takotab/_v2_seq_seq.ipynb

## _v2_seq_seq.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false",
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/takotab/gas-predictor/blob/master/_v2_seq_seq.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "Collapsed": "false",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "C5GSHodG-nPq",
    "outputId": "0559094a-2d5f-418c-c37b-34ea106f9c04"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
      "Collecting torch===1.2.0\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/30/57/d5cceb0799c06733eefce80c395459f28970ebb9e896846ce96ab579a3f1/torch-1.2.0-cp36-cp36m-manylinux1_x86_64.whl (748.8MB)\n",
      "\u001b[K     |████████████████████████████████| 748.9MB 20kB/s \n",
      "\u001b[?25hCollecting torchvision===0.4.0\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/06/e6/a564eba563f7ff53aa7318ff6aaa5bd8385cbda39ed55ba471e95af27d19/torchvision-0.4.0-cp36-cp36m-manylinux1_x86_64.whl (8.8MB)\n",
      "\u001b[K     |████████████████████████████████| 8.8MB 29.1MB/s \n",
      "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch===1.2.0) (1.17.4)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision===0.4.0) (1.12.0)\n",
      "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision===0.4.0) (4.3.0)\n",
      "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow>=4.1.1->torchvision===0.4.0) (0.46)\n",
      "Installing collected packages: torch, torchvision\n",
      "  Found existing installation: torch 1.3.1\n",
      "    Uninstalling torch-1.3.1:\n",
      "      Successfully uninstalled torch-1.3.1\n",
      "  Found existing installation: torchvision 0.4.2\n",
      "    Uninstalling torchvision-0.4.2:\n",
      "      Successfully uninstalled torchvision-0.4.2\n",
      "Successfully installed torch-1.2.0 torchvision-0.4.0\n"
     ]
    }
   ],
   "source": [
    "!pip3 install torch===1.2.0 torchvision===0.4.0 -f https://download.pytorch.org/whl/torch_stable.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "Collapsed": "false",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "qPMWfQ86-6Jh",
    "outputId": "eeb1161f-449f-4bc4-cb5a-8201a6b76811"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Running command git clone -q https://github.com/fastai/fastai_dev /tmp/pip-req-build-g1oa4agn\n"
     ]
    }
   ],
   "source": [
    "!pip install git+https://github.com/fastai/fastai_dev > /dev/null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "Collapsed": "false",
    "colab": {},
    "colab_type": "code",
    "id": "mfwaBFKc-8ZT"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tako/devtools/fastai_dev/env/lib/python3.7/site-packages/pandas/compat/__init__.py:85: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n",
      "  warnings.warn(msg)\n",
      "/home/tako/devtools/fastai_dev/env/lib/python3.7/site-packages/pandas/compat/__init__.py:85: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "# sys.path.append('..')\n",
    "# from pathlib import Path\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import numpy as np\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "Collapsed": "false",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "PI41wzRI_fg5",
    "outputId": "d5abf67a-565d-451f-f9de-2ba7f2746b0c"
   },
   "outputs": [],
   "source": [
    "from fastai2.test import *\n",
    "from fastai2.core import *\n",
    "from fastai2.layers import *\n",
    "from fastai2.data.all import *\n",
    "from fastai2.optimizer import *\n",
    "from fastai2.learner import *\n",
    "from fastai2.metrics import *\n",
    "from fastai2.text.core import *\n",
    "from fastai2.text.data import *\n",
    "from fastai2.text.models.core import *\n",
    "from fastai2.text.models.awdlstm import *\n",
