self, input_size = 36, hidden_size = 64, num_layers = 2, wdrop = 0.2, dropouto = 0.4):\n",
    "        super().__init__()        \n",
    "        self.rnn = WeightDrop(nn.LSTM(input_size, hidden_size, num_layers, batch_first = True), wdrop)\n",
    "        self.dropouto = LockedDropout(dropouto)\n",
    "    \n",
    "    def forward(self, cc_inp_pack):\n",
    "        with warnings.catch_warnings():\n",
    "            warnings.simplefilter(\"ignore\")\n",
    "            packed_output, _ = self.rnn(cc_inp_pack)        \n",
    "        outp, _ = pad_packed_sequence(packed_output, batch_first=True)\n",
    "        outp = self.dropouto(outp)\n",
    "        return outp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "class attention(nn.Module):\n",
    "    def __init__(self, dim, ip_sz = 64, op_sz = 36, drop = 0.2):\n",
    "        super().__init__()\n",
    "        self.w = self.rand_p(ip_sz, op_sz)\n",
    "        if dim == 1:\n",
    "            self.v = self.rand_p(op_sz)  \n",
    "        else:\n",
    "            self.ln = linear_block(op_sz, op_sz, drop)\n",
    "            self.v = self.rand_p(op_sz, op_sz)\n",
    "           \n",
    "    @staticmethod\n",
    "    def rand_p(*sz): return nn.Parameter(torch.randn(sz)/math.sqrt(sz[0]))\n",
    "    \n",
    "    def forward(self, outp, mtrx = None):\n",
    "        we = outp@self.w\n",
    "        u = F.tanh(we)\n",
    "        at = F.softmax(u@self.v, 1)\n",
    "        xa = (at.unsqueeze(2)*outp).sum(1) if mtrx is None else self.ln((at*mtrx).sum(1))\n",
    "        return xa, at"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "class RNNModel(nn.Module):\n",
    "    def __init__(self, bs, cc_ref, cc_sk_id,  \n",
    "                 hd_sz, dropouti, wdrop, dropouto, \n",
    "                 att_ip_drop, concat_drop,\n",
    "                 ln_lyrs, ln_drop, ):        \n",
    "        super().__init__()\n",
    "        self.bs, self.cc_sk_id, self.cc_ref, self.hd_sz = bs, cc_sk_id, cc_ref, hd_sz\n",
    "        \n",
    "        self.dropouti = LockedDropout(dropouti)\n",
    "        self.rnn = rnn_block(hidden_size = hd_sz, wdrop = wdrop, dropouto = dropouto)      \n",
    "\n",
    "        self.att_RNN = attention(dim = 1, ip_sz = self.hd_sz, op_sz = self.hd_sz)\n",
    "        self.att_ip = attention(dim = 2, ip_sz = self.hd_sz, drop = att_ip_drop)\n",
    "        \n",
    "        self.concat_lyrs = linear_block(self.hd_sz*4+36, ln_lyrs[0], concat_drop)        \n",
    "        self.lns = nn.ModuleList([linear_block(ln_lyrs[i], ln_lyrs[i+1], ln_drop[i]) for i in range(len(ln_lyrs)-1)])\n",
    "    \n",
    "    def pad_from_inp(self, inp):\n",
    "        inp = [V(self.cc_ref[i]) for i in to_np(inp)]\n",
    "        lengths = [i.size()[0] for i in inp]\n",
    "        max_len = int(np.max(lengths))\n",
    "        pad_inp = torch.stack([nn.ConstantPad2d((0,0,0,max_len-i.size()[0]), 0)(i) for i in inp])\n",
    "        pad_inp_droped = self.dropouti(pad_inp)\n",
    "        return pack_padded_sequence(pad_inp_droped, lengths, batch_first=True), pad_inp\n",
    "    \n",
    "    def pool(self, x, bs, is_max):\n",
    "        '''pooling layer, stole from fastai'''\n",
    "        f = F.adaptive_max_pool1d if is_max else F.adaptive_avg_pool1d\n",
    "        return f(x.permute(0,2,1), (1,)).view(bs,-1)\n",
    "    \n",
    "    def forward(self, x_in):\n",
    "        #pdb.set_trace()\n",
    "        cc_inp, pad_inp = self.pad_from_inp(x_in)\n",
    "        \n",
    "        outp = self.rnn(cc_inp)\n",
    "        \n",
    "        att_rnn_out, at_rnn_m = self.att_RNN(outp)\n",
    "        att_inp_out, at_inp_out_m = self.att_ip(outp, pad_inp)\n",
    "        \n",