    "from fastai2.callback.rnn import *\n",
    "from fastai2.callback.all import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "Collapsed": "false",
    "colab": {},
    "colab_type": "code",
    "id": "qn6CiuN3_g_1"
   },
   "outputs": [],
   "source": [
    "def plot_days(data, ax=None, left=None, right=None, figsize = None,title=None, spacing=.1,  **kwargs):\n",
    "    from pandas import plotting\n",
    "    figsize = ifnone(figsize,(20,10))\n",
    "    if ax is None: _,ax = plt.subplots(figsize=figsize)           \n",
    "    colors = getattr(getattr(plotting, '_matplotlib').style, '_get_standard_colors')(num_colors=len(left)+len(right))\n",
    "    for c in left+right:\n",
    "        if c not in data:\n",
    "            print(f'Warning{c} not in {data.columns}')\n",
    "    # First axis\n",
    "    ax = data.loc[:, left].plot(label=left, color=colors[:len(left)],ax = ax, **kwargs)\n",
    "    ax.set_ylabel(ylabel=left)\n",
    "    lines, labels = ax.get_legend_handles_labels()\n",
    "\n",
    "    for n in range(len(right)):\n",
    "        # Multiple y-axes\n",
    "        ax_new = ax.twinx()\n",
    "        ax_new.spines['right'].set_position(('axes', 1 + spacing * (n - 1)))\n",
    "        data.loc[:, right[n]].plot(ax=ax_new, label=right[n], color=colors[len(left)+n % len(colors)],**kwargs)\n",
    "        ax_new.set_ylabel(ylabel=right[n])\n",
    "\n",
    "        # Proper legend position\n",
    "        line, label = ax_new.get_legend_handles_labels()\n",
    "        lines += line\n",
    "        labels += label\n",
    "\n",
    "    ax.legend(lines, labels, loc=0)\n",
    "    if title is not None: ax.set_title(title)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "Collapsed": "false",
    "colab": {},
    "colab_type": "code",
    "id": "CVHQyiyEDz0f"
   },
   "outputs": [],
   "source": [
    "from copy import copy \n",
    "class Days(CollBase, GetAttr, FilteredBase):\n",
    "    def __init__(self,df, y_names, cat_var, con_var):\n",
    "        self.cat_var, self.con_var, self.y_names = cat_var, con_var, y_names                    \n",
    "        self.df = df\n",
    "        self.df.set_index('datetime',drop=False,inplace=True)  \n",
    "        self.cols = ['datetime']+self.cat_var+self.con_var\n",
    "        for c in self.cols+self.y_names:\n",
    "            if c not in self.df.columns:\n",
    "                print(f'Warning {c} not in {self.df.columns}')\n",
    "    def __repr__(self):\n",
    "        return str(self.df)\n",
    "    \n",
    "    def copy(self):\n",
    "        return copy(self)\n",
    "    \n",
    "    def repr(self):\n",
    "        return {'y_names':self.y_names,'cat_var':self.cat_var,'con_var': self.con_var }\n",
    "    \n",
    "    def show(self,plot=True,**kwargs):\n",
    "        title= f\"{self.df['loc'][0]} ({self.df['city'][0]}) from {str(min(self.df.loc[:,'datetime']))} to {str(max(self.df.loc[:,'datetime']))} #{self.df.shape[0]}\"\n",
    "\n",
    "        if plot:\n",
    "            plot_days(self.df,left=self.y_names+['pred'],right=self.con_var,title=title,**kwargs)\n",
    "        else:\n",
    "            return title+'\\n'+str(self.df.head(1)   ) \n",
    "            \n",
    "    @classmethod\n",
    "    def from_intervals(cls,intervals,seq_len,y_names, cat_var, con_var):\n",
    "        \n",
    "        cls(_df, y_names, cat_var, con_var)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "Collapsed": "false",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 645
    },
    "colab_type": "code",
    "id": "7CjQ6GAgAbJA",
    "outputId": "5cfab76f-75f5-4264-e053-304d614ad423"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def make_fake_data(num, l=None, locations = 4, jumps = True):\n",
    "    locs,citys  =['blm','cnt','rzb','ctl'],['ADM','ADM','ADM','RTD']\n",
    "    dfs = []\n",
    "    for i_loc in range(locations):\n",
    "        loc,city = locs[i_loc], citys[i_loc]\n",
    "        start = pd.Timestamp('2019-01-01')\n",
    "        for i in range(num):\n",
    "            l = ifnone(l,100)\n",
    "            t = i+np.arange(l)/12\n",
    "            _df = pd.DataFrame(data={'datetime':[start+pd.Timedelta(hours=x) for x in range(l)],\n",
    "                                'target':np.random.randn(l)*.5+np.sin(t)+np.cos(t/2), \n",
    "                                'pred':np.random.randn(l)*.5+np.sin(t),                                        \n",
    "                                'temp':np.random.randn(l)*2+np.cos(t)+np.cos(-t/2),\n",
    "                                'pressure':1000+100*(np.random.randn(l)*2+np.cos(-t)),\n",
    "                                'humidity':np.random.randn(l)*5+np.cos(t)*100,\n",
    "                                'loc':[loc]*l,\n",
    "                                'city':[city]*l})\n",
    "            dfs.append(_df)\n",
    "            start += pd.Timedelta(hours=l)\n",
    "            if np.random.rand()> 0.9 and jumps:\n",
    "                start += pd.Timedelta(hours=l*3)   \n",
    "                start+=(pd.Timedelta('1H')*np.random.choice(np.arange(24)))\n",
    "    return pd.concat(dfs)\n",
    "_df = make_fake_data(1,locations=1)\n",
    "it = Days(_df,['target'],['loc','city'],['temp','pressure','humidity'])\n",
    "it.show(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "Collapsed": "false",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "M-jtM40PAfoP",
    "outputId": "ddd61e4f-1125-4f22-dd8b-db0a03f87e83"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40000, 8) 2019-01-01 00:00:00 2020-06-29 08:00:00\n"
     ]
    },
    {
     "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>datetime</th>\n",
       "      <th>target</th>\n",
       "      <th>pred</th>\n",
       "      <th>temp</th>\n",
       "      <th>pressure</th>\n",
       "      <th>humidity</th>\n",
       "      <th>loc</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01 00:00:00</td>\n",
       "      <td>1.305375</td>\n",
       "      <td>-0.978042</td>\n",
       "      <td>3.669741</td>\n",
       "      <td>893.347089</td>\n",
       "      <td>105.077794</td>\n",
       "      <td>blm</td>\n",
       "      <td>ADM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-01 01:00:00</td>\n",
       "      <td>0.459497</td>\n",
       "      <td>0.627992</td>\n",
       "      <td>0.657617</td>\n",
       "      <td>1031.848773</td>\n",
       "      <td>97.020287</td>\n",
       "      <td>blm</td>\n",
       "      <td>ADM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-01 02:00:00</td>\n",
       "      <td>1.839496</td>\n",
       "      <td>0.464550</td>\n",
       "      <td>-2.782163</td>\n",
       "      <td>813.688319</td>\n",
       "      <td>90.758840</td>\n",
       "      <td>blm</td>\n",
       "      <td>ADM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-01 03:00:00</td>\n",
       "      <td>1.170268</td>\n",
       "      <td>-0.225412</td>\n",
       "      <td>2.967662</td>\n",
       "      <td>999.629177</td>\n",
       "      <td>103.892557</td>\n",
       "      <td>blm</td>\n",
       "      <td>ADM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-01 04:00:00</td>\n",
       "      <td>1.817347</td>\n",
       "      <td>-1.164647</td>\n",
       "      <td>-1.323095</td>\n",
       "      <td>973.378147</td>\n",
       "      <td>92.042316</td>\n",
       "      <td>blm</td>\n",