    "        avgpool, mxpool = self.pool(outp, len(x_in), False), self.pool(outp, len(x_in), True)\n",
    "        x = self.concat_lyrs(torch.cat([outp[:,-1,:], avgpool, mxpool, att_rnn_out, att_inp_out], 1))\n",
    "        for lyer in self.lns: x = lyer(x)\n",
    "        \n",
    "        return x.view(-1), at_rnn_m, at_inp_out_m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Build"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def bcewl(ip,tg): return F.binary_cross_entropy_with_logits(ip, tg, tg*0.6+0.2, size_average=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "[dropouti, wdrop, dropouto] = np.array([0.25, 0.2, 0.15])*0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "m = RNNModel(bs = bs, cc_ref = cc_train_group, cc_sk_id = cc_sk_id,\n",
    "             hd_sz = 128, dropouti = dropouti, wdrop = wdrop, dropouto = dropouto,\n",
    "             att_ip_drop = 0.1, concat_drop = 0.1, \n",
    "             ln_lyrs = [600, 400, 200, 100, 1], ln_drop = [0.1, 0.1, 0.1, 0.1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "RNN_learner = Learner.from_model_data(m, md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "RNN_learner.crit = bcewl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fd3a6234c9814b8783d0becd55ce0d17",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 57%|█████▋    | 39/68 [00:12<00:09,  3.08it/s, loss=384]"
     ]
    }
   ],
   "source": [
    "RNN_learner.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BnKq2KRtMGhCLMXqbSSlvNHdNBh3atOLiPu3sDgVw4QoC6GiMubT23ZQndIsJpXtMCIt2HmGqjRNolFKNa9fhAjam5/OHib3w9fHcmg81ceUKYq2I9Hd7JMplE/vHsTEjn5yiErtDUUo1krfWZNDK35cbkuwd2lqdKwliDLDZWgVuh7Xq2w53B6bObWL/OIyBxbuy7Q5FKdUI8k6W8tm2w1wztAPhrf3tDucHrtxiusztUag66dEuhK7RwSzacYTbbKgRr5RqXB9sOEBZZVWTGNpanSvF+jKBNsAV1tbGalM2EREm9o9jQ/ox8k46XVdJKdVMlFVU8c76TMb1iKZbTIjd4fyIK0uO/hp4D4ixtndF5JfuDkzV7LJ+cVQZWLxLRzMp1Zx9lXKEnKJS7myCg05c6YO4GzjPGPMnY8yfcKwGN929Yana9I4LJSEqWNeIUKqZm7smg8SoYM7v0fRWyHQlQQhQWe11pdWmbCQiXNYvlnX7j5F/qszucJRS9bD1wHG2HzzB1FHx+DSRoa3VudJJ/RawQUQ+sV5fBbzpvpCUqyb2j+Mfy7/nm11HudHmqo9KNSUl5ZW8+n/76Ny2NWO7RxEX7tk1VIwxHC8uJ6eohOzCUnIKS8gp+u9j9g+vSwkN9OOaoR09Gp+rak0QxpgXRWQ5juGuAtxpjNnq7sBU7fq2D6NzZGu+3HlEE4RS1SxMPsir3+774XXX6GDGdo9mTLcoRnRtS0igK38b18wYw+GCEnZmFbDz0An25Zwku7CU3KJScopKKK80P/lMaJAf7cKCiAkNJKlLBO3Cgrikb7tGiccdalpRLswYUygikUCGtZ15L9IYk+/+8FRNzoxmmr1qP8dPlRERHGB3SErZrqrK8ObqdAZ2DOfZawewem8eq/bm8eGmA8xbm4GfjzC4cxvGdItmTPcoBnYMx6+WRXmqJ4OUQwXstLYzt3d9fYSEqGBiw4JIjA4mJjSIdmGBxIQGERMWSLvQIKJDA2kV4OuJX0GjqSltvQ9cDmwGqqdCsV7rqjVNwMT+sfxzxfcsSc3m+mGd7A5HKdst+y6HjGPF/P2mwfSKDaNXbBjTxiZSWlHJ5szjrN6bx+p9eby8bA8vLd1DaKAfI7q2ZWz3KMZ0iyIhKpgjBSWOJJDlSAQphwo4Vi0ZdI8J4aLeMfTvEE6/DuH0jgsjyL95/ePvippWlLvcemxaMzfUj/TvEE7HiFYsSjmiCUIpYPaq/XRo04rL+sX+qD3Qz5dRXaMY1TWK3wLHT5Wx9vtjrN6Xy6q9eSxJdVQmaB3gS3GZY1zOmWRwQa8YBnT07mTgjCvlvpcZYy6srU3Z48xtprfWpFNQXN6kpukr5Wk7sk6wMT2fxyb1rvW2UURwAJMGxDFpQBzGGA7kF7Nqbx57sovoFhNCvw7h9GlBycCZmvoggoDWQJSIRPDfoa1hQHsPxKZcNLF/HLNW7mdJWjbXNtHREEp5wpur0wkJ9Kvz1bSI0KVtMF3aBrspsuapphQ7E0f/Qy/r8cz2H+A194emXDWwYzgd2rTSleZUi3b4xGm+2HGEG4d1IixIr6QbwzkThDHmf63+h98YYxKNMQnWNtAY86oHY1S1ODNpbtXeXApLyu0ORylb/GttBsYY7hgdb3coXsOVYn1/F5F+InK9iNx+ZvNEcMp1l/WPo7zSsDRVS4CrludkaQXvbzzAZf3j6BjR2u5wvIYrxfr+DPzd2iYAzwFXujkuVUeDO7UhLjyIRVqbSbVACzYdpKikguljdfR9Y3KlFtO1wIXAUWPMncBAINCtUak68/ERLu0Xy8q9uRTpbSbVglRWGeauSSepSwSDOrWxOxyv4kqCOG2MqQIqRCQMyMGFSXIiMldEckQk5az2X1qr0+0SkeeqtT8iIvus935W1x9EwaT+cZRVVPF/3+XYHYpSHvPNrqNkHT/NtLE6ZauxuZIgkkWkDTAbxyimLcBGFz43D7i0eoOITAAmAwOMMX2B5632PsCNQF/rM/8QkZY7+LiehnSOoF1YIF/u0NFMquWYvWo/nSNbc3Gf2Np3VnXiSif1z40xJ4wx/wQuBqZat5pq+9xK4Ox6TfcCzxhjSq19zvypOxn40BhTaoxJB/YBw+vwcygct5ku6xfH8j25nCytsDscpdxuc+Zxthw4wV2j4/FtguWym7tzJggRGXL2BkQCftbz+ugBjBWRDSKyQkSGWe0dgIPV9suy2lQdXdYvVm8zqRZj7up0woL8uC5Jy8y4Q02lNl6wHoOAJGA7jtnUA4ANOMp/1+d8EThWpRsGLBCRRJwvQPTTWrmAiMwAZgB07qwlrs+WFB9JTGggb65OZ2K/2FrLDSjVXB3ML