       "      <td>ADM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime    target      pred      temp     pressure    humidity  \\\n",
       "0 2019-01-01 00:00:00  1.305375 -0.978042  3.669741   893.347089  105.077794   \n",
       "1 2019-01-01 01:00:00  0.459497  0.627992  0.657617  1031.848773   97.020287   \n",
       "2 2019-01-01 02:00:00  1.839496  0.464550 -2.782163   813.688319   90.758840   \n",
       "3 2019-01-01 03:00:00  1.170268 -0.225412  2.967662   999.629177  103.892557   \n",
       "4 2019-01-01 04:00:00  1.817347 -1.164647 -1.323095   973.378147   92.042316   \n",
       "\n",
       "   loc city  \n",
       "0  blm  ADM  \n",
       "1  blm  ADM  \n",
       "2  blm  ADM  \n",
       "3  blm  ADM  \n",
       "4  blm  ADM  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = make_fake_data(100,locations=4)\n",
    "print(df.shape, min(df.datetime),max(df.datetime))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faaf967abe0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "\n",
    "df = make_fake_data(100,locations=2,l=120,jumps=True)\n",
    "df.index = df.datetime\n",
    "df[['humidity']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "Collapsed": "false",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "NICS-GuwEgNL",
    "outputId": "1537af17-43f1-444d-8a71-bc1e227ccf4d"
   },
   "outputs": [],
   "source": [
    "from copy import copy\n",
    "def to_elapsed(s):\n",
    "    return s.astype(np.int64) // 10 ** 9\n",
    "\n",
    "def remove_last(df,start, end, sz):\n",
    "    old_start = copy(start)\n",
    "    start = end - sz + pd.Timedelta(hours = 1)\n",
    "    return df[start:end], old_start, start\n",
    "    \n",
    "def get_not_interval(df, interval, shift = 1):\n",
    "    df.index = df.datetime\n",
    "    df = df.sort_index()\n",
    "    df[\"delta\"] = abs(\n",
    "        (to_elapsed(df[\"datetime\"]) - to_elapsed(df[\"datetime\"].shift(shift)))\n",
    "    )\n",
    "    #     df[\"delta\"] = df['delta'].dt.seconds\n",
    "    not_hour = df.index[df[\"delta\"] != interval].tolist()\n",
    "    del df[\"delta\"]\n",
    "    return not_hour\n",
    "    \n",
    "\n",
    "def make_interval(\n",
    "    df: pd.DataFrame,\n",
    "    interval=3600,\n",
    "    sz='72H',\n",
    "    max_splits=35,\n",
    "    callback_error=None,\n",
    ") -> pd.DataFrame:\n",
    "    \"\"\"Will check if `df.datetime` has interval of `interval` in seconds. \n",
    "    \n",
    "    if not will make it happen and return a list where this is done.\n",
    "    \"\"\"\n",
    "    starts = get_not_interval(df, interval, 1)\n",
    "    ends = get_not_interval(df, interval, -1)\n",
    "    dfs = []\n",
    "    for start, end in zip(starts,ends):        \n",
    "        dfs += [df.loc[i:i+pd.Timedelta(sz),:] for i in pd.date_range(start,end,freq=sz) ]       \n",
    "\n",
    "    return dfs\n",
    "\n",
    "dfs = []\n",
    "for l in set(df['loc']):  \n",
    "    _df = df[df['loc'] == l]  \n",
    "    dfs += L(make_interval(_df)).map(lambda x: Days(x, ['target'], ['city','loc'], ['temp','pressure','humidity']))\n",
    "lengths =pd.DataFrame({'rows':[dfs[i].df.shape[0] for i in range(len(dfs))]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73    322\n",
       "48     10\n",
       "24      8\n",
       "72      2\n",
       "Name: rows, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(lengths['rows'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "Collapsed": "false",
    "colab": {},
    "colab_type": "code",
    "id": "wwb90BTEE4hG"
   },
   "outputs": [],
   "source": [
    "df = make_fake_data(100,locations=4,l=100,jumps=True)\n",
    "items = []\n",
    "for l in set(df['loc']):  \n",
    "    _df = df[df['loc'] == l]  \n",