+arlCPMGNeV4CZaLru5q2mi3ARjzAQgExhijEkyxgwFBuO4BVQfWcDHxmEjUAVEWe3V/wToCBw+R1yzrFiSoqOb3hJ9dvP1Ef54eR+2HzzBP1d8b3c4SrnNW2sy8BFh6qgudofitVz587KXMWbnmRfGmBRgUD3P9ylwAYCI9AACgDzgM+BGEQkUkQSgO651hCsnrhjYnssHxPHy0r2kHCqwOxylGl1hSTnzNx3g8gFxHl8triVxJUGkicgcERkvIueLyGwgrbYPicgHwDqgp4hkicjdwFwg0Rr6+iGODm9jjNkFLABSga+BXxhjKs91bFW7Jyf3IzI4gAcXbKOkXH+Vyrt8uPEAp8oqmaYT49xKjHF6q/+/Oziqut4LjLOaVgKvG2NK3BxbrZKSkkxycrLdYTRZ3+7O4c63NjFjXCJ/mNjb7nCUahTllVWc/9y3dG7bmg9njLQ7nGZJRDYbY5Jq28+VNalLgJesTTUjE3rGcPN5nZm9aj8X9orhvMS2doekVIN9lXKUwwUl/GVyP7tD8Xo1DXNdYD3uFJEdZ2+eC1E1xKMTe9MpojUPLdyucyNUs2eMYc6q/SRGBXNBrxi7w/F6NfVB/Np6vBy4wsmmmoHgQD9evH4gh06c5qkvUu0OR6kG2ZRxnB1ZBdw1JgEfnRjndjWtSX3Eesz0XDjKHZLiI5k5riv/XPE9F/dpx4W929kdklL1MmfVfiJa+3PNEF050RNqusVUJCKFTrYiESn0ZJCq4R64uDu9YkP53Uc7yT9VZnc4StVZRt4plqRlc+uILrQK0FJtnlDTRLlQY0yYky3UGBPmySBVwwX6+fLi9YMoOF3Go5/spLbRa0o1NXPXpOPv48NtI3VinKe4XIdBRGJEpPOZzZ1BKffo0z6MBy7uwVcpR/nPNqcT1ZVqkk4Ul7EwOYsrB7UnJjTI7nBaDFdWlLtSRPYC6cAKIAP4ys1xKTeZOa4rQ7tE8Mf/pHCk4LTd4Sjlkvc3HuB0eaWu+eBhrlxBPImjuN4eY0wCjtXl1rg1KuU2vj7CC9cNpKLS8Nt/79BbTarJK6uo4l9rMxjbPYpesXp325NcSRDlxphjgI+I+BhjvqX+tZhUExAfFcyjk3qzam8e767XQWqqaftix2GyC0u5e4xePXiaKwnihIiE4Cix8Z6I/C+gM66auVvO68y4HtH8dVEa6Xmn7A5HKaeqqgyzV6XTPSaE83to9WZPcyVBTAaKgQdwFNL7Hp0o1+yJCM9dM4BAP18eXLCNisoqu0Nq8U6XVTJn1X5yi0rtDqXJWLj5IGlHCrl3fFdEdGKcp7mSIGYA7Y0xFcaYfxljXrFuOalmLjY8iCev6sfWAyd4Y+V+u8Np0YwxPPLxDp76Mo0bZq0ju9D2Wpi2yztZytOLvmN4QiRTBusCk3ZwJUGEAYtFZJWI/EJEdBquF7nSWjvipSV7dO0IG/1rbQafbjvM1YM7kF1Qwg1vrOPwiZY9yuypL1IpLqvg6Sn99erBJrUmCGPME8aYvsAvgPbAChFZ6vbIlMecWTvioQXbde0IG2zKyOepL9O4qHcMz183kHemncexk2XcMGsdB/OL7Q7PFqv25vLptsPcO74b3WJC7A6nxarLgsU5wFHgGKBlFL1IRHAAz147gN3ZRby5Ot3ucFqUnMISfv7eFjpGtOKF6wfh4yMM6RzBu9POo6C4nBtnrSfzWMsaRFBSXsmjn6SQGBXMz8d3tTucFs2ViXL3ishyYBmO9aOnG2MGuDsw5VkTesYwrkc0b63J0KsIDymrqOLn723hZEkFb9yWRHgr/x/eG9ipDe9PH8GpsgpueGM9+3NP2hipZ72ybC8H8ot5ako/gvy15pKdXLmC6ALcb4zpa4z5szFGa0Z7qeljE8g7WcpnWobDI55elEZy5nGevXYAPWNDf/J+vw7hfDB9BOWVVdw4az37copsiNKzdh8tYtbK/Vw7tCOjukbZHU6L50ofxO+NMds8EYyy15huUfSKDWXO6v06w9rNPtmaxby1Gdw9JoErB7Y/536948L4cMYIqgzcOGs9u482XpIoKC5vUlcmVVWGP3yyk9AgP10it4moSx+E8nIiwrSxiezJPsmKPbl2h+O1Ug8X8sjHOxmeEMnvL+tV6/7d24Uyf+YIfH2EG2etY9fhho02O3CsmMc/28XIZ5ZxyUsr2Zie36DjNZYPNh1gc+ZxHpvUh8jgALvDUWiCUGe5cmB7YkIDmbNKO6vd4URxGTPfTSa8lT+v3TwEf1/X/hfsGh3C/BkjaeXvy82zN7Azq+5JYuuB4/zivS2Mf/5b3tuQyWX94ugc2Zp7391M1nF7R0vlFJbwzFffMaprW64eonMemgpNEOpHAvx8mDoqntX78kg9rOtCNaaqKsP987dxtKCE128dSnRoYJ0+Hx8VzPyZIwkN8uPmOevZeuB4rZ+prDIs3nWU6/65lin/WMuqvbnMPL8rq393AS9cP5DZU5Moq6xixtubKS6zr4LOX75IpbSiiqeu6qdzHpoQTRDqJ245rzOt/H2Zs1pnVzeml5ftZfnuXP50RV+GdI6o1zE6RbZm/syRRLQO4LY3N5Kc4fz20OmySt5Zn8lFL65g5jubOVJQwp+v6MO6Ry7kd5f2ol2YY02FrtEhvHLTYNKOFvKwTdV9v92dwxc7jnDfhG4kRuuch6ZEE4T6iTatA7g+qSOfbz+sJR8aybK0bF5Ztpdrh3bk1vMatt5WhzatWDBzJDGhgdw+dyMb9v+38k1uUSkvfrObUc8s44+fphAW5MdrNw9h+W/Gc+foBIIDf7oM/YSeMfzu0l58ueMI/1j+fYNiq6visgoe+ySFbjEhzDw/0aPnVrX76bdFKeCuMQm8vT6TeWsz+N2ltXekqnPLyDvF/fO30a9DWKPdQokND+LDGSO4ec4Gpr61kb9e1Z/kzHw+2nKI8soqLurdjuljExkWH+HS+WaOS+S7I4U8/81uerYL5aI+nqmo879L93LoxGnmzxhBoJ/OeWhq9ApCOdWlbTA/6xPLe+szOVWq1d3rq7isgpnvbMbXR3j9lqGNOvErJsyRJOLbBvPQwu18vOUQ1w7tyNIHz2f27UkMT4h0ORmJCM9cM4D+HcK5f/429ma7f85F6uFC5qxO54akTpyX2Nbt51N1pwlCndP0cQkUllSwMPmg3aE0S8YYfv/RTvbkFPHKjYPpFNm60c8RFRLIB9NH8Ler+7P29xfw9JT+dK3nffwgf1/euM2RxKa/nUxBcXkjR/tflVWGRz7ZSZtW/jwyUa9QmypNEOqchnaJZHDnNsxdk0FllU6cq6u31mTw2fbD/OaSnoxz42I3EcEB3DS8M21D6jYqypm48Fa8cdsQDp8o4b4PtrhtnZD3NmSy/eAJ/nh5H9q01jkPTZXbEoSIzBWRHBFJqdb2uIgcEpFt1jbRavcXkX+JyE4RSRORR9wVl6qb6WMTOZBfzDe7jtodSrOyYf8x/roojUv6tOPe85tXwbmhXSJ56qp+rNqbxzNffdfoxz9aUMJzX+9mbPcoJg869yxyZT93XkHMAy510v6SMWaQtS2y2q4DAo0x/YGhwEwRiXdjbMpFP+sbS6fIVsxepUNeXVVaUcl9H2ylS2RrXrh+ID4+zW9c//XDOnHHqHjmrE7no81ZjXrsJz7fRXmlznloDtyWIIwxKwFX5/AbIFhE/IBWQBmgs7SaAF8f4a7RCWw5cILNmbVPzFKw9vtj5BaV8uik3oQG+df+gSbq0Um9GdW1LY8RMJ+JAAAS70lEQVR8stOlSXmuWJqazVcpR/nVhd3p0ja4UY6p3MeOPoj7RGSHdQvqzGyhfwOngCPAAeB5Y0zTKBCjuD6pE2FBfszRqwiXLE3NpnWAL6O7Ne9qpP6+Prx28xBiw4KY+c7mBs+JOVVawZ/+k0KPdiFMH6tzHpoDTyeI14GuwCAcyeAFq304UIljxboE4CERcfoNEpEZIpIsIsm5uVpQzhOCA/24+bwuLN51lAPHWuYKZ66qqjIsTctmXPdor1jLICI4gNm3J3GqtIIZ72yu11ohJeWVrN9/jN9+tIPDBSX87er+BPjp+JjmwKP/lYwx2caYSmNMFTAbR2IAuBn42hhTbozJAdYASec4xixjTJIxJik62n0jQ9SP3TEqHh8R5q7RIn41STlcQHZhKRd7aKKZJ/SMDeXFGwax/eAJ/vDJzlrLceSfKuObXUd5elEaU/6xhv6PL+bGWev5cscR7jm/K0O7RHooctVQHp1JLSJxxpgj1sspwJkRTgeAC0TkXaA1MAJ42ZOxqZrFhgdx5cD2LEg+yAMX9SC8dfO9t+5OS1Kz8RGY0Mu7VuX9Wd9YHrioBy8t3UOfuDCmWbeIjDEczD/Npox8kjPz2Ziez/e5jiVSA3x9GNgpnGnWjO6hnSP1e9PMuC1BiMgHwHggSkSygD8D40VkEI5O6QxgprX7a8BbOBKGAG8ZY3a4KzZVP9PGJvLx1kO8tzGTn4/vZnc4TdKS1GyS4iO9cj2DX17Qje+OFvL0ojROFJeTnneKTRn55BSVAhAW5EdSfCTXDO3I8PhI+nUI94rbbC2Z2xKEMeYmJ81vnmPfkziGuqomrE/7MEZ3a8u/1mYwbUyi3kc+y8H8Yr47WsRjk7xzNTQfH+H56waScayYV7/dR4c2rRjVtS1J8ZEMi4+ke0xIsxzSq85Ni/WpOpk2NpE739rE59sPc83QjnaH06QsSc0G4KLe3tP/cLbgQD/+fc9IikoqiA0Psjsc5Wb6J6Cqk/E9oukeE8LsVbpu9dmWpGbTPSaE+CjvHt8fHOinyaGF0ASh6sSxbnUC3x0tYs2+Y7V/oIUoKC5nY0a+V41eUkoThKqzyYM6EBUSoOU3qvl2dw6VVcZj6ygo5QmaIFSdBfn7cvvIeFbsyWWPB9YNaA6WpGYTHRrIoI5t7A5FqUajCULVy60juhDk76PlN3AU51uxJ5eLesfoKB7lVTRBqHqJDA7gmiEd+XTrYXKKWva61ev353OytMKrRy+plkkThKq3u8ckUF5VxTvrMu0OxVZLUo/Syr/5F+dT6myaIFS9JUaHcGGvdry7PpPTZXUv4uYNjDEsTc1hXI8onTWsvI4mCNUgM89P5HhxOY9+spOqFrgsacqhQo4WlujtJeWVNEGoBhkWH8mDF/fg462HePbrxl+esqlbkuYoznehJgjlhbTUhmqwX17QjdyiUt5YuZ+okECmj2s5i8EsSc0mqYt3FudTSq8gVIOJCI9f2ZeJ/WP566I0PtnauGsYN1UH84tJO1LIRX28q7S3UmdoglCNwtdHeOmGQYzq2paHF+5g+e4cu0Nyu2VpjuJ8F/eJtTkSpdxDE4RqNIF+vrxx21B6tAvl3ne3NNpC903VkrRsusWEkODlxflUy6UJQjWq0CB/5t01jOjQQO6at4l9OSftDsktCk6Xs2F/vo5eUl5NE4RqdDGhQbx913B8fYSpczdytMD7Zlov351DRZXR6q3Kq2mCUG4RHxXMvDuHU3C6nKlzN1JQXG53SI1qSWo2USEBDO6kxfmU99IEodymX4dwZt02lPS8U0x7exMl5d4x27qsoooVu3O5sFc7Lc6nvJomCOVWo7pF8eINA0nOPM5972+lorLK7pAabEP6MYpKK/T2kvJ6miCU210+oD2PX9GXpWnZPPZpSrNfqnRJajZB/j6M6a7F+ZR305nUyiOmjoon72Qpf/+/fUSHBvLQJT3tDqleHMX5shnbPVqL8ymvpwlCecyDF/f4IUm0DQ7gjtEJdodUZ7sOF3K4oIT7L+5hdyhKuZ0mCOUxIsKTk/tx7GQZT3yRStuQQK4Y2N7usOpkSWo2InBhLy2vobyf9kEoj/Lz9eGVmwYzrEskDy7YxraDJ+wOqU6WpmUztHMEbUMC7Q5FKbfTBKE8Lsjfl9m3J9EuLIj73t/SbOZIHDpxml2HC3X0kmoxNEEoW4S39ufVm4eQXVjCb/69vVmMbFqaeqY4nyYI1TK4LUGIyFwRyRGRlGptj4vIIRHZZm0Tq703QETWicguEdkpIkHuik01DYM6teGRy3qzJDWbuWsy7A6nVkvTskmMDiYxOsTuUJTyCHdeQcwDLnXS/pIxZpC1LQIQET/gXeAeY0xfYDzQPO47qAa5c3Q8l/RpxzNfpbm1P6K0orJB62YXlpSzfv8xvXpQLYrbEoQxZiWQ7+LulwA7jDHbrc8eM8Z4R10GVSMR4X+uHUi7sCB+8Z57+iPyTpYy+dU1jHxmGe+uz6SyHmtnL9+dS3ml4WKt3qpaEDv6IO4TkR3WLagIq60HYERksYhsEZHf2hCXssmZ/oicosbvj8gpLOHGWevJPFZMt+gQHvs0hStfXc3mTFf/dnFYmppN2+AABneOqH1npbyEpxPE60BXYBBwBHjBavcDxgC3WI9TRORCZwcQkRkikiwiybm5uR4IWXmCO/ojjhSc5oZZ6zl84jTz7hzGwntG8urNg8k/VcY1r6/joQXbySmqvRR5eWUV3+7O4cLeMfhqcT7Vgng0QRhjso0xlcaYKmA2MNx6KwtYYYzJM8YUA4uAIec4xixjTJIxJik6OtozgSuPaMz+iKzjxdzwxnryikp55+7hnJfYFhHh8gHtWfrg+fx8fFc+236IC59fwZur0ymvoYjghv35FJVU6OJAqsXxaIIQkbhqL6cAZ0Y4LQYGiEhrq8P6fCDVk7Ep+zVWf8SBY47kcKK4jHemncfQLpE/ej840I/fXtqLxfePY2h8BE9+kcqkV1ax9vs8p8dbmuYozje2u/5BoloWdw5z/QBYB/QUkSwRuRt4zhrCugOYADwAYIw5DrwIbAK2AVuMMV+6KzbVdDW0PyI97xQ3zFrHqbIK3p8+gkE1LOiTGB3CW3cMY87tSZwur+Tm2Rv4xftbOHzi9A/7GGNYkprNmG7RtArQ4nyqZZHmMEHpXJKSkkxycrLdYSg3mLs6nb98kcpjk3ozbWyiS5/Zl3OSm2evp6LK8N608+gdF+by+UrKK5m1cj+vfbsPHxHuu6Ab08Ym8H3OKSa+sopnr+nPDcM61/fHUapJEZHNxpik2vbTYn2qSbpzdDzr9x/jma++Y2iXiFpHD+0+WsQtc9YDwoczRtCjXWidzhfk78uvLuzOlMEd+OuXafzP4t0sTD5Iz9hQROCCXtr/oFoeLbWhmqQz/RGx4UHc9/5WThSXnXPfXYcLuHHWOnx9hPkz654cqusU2Zp/3jaUt+8ajo+PsHhXNkM6RxAdqsX5VMujCUI1WT/qj1i4w2l/xM6sAm6evYFW/r7MnzGSro1UBmNcj2i+/vU4/nZ1f/50eZ9GOaZSzY0mCNWknZkfsTQtmzdXp//ova0HjnPznPWEBvkxf+ZI4qOCG/XcAX4+3DS8MwNr6OhWyptpglBN3p2j4/lZ33Y889V3bD1wHIDkjHxue3MjkcEBzJ85kk6RrW2OUinvowlCNXkiwnPV+iO+TjnK7XM3EhMayPwZI+nQppXdISrllTRBqGYhvJU/r1n9Efe8u5kObVrx4cwRxIZrVXil3EUThGo2BnZqw9NT+jO+ZzQfzBhBTKgmB6XcSedBqGbluqROXJfUye4wlGoR9ApCKaWUU5oglFJKOaUJQimllFOaIJRSSjmlCUIppZRTmiCUUko5pQlCKaWUU5oglFJKOdWsV5QTkVwg04Vdw4GCBu5T0/tRgPMFjZs+V343TfV8DT1WXT/v6v6NtZ9+55re+RpyrPp8tjG/c9X36WKMqX2RdWOM12/ArIbuU9P7QLLdP6M7fzdN9XwNPVZdP+/q/o21n37nmt75GnKs+ny2Mb9z9Tl/S7nF9Hkj7OPKMZojT/9cjXm+hh6rrp93df/G2k+/c03vfA05Vn0+25jfuTqfv1nfYmoqRCTZuLAAuFKNRb9zyhNayhWEu82yOwDV4uh3TrmdXkEopZRySq8glFJKOaUJQimllFOaIJRSSjmlCcIDRCRYRDaLyOV2x6K8m4j0FpF/isi/ReReu+NRzZsmiBqIyFwRyRGRlLPaLxWR3SKyT0R+78KhfgcscE+Uyls0xvfNGJNmjLkHuB7QYbCqQXQUUw1EZBxwEnjbGNPPavMF9gAXA1nAJuAmwBf421mHuAsYgKMsQhCQZ4z5wjPRq+amMb5vxpgcEbkS+D3wqjHmfU/Fr7yPn90BNGXGmJUiEn9W83BgnzFmP4CIfAhMNsb8DfjJLSQRmQAEA32A0yKyyBhT5dbAVbPUGN836zifAZ+JyJeAJghVb5og6q4DcLDa6yzgvHPtbIx5FEBE7sBxBaHJQdVFnb5vIjIeuBoIBBa5NTLl9TRB1J04aav1Pp0xZl7jh6JagDp934wxy4Hl7gpGtSzaSV13WUCnaq87AodtikV5P/2+Kdtogqi7TUB3EUkQkQDgRuAzm2NS3ku/b8o2miBqICIfAOuAniKSJSJ3G2MqgPuAxUAasMAYs8vOOJV30O+bamp0mKtSSimn9ApCKaWUU5oglFJKOaUJQimllFOaIJRSSjmlCUIppZRTmiCUUko5pQlCeZSInPTAOa50sQx7Y55zvIiMqsfnBovIHOv5HSLyauNHV3ciEn922XEn+0SLyNeeikl5niYI1SxZZbCdMsZ8Zox5xg3nrKl22XigzgkC+APw93oFZDNjTC5wRERG2x2Lcg9NEMo2IvKwiGwSkR0i8kS19k+tFfh2iciMau0nReQvIrIBGCkiGSLyhIhsEZGdItLL2u+Hv8RFZJ6IvCIia0Vkv4hca7X7iMg/rHN8ISKLzrx3VozLReRpEVkB/FpErhCRDSKyVUSWikg7q0T3PcADIrJNRMZaf11/ZP18m5z9IyoiocAAY8x2J+91EZFl1u9mmYh0ttq7ish665h/cXZFZq1g+KWIbBeRFBG5wWofZv0etovIRhEJta4UVlm/wy3OroJExFdE/qfaf6uZ1d7+FLjF6X9g1fwZY3TTzWMbcNJ6vASYhaNaqQ/wBTDOei/SemwFpABtrdcGuL7asTKAX1rPfw7MsZ7fgWOxHIB5wELrHH1wrK0AcC2Octg+QCxwHLjWSbzLgX9Uex3BfysQTANesJ4/Dvym2n7vA2Os552BNCfHngB8VO119bg/B6Zaz+8CPrWefwHcZD2/58zv86zjXgPMrvY6HAgA9gPDrLYwHNWcWwNBVlt3INl6Hg+kWM9nAI9ZzwOBZCDBet0B2Gn390o392xa7lvZ5RJr22q9DsHxD9RK4FciMsVq72S1HwMqgY/OOs7H1uNmHOsgOPOpcazDkSoi7ay2McBCq/2oiHxbQ6zzqz3vCMwXkTgc/+imn+MzFwF9RH6o1h0mIqHGmKJq+8QBuef4/MhqP887wHPV2q+ynr8PPO/kszuB50XkWeALY8wqEekPHDHGbAIwxhSC42oDeFVEBuH4/fZwcrxLgAHVrrDCcfw3SQdygPbn+BlUM6cJQtlFgL8ZY974UaNjwZuLgJHGmGIRWY5juVaAEmNM5VnHKbUeKzn397m02nM569EVp6o9/zvwojHmMyvWx8/xGR8cP8PpGo57mv/+bLVxuWiaMWaPiAwFJgJ/E5FvcNwKcnaMB4BsYKAVc4mTfQTHldpiJ+8F4fg5lBfSPghll8XAXSISAiAiHUQkBsdfp8et5NALGOGm868GrrH6Itrh6GR2RThwyHo+tVp7ERBa7fU3OKqwAmD9hX62NKDbOc6zFkdpb3Dc419tPV+P4xYS1d7/ERFpDxQbY97FcYUxBPgOaC8iw6x9Qq1O93AcVxZVwG041ro+22LgXhHxtz7bw7ryAMcVR42jnVTzpQlC2cIY8w2OWyTrRGQn8G8c/8B+DfiJyA7gSRz/ILrDRzgW40kB3gA2AAUufO5xYKGIrALyqrV/Dkw500kN/ApIsjp1U3H0F/yIMeY7INzqrD7br4A7rd/DbcCvrfb7gQdFZCOOW1TOYu4PbBSRbcCjwFPGmDLgBuDvIrIdWILjr/9/AFNFZD2Of+xPOTneHCAV2GINfX2D/16tTQC+dPIZ5QW03LdqsUQkxBhzUkTaAhuB0caYox6O4QGgyBgzx8X9WwOnjTFGRG7E0WE92a1B1hzPSmCyMea4XTEo99E+CNWSfSEibXB0Nj/p6eRgeR24rg77D8XRqSzACRwjnGwhItE4+mM0OXgpvYJQSinllPZBKKWUckoThFJKKac0QSillHJKE4RSSimnNEEopZRyShOEUkopp/4fM0bewq0AgvYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "RNN_learner.sched.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 1e-4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "403302af800c4c37b502103c289e4dfa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=3), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch      trn_loss   val_loss                             \n",
      "    0      143.26762  145.529378\n",
      "    1      143.522918 145.716162                           \n",
      "    2      143.170848 145.176775                           \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[array([145.17678])]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RNN_learner.fit(lr, n_cycle = 3, cycle_len = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "RNN_learner.save('RNN_learner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "it = iter(md.trn_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = next(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred, a1, a2 = RNN_learner.predict_array(to_np(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pss = [i for i,j in enumerate(y) if j==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos = 2\n",