    "    items += L(make_interval(_df)).map(lambda x: Days(x, ['target'], ['city','loc'], ['temp','pressure','humidity']))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "Collapsed": "false",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "2mvWiqJvRm9c",
    "outputId": "1e82b103-4d68-4438-805f-815700779024"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cnt (ADM) from 2020-06-30 04:00:00 to 2020-06-30 19:00:00 #16\n",
      "                               datetime    target      pred      temp  \\\n",
      "datetime                                                                \n",
      "2020-06-30 04:00:00 2020-06-30 04:00:00 -1.104032 -0.533417 -0.532171   \n",
      "\n",
      "                        pressure   humidity  loc city  \n",
      "datetime                                               \n",
      "2020-06-30 04:00:00  1166.979506  76.857514  cnt  ADM  \n",
      "blm (ADM) from 2019-01-01 00:00:00 to 2019-01-04 00:00:00 #73\n",
      "             datetime    target      pred      temp    pressure    humidity  \\\n",
      "datetime                                                                      \n",
      "2019-01-01 2019-01-01  0.594917 -0.325914  1.608822  891.552478  100.911778   \n",
      "\n",
      "            loc city  \n",
      "datetime              \n",
      "2019-01-01  blm  ADM  \n"
     ]
    }
   ],
   "source": [
    "print('\\n'.join([d.show(plot=False) for d in items[[0,-1]][::-1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "Collapsed": "false",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "lHCFsLGbRa4x",
    "outputId": "49c7fa4c-2e37-4e28-a15e-b77ffd5e254b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test set length\t\t 0.10801393728222997 620000\n",
      "Validation set length\t 0.09930313588850175 570000\n",
      "Train set length\t 0.7926829268292683 4550000\n",
      "5740000 620000\n"
     ]
    }
   ],
   "source": [
    "def split_idxs(items):\n",
    "    total = 0\n",
    "    train_idx, valid_idx, test_idx = [], [], []\n",
    "    train_idx_s, valid_idx_s, test_idx_s = 0,0,0\n",
    "    for idx, it in enumerate(items):\n",
    "        \n",
    "        ts = it.df['datetime'].iloc[0]\n",
    "        total += _df.shape[0]\n",
    "        if  ts.month == 2       :\n",
    "            if ts.minute == 0:\n",
    "                test_idx.append(idx)\n",
    "                test_idx_s += _df.shape[0]\n",
    "\n",
    "        elif ts.month == 9 or ts.month == 10:\n",
    "            if ts.minute == 0:\n",
    "                valid_idx.append(idx)\n",
    "                valid_idx_s += _df.shape[0]\n",
    "        else:\n",
    "            train_idx.append(idx)\n",
    "            train_idx_s +=_df.shape[0]\n",
    "\n",
    "    if len(valid_idx) == 0 and len(train_idx):\n",
    "        valid_idx.append(train_idx.pop(-1))\n",
    "    if len(test_idx) == 0 and len(train_idx):\n",
    "        test_idx.append(train_idx.pop(-1))\n",
    "    print()\n",
    "    print(\"Test set length\\t\\t\", test_idx_s / total, test_idx_s)\n",
    "    print(\"Validation set length\\t\", valid_idx_s / total,valid_idx_s)\n",
    "    print(\"Train set length\\t\", train_idx_s / total, train_idx_s)\n",
    "    print(total, test_idx_s)\n",
    "    \n",
    "    return [train_idx, valid_idx, test_idx]\n",
    "\n",
    "splits = split_idxs(items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-01-01 00:00:00')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def _to_elapsed(o:int):\n",
    "    return o // 10 ** 9\n",
    "def _to_dt(o):\n",
    "    return o*(10**9)\n",
    "pd.Timestamp(_to_dt(_to_elapsed(pd.Timestamp('2019-01-01').value)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "Collapsed": "false",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 544