    "df_ps = df_pss[pos]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_bar(df_ps, a1):\n",
    "    a1 = to_np(a1)[df_ps]\n",
    "    time_range = list(range(-len(a1)-1, -1))\n",
    "    plt.figure(figsize=(20,10))\n",
    "    plt.bar(time_range, a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_heatmap(df_ps, a2):\n",
    "    plt.gcf().clear()\n",
    "    fig, ax = plt.subplots(figsize=(100,50))\n",
    "    a2 = to_np(a2)[df_ps].T\n",
    "    time_range = list(range(-len(a2.T)-1, -1))\n",
    "    ax = sns.heatmap(a2, xticklabels = time_range, yticklabels=cols, cmap=\"YlGnBu\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_bar(df_ps, a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 7200x3600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_heatmap(df_ps, a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_line(df_ps, col):\n",
    "    cc_train[cc_train['SK_ID_CURR'] == x[df_ps]][['MONTHS_BALANCE', col]].plot.line('MONTHS_BALANCE', col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_line(df_ps, 'AMT_PAYMENT_CURRENT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_line(df_ps, 'AMT_CREDIT_LIMIT_ACTUAL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_line(df_ps, 'CNT_DRAWINGS_ATM_CURRENT')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos = 60\n",
    "df_ps = df_pss[pos]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_bar(df_ps, a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 7200x3600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_heatmap(df_ps, a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SWCUAiafNAODu17l7ubtXkDTiLnT3vwYWAReEbBOAR8Lyo2GdsH2hu3tIHxd6CQ0GhgAvdNg7EelCmhqB1QYgEWXaztKqHwLVZnYL8ApwX0i/D3jAzOpIfvmPA3D318xsDvA6sAWY7O6Nu3F+kS5rWxWQSgAST7sCgLs/BTwVlt+mhV487r4JuLCV/acB09p7kSJ7m9yDYHoOQGLSk8AiETQNBaE2AIlIAUAkgqbB4FQFJBEpAIhEsG0wOFUBSTwKACIRaDA4KQYKACIRaDA4KQYKACIRaDA4KQYKACIRpFMaDE7iUwAQiSCroSCkCCgAiETQ1AisXkASkQKASATbGoFVApB4FABEIsiqG6gUAQUAkQjSKcNMVUASlwKASCTZVErdQCUqBQCRSDJpUzdQiUoBQCSSTMrUCCxRKQCIRJJNp9QGIFEVMil8DzN7wcz+aGavmdk/hPTBZva8mS01s9lm1i2kdw/rdWF7Rd6xrgvpS8zsjD31pkS6gqQKSCUAiaeQEsCnwKnufixQCYwxs5OA24Hp7j4EWAdMDPknAuvc/XBgesiHmR1FMj3kUGAM8CszS3fkmxHpSjKplKqAJKpCJoV3d/8krGbDPwdOBeaG9JnAuWF5bFgnbB9tZhbSq939U3d/B6ijhSklRUpFJm2qApKoCmoDMLO0mdUCq4H5wFvAh+6+JWSpBwaF5UHACoCwfT0wID+9hX1ESk4mpSogiaugAODuje5eCZST/Go/sqVs4dVa2dZaejNmNsnMasyspqGhoZDLE+mSsumU5gOQqNrVC8jdPwSeAk4C+ppZJmwqB1aF5XrgYICwvQ+wNj+9hX3yz3Gvu49w9xFlZWXtuTyRLiWpAlIJQOIppBdQmZn1Dcs9ga8DbwCLgAtCtgnAI2H50bBO2L7Q3T2kjwu9hAYDQ4AXOuqNiHQ1SSOwSgAST6btLAwEZoYeOylgjrs/bmavA9VmdgvwCnBfyH8f8ICZ1ZH88h8H4O6vmdkc4HVgCzDZ3Rs79u2IdB3ZtNGoEoBE1GYAcPfFwHEtpL9NC7143H0TcGErx5oGTGv/ZYrsfTKplBqBJSo9CSwSSSZtbFY3UIlIAUAkkmxaJQCJSwFAJJJkMDiVACQeBQCRSNQNVGJTABCJJGkEVglA4lEAEIkkk9Z8ABKXAoBIJMmUkCoBSDwKACKRaD4AiU0BQCQSDQYnsSkAiESSSWkoCIlLAUAkkkw6xWYFAIlIAUAkkmza1A1UolIAEIkkk0qx1WGrSgESiQKASCSZdDJJngaEk1gUAEQiyaSSAKCuoBKLAoBIJJl08uenACCxFDIl5MFmtsjM3jCz18zs70J6fzObb2ZLw2u/kG5mdreZ1ZnZYjMbnnesCSH/UjOb0No5RUpBVlVAElkhJYAtwNXufiTJZPCTzewoYAqwwN2HAAvCOsCZJPP9DgEmAfdAEjCAqcCJJDOJTc0FDZFSlEmpBCBxtRkA3P09d385LH9MMiH8IGAsMDNkmwmcG5bHArM88RzQ18wGAmcA8919rbuvA+YDYzr03Yh0IU2NwOoKKpG0qw3AzCpI5gd+HjjA3d+DJEgA+4dsg4AVebvVh7TW0kVKUq4KSHMCSCwFBwAz2xf4N+Aqd/9oZ1lbSPOdpG9/nklmVmNmNQ0NDYVenkiXk6sCalQbgERSUAAwsyzJl/+/uPtDIfn9ULVDeF0d0uuBg/N2LwdW7SS9GXe/191HuPuIsrKy9rwXkS6lqRFYbQASSSG9gAy4D3jD3f8xb9OjQK4nzwTgkbz0S0NvoJOA9aGK6LfA6WbWLzT+nh7SREqSGoEltkwBef4K+DbwqpnVhrTrgduAOWY2EXgXuDBsewL4BlAHbAAuB3D3tWb2E+DFkO9md1/bIe9CpAvSk8ASW5sBwN3/k5br7wFGt5DfgcmtHGsGMKM9Fyiyt8rqQTCJTE8Ci0SybSgIlQAkDgUAkUi2VQGpBCBxKACIRLKtEVglAIlDAUAkkoy6gUpkCgAikTQ1AqsXkESiACASieYDkNgUAEQi2VYCUACQOBQARCLJtQGoEVhiUQAQiSTXC0jdQCUWBQCRSLIqAUhkCgAikWhOYIlNAUAkklwvIA0GJ7EoAIhEom6gEpsCgEgkaQ0GJ5EpAIhEYmZk06ZeQBKNAoBIRJlUSiUAiaaQKSFnmNlqM/tTXlp/M5tvZkvDa7+QbmZ2t5nVmdliMxuet8+EkH+pmU1o6VwipSaTNg0GJ9EUUgK4HxizXdoUYIG7DwEWhHWAM4Eh4d8k4B5IAgYwFTgROAGYmgsaIqUsm07RqCogiaTNAODuTwPbz907FpgZlmcC5+alz/LEc0BfMxsInAHMd/e17r4OmM+OQUWk5GRSptFAJZpdbQM4wN3fAwiv+4f0QcCKvHz1Ia21dJGSlk2nVAUk0XR0I3BLk8f7TtJ3PIDZJDOrMbOahoaGDr04kWKTSZsagSWaXQ0A74eqHcLr6pBeDxycl68cWLWT9B24+73uPsLdR5SVle3i5Yl0DZmUuoFKPLsaAB4Fcj15JgCP5KVfGnoDnQSsD1VEvwVON7N+ofH39JAmUtKyaXUDlXgybWUwsyrga8B+ZlZP0pvnNmCOmU0E3gUuDNmfAL4B1AEbgMsB3H2tmf0EeDHku9ndt29YFik56ZRpKAiJps0A4O7jW9k0uoW8Dkxu5TgzgBntujqRvVwmnVIVkESjJ4FFIsqm1Ags8SgAiESU9AJSCUDiUAAQiSibTmk+AIlGAUAkokzKNBSERKMAIBJRRk8CS0QKACIRZfUksETUZjfQYrN582bq6+vZtGlT7EsRabcePXpQXl5ONpsFwnwAqgKSSLpcAKivr6d3795UVFRg1tIQQyLFyd1Zs2YN9fX1DB48GMjNB6ASgMTR5aqANm3axIABA/TlL12OmTFgwIBmpddsKqVuoBJNlwsAgL78pcva/rObTms+AImnSwYAkb1FNqUpISUeBYBdNG/ePMyMN998E4Bly5ZhZvzoRz9qyvPBBx+QzWa54oormDZtGpWVlVRWVpJOp5uW77777haPf9NNN3HnnXcCcNlllzFo0CA+/fTTpuNWVFQAsHXrVq688kqGDRvG0UcfzfHHH88777zDiSeeSGVlJYcccghlZWVN51u2bFmL56uoqGDUqFHN0iorKxk2bBgATz31FGeddRYA999/P6lUisWLFzflHTZsWKvHzh3/6KOP5phjjuGrX/0qy5cvb9qWfz8qKyu57bbbANiyZQvXX389Q4YMado2bdq0pvudu7bW7tncuXNbvJbp06fTo0cP1q9f35T21FNP0adPH4477ji++MUvcs011zRtu//++9v8Pxw5ciTJUFjQ2NhIZWUlf/jDH1q9HzkZjQYqESkA7KKqqipOPvlkqqurm9IOO+wwHn/88ab1Bx98kKFDhwJwww03UFtbS21tLT179mxavvLKKws6XzqdZsaMHcfSmz17NqtWrWLx4sW8+uqrzJs3j759+/L8889TW1vLzTffzMUXX9x0vlzgaMnHH3/MihXJxG1vvPHGTq+nvLy86cu4UIsWLWLx4sV87Wtf45ZbbmlKz78ftbW1TJmSTDF94403smrVKl599VVqa2t55pln2Lx5c7vO2ZKqqiqOP/545s2b1yx91KhRvPLKK7zyyis8/vjjPPvss8227+z/8NBDD+W+++4D4Oc//znHH388X/7yl9u8lkxa8wFIPF2uF1C+f3jsNV5f9VGHHvOogz7H1LOH7jTPJ598wrPPPsuiRYs455xzuOmmm4Dki+zII4+kpqaGESNGMHv2bC666CJWrWpx7pt2ueqqq5g+fTrf/e53m6W/9957DBw4kFQqieXl5eW7fI6LLrqI2bNnc80111BVVcX48eN54IEHWsx71lln8fTTT7NkyRK+8IUvtOs8I0eObLXkk7NhwwZ+/etfs2zZMnr06AFA7969m+71rnrrrbf45JNPuOOOO7j11lu57LLLdsjTs2dPKisrWblyZcHHnT59OieffDIjR47kF7/4BS+88EJB+yWNwCoBSBwqAeyChx9+mDFjxnDEEUfQv39/Xn755aZt48aNo7q6mvr6etLpNAcddFCHnPOQQw7h5JNP3uEL+aKLLuKxxx6jsrKSq6++mldeeWWXz3HBBRfw0EMPAfDYY49x9tlnt5o3lUpx7bXXcuutt7b7PE8++STnnntu0/rGjRubVQHNnj2buro6DjnkEHr37t3+N7ITucA2atQolixZwurVq3fIs27dOpYuXcpXvvKVgo87cOBArrrqKkaOHMmNN95I//79C9ovkza2OmxVKUAi6NIlgLZ+qe8pVVVVXHXVVUDyhV9VVcXkyck0CGPGjOFHP/oRBxxwABdffHGHnvf666/nnHPO4Zvf/GZTWnl5OUuWLGHhwoUsXLiQ0aNH8+CDDzJ69A7TNbSpf//+9OvXj+rqao488kh69eq10/yXXHIJ06ZN45133ino+Keccgrvv/8++++/f4tVQPny2xcAfvOb3/Czn/2MNWvW8Ic//KHVnmBt9RCrrq5m3rx5pFIpvvWtb/Hggw82/d8988wzHHPMMSxZsoQpU6Zw4IEHFvS+ciZPnsyUKVNaLFW0JptOfoOt+ctnlPXu3q7zieyuTi8BmNkYM1tiZnVmNqWzz7+71qxZw8KFC/nOd75DRUUFd9xxB7Nnz25qAOzWrRtf+tKX+OlPf8r555/foec+/PDDqaysZM6cOc3Su3fvzplnnskdd9zB9ddfz8MPP7zL57j44ouZPHky48e3Ng/QNplMhquvvprbb7+9oGMvWrSI5cuXM3ToUH784x/vNO/hhx/Ou+++y8cffwzA5ZdfTm1tLX369KGxsZEBAwawbt26ZvusXbuW/fbbr9VjLl68mKVLl3LaaadRUVFBdXU1VVVVTdtHjRrV1JZyzz337BCU2pJKpdrdRfmrR5TRPZPiu7Nq2PDZlnbtK7K7OjUAmFka+CVwJnAUMN7MjurMa9hdc+fO5dJLL2X58uUsW7aMFStWMHjwYOrr65vy5L4UBwwY0OHnv+GGG5p6ugC8/PLLTW0MW7duZfHixRx66KG7fPzzzjuPa6+9ljPOOKOg/Jdddhm///3vaWhoKCh/z549ueuuu5g1axZr17Y+K2ivXr2YOHEiV1xxRdODU42NjXz22WcA7LvvvgwcOJAFCxYAyZf/k08+ycknn9zqMauqqrjppptYtmwZy5YtY9WqVaxcubJZjySAI444guuuu67gwLY7hg3qw8/HH8fi+g/5m//7sp4Klk7V2SWAE4A6d3/b3T8DqoGxnXwNu6WqqorzzjuvWdr555/frC586NChTJgwYY+cf+jQoQwfPrxpffXq1Zx99tkMGzaMY445hkwmwxVXXLHLx+/duzc//OEP6datW0H5u3XrxpVXXtliXXprBg4cyPjx4/nlL38J7NgGkOsFNG3aNAYOHMiwYcM47rjjGDVqFBMmTGhqV5k1axa33HILlZWVnHrqqUydOpXPf/7zTef53ve+R3l5OeXl5YwcOZLq6uod/u/OO++8Zj25cr7//e/z9NNPF1y9tTtOH3ogt553NP/x5waunbtY7QHSaSxXddEpJzO7ABjj7t8J698GTnT3Fr+xRowY4TU1Nc3S3njjDY488sg9fq0ie0prn+FfLFzKnb/7M0cP6kPvHl26eU46wEmHDeDK0UN2aV8ze8ndR7SVr7M/ZS1VkDaLQGY2CZgESc8XkVIx+ZTDAfiPPzeoKkg6ZaKgzg4A9cDBeevlQLNO8u5+L3AvJCWAzru0OKZNm8aDDz7YLO3CCy/khhtu2CPnO/HEE5ueKM554IEHOProo7vE8fdmZsYVpw7hilN37VefSHt1dhVQBvgzMBpYCbwIXOLur7WUX1VAsjfSZ1j2tKKsAnL3LWZ2BfBbIA3MaO3Lv43jaERQ6ZI68weXSFs6vaXJ3Z8AntjV/Xv06MGaNWs0J4B0ObkJYXJDW4jE1uW6GpSXl1NfX19wv3ORYpKbElKkGHS5AJDNZpum0xMRkV2nweBEREqUAoCISIlSABARKVGd+hxAe5lZA7C8zYwdYz/gg046V1em+1QY3afC6D4Vrj336lD6uosBAAAHT0lEQVR3L2srU1EHgM5kZjWFPDhR6nSfCqP7VBjdp8LtiXulKiARkRKlACAiUqIUALa5N/YFdBG6T4XRfSqM7lPhOvxeqQ1ARKREqQQgIlKiSjoAmFmlmT1nZrVmVmNmJ4R0M7O7w8T1i81seFvHKgVm9rdmtsTMXjOz/52Xfl24V0vMrLDJhPdyZnaNmbmZ7RfW9ZnKY2Z3mNmb4V7MM7O+edv0ecpjZmPCvagzsykdenB3L9l/wO+AM8PyN4Cn8pb/nWQGs5OA52Nfa+x/wCnA74HuYX3/8HoU8EegOzAYeAtIx77eyPfqYJIhz5cD+4U0faaa36PTgUxYvh24PSzr89T8PqXDPTgM6BbuzVEddfySLgGQTEf5ubDch22zk40FZnniOaCvmQ2McYFF5G+A29z9UwB3z80CPxaodvdP3f0doA44IdI1FovpwLU0n+5Un6k87v47d98SVp8jmR0Q9Hna3glAnbu/7e6fAdUk96hDlHoAuAq4w8xWAHcC14X0QcCKvHz1Ia2UHQGMMrPnzew/zOz4kK57lcfMzgFWuvsft9uk+9S6/0lSOgLdp+3t0fvR5YaDbi8z+z1wYAubbiCZmvIH7v5vZnYRcB/wdQqYvH5v1Ma9ygD9SKovjgfmmNlhlOC9auM+XU9SvbHDbi2klex9cvdHQp4bgC3Av+R2ayH/Xn2f2rBH78deHwDc/eutbTOzWcDfhdUHgX8Oy21OXr83auNe/Q3wkCcVky+Y2VaSsUlK7l61dp/M7GiSeus/htnqyoGXQ+cC3aftmNkE4CxgdPhcQQnepzbs0ftR6lVAq4CvhuVTgaVh+VHg0tBz4yRgvbu/F+MCi8jDJPcIMzuCpEHqA5J7Nc7MupvZYGAI8EK0q4zI3V919/3dvcLdK0j+eIe7+3+jz1QzZjYG+CFwjrtvyNukz1NzLwJDzGywmXUDxpHcow6x15cA2vBd4GdmlgE2AZNC+hMkvTbqgA3A5XEur6jMAGaY2Z+Az4AJ4Vfba2Y2B3idpCg/2d0bI15nsdJnqrlfkPT0mR9KS8+5+/fdXZ+nPO6+xcyuIOlVlgZmuPtrHXV8PQksIlKiSr0KSESkZCkAiIiUKAUAEZESpQAgIlKiFABEREqUAoCISIlSAJCowpDJD+StZ8yswcwez0s7Nwwb/KaZvWpm5+Ztu9/MVppZ97C+n5ktM7OjwzDftWa21szeCcu/N7OK8DxD/nXcZGbXhOWTwphHtWb2hpndtJPrvyxcb20YJnuumfXaLs8fzaxqu7T7zeyCVo75AzPbZGZ98tK+Fu7V2Xlpj5vZ18Jy1sxuM7OlZvYnM3vBzM4M25aF+5a7H3e39n6ktCgASGx/AYaZWc+wfhqwMrfRzI4lGahvrLt/ETgHuNPMjsk7RiPJgGJNwlO5le5eSfLk5N+H9Z0OTxDMBCaFfYcBc9rIPzsceyjJQ3IX513/kSR/Z18xs30KODfAeJInQM/bLr2eZLyhlvwEGAgMc/dhwNlA77ztp+Tuh7tfWeB1yF5OAUCKwb8D3wzL44H8X8vXALeGoYEJr/8L+Pu8PHcBPwhPdHeE/YH3wvka3f31QnYK598HWJeXfAnwAMncE+cUcIzPA/sCN5Lci3x/BNab2Wnb7dOL5Kn2v80brvt9d28rcEmJUwCQYlBNMv5LD+AY4Pm8bUOBl7bLXxPSc94F/hP4djvO+fm8KpFa4Pt526YDSyyZqep74bp25uJwjJVAf+Cx/G3AbJKgtv0XektyAfAZ4Atmtv92228hCQ75DgfedfePdnLcRXnv9wcFXIeUAAUAic7dFwMVJF9+T2y32dhx+NuW0m4lKRUU+pl+K69KpBL4p7zruRkYQfKr/RLgyTaONTsc40Dg1XAdhDkTGtx9ObAAGG5m/do41jiSCVG2Ag8BF+ZvdPdnwrFHFfY2m+RXAU1v576yl1IAkGLxKEldf9V26a+RfBnnG04yWFgTd68DaoGLOuJi3P0td7+HZM6IY81sQAH7OMmv/6+EpPHAF81sGcm0fp8Dzm9t/9CuMYRkgLRlJMGgpVLDNJq3BdQBh5hZ7xbyirRKAUCKxQzgZnd/dbv0O4HrzKwCILxeD/y0hWNMI2kz2C1m9k0LQ1SSfCE3Ah8WuPvJwFtmliL59X5M3vDQY9l5NdB44KZcfnc/CBhkZofmZ3L335FMznNsWN9AMpnR3WHIYMxsoJn9jwKvWUpUqQ8HLUXC3euBn7WQXmtmPwQeM7MssBm41t1rW8j7mpm9TFJC2B3fBqab2QaSIYn/uo0hiS82s5NJflDVA5eRlAJWuvvKvHxPA0fZtrmA/4+Z3RWWV5BUIZ253bHnkZQEnt8ufRrwSN76jSTtA6+b2SaS3lU/ztu+yMxy72Gxu1+6k/cjJULDQYuIlChVAYmIlChVAYkUwMwuZ9v80TnPuvvkGNcj0hFUBSQiUqJUBSQiUqIUAERESpQCgIhIiVIAEBEpUQoAIiIl6v8DDR+zYEKBCU0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_line(df_ps, 'AMT_INST_MIN_REGULARITY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_line(df_ps, 'AMT_DRAWINGS_POS_CURRENT')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = next(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred, a1, a2 = RNN_learner.predict_array(to_np(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pss = [i for i,j in enumerate(y) if j==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos = 30\n",
    "df_ps = df_pss[pos]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_bar(df_ps, a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7200x3600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_heatmap(df_ps, a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_line(df_ps, 'AMT_INST_MIN_REGULARITY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_line(df_ps, 'AMT_RECEIVABLE_PRINCIPAL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_line(df_ps, 'AMT_RECIVABLE')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  },
  "toc": {
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