    },
    "colab_type": "code",
    "id": "dHd6ICLjSNSX",
    "outputId": "fcb4fd20-5372-4289-e975-3af2f7a52f23"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                               datetime    target      pred      temp  \\\n",
       "datetime                                                                \n",
       "2019-01-01 00:00:00 2019-01-01 00:00:00  1.254589 -0.238976  2.590398   \n",
       "2019-01-01 01:00:00 2019-01-01 01:00:00  1.754765 -0.426422  4.002975   \n",
       "2019-01-01 02:00:00 2019-01-01 02:00:00  0.592527  0.296526  7.894488   \n",
       "2019-01-01 03:00:00 2019-01-01 03:00:00  0.192864 -0.119999  4.316580   \n",
       "2019-01-01 04:00:00 2019-01-01 04:00:00  0.997639 -1.423766  0.522560   \n",
       "...                                 ...       ...       ...       ...   \n",
       "2019-01-04 23:00:00 2019-01-04 23:00:00 -0.382488  0.807083 -0.482182   \n",
       "2019-01-05 00:00:00 2019-01-05 00:00:00  0.276797  1.316395  1.750470   \n",
       "2019-01-05 01:00:00 2019-01-05 01:00:00  0.142505  0.966143 -5.200077   \n",
       "2019-01-05 02:00:00 2019-01-05 02:00:00  0.587667  1.122057  1.630198   \n",
       "2019-01-05 03:00:00 2019-01-05 03:00:00  1.276253  0.193957  2.272730   \n",
       "\n",
       "                        pressure    humidity  loc city  \n",
       "datetime                                                \n",
       "2019-01-01 00:00:00  1296.575755   87.075818  blm  ADM  \n",
       "2019-01-01 01:00:00  1170.020548   88.370880  blm  ADM  \n",
       "2019-01-01 02:00:00  1184.275201  107.308536  blm  ADM  \n",
       "2019-01-01 03:00:00  1213.590941   99.937195  blm  ADM  \n",
       "2019-01-01 04:00:00  1204.928997   85.050706  blm  ADM  \n",
       "...                          ...         ...  ...  ...  \n",
       "2019-01-04 23:00:00   801.833672    9.221999  blm  ADM  \n",
       "2019-01-05 00:00:00  1117.700333  -13.098188  blm  ADM  \n",
       "2019-01-05 01:00:00   824.667608  -29.017559  blm  ADM  \n",
       "2019-01-05 02:00:00   977.961765  -38.042900  blm  ADM  \n",
       "2019-01-05 03:00:00  1154.918245  -32.614839  blm  ADM  \n",
       "\n",
       "[100 rows x 8 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class Change_dt(Transform):\n",
    "    def __init__(self, start=None, col='datetime'):\n",
    "        self.start = ifnone(start, _to_elapsed(pd.Timestamp('2019-01-01').value))\n",
    "        self.col = col\n",
    "        \n",
    "    def encodes(self, o:Days): \n",
    "        o.df[self.col] = _to_elapsed(o.df[self.col].astype(int)) - self.start\n",
    "        return o\n",
    "    \n",
    "    def decodes(self, o:Days): \n",
    "        o.df[self.col] = pd.to_datetime(_to_dt(o.df[self.col] + self.start))\n",
    "        o.df.index = o.df[self.col]\n",
    "        return o\n",
    "\n",
    "_df = make_fake_data(1,locations=1)\n",
    "it = Days(_df, ['target'],['loc','city'],['temp','pressure','humidity'])\n",
    "Dt = Change_dt()\n",
    "it = Dt(it) \n",
    "Dt.decodes(it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "Collapsed": "false",
    "colab": {},
    "colab_type": "code",
    "id": "uRL6lL3nTrSR"
   },
   "outputs": [],
   "source": [
    "test_eq(it.df, Dt.decode(Dt(it)).df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "class TensorSequenceX(TensorBase):pass\n",
    "class Numericalize(Transform):\n",
    "    \"Reversible transform of tokenized cat_var to numericalized ids\"\n",
    "    \n",
    "    def setup(self, dsrc):\n",
    "        if dsrc is None: return\n",
    "        self.item = dsrc[0]\n",
    "        self.vocab,self.o2i = {},{}\n",
    "        for cat in self.item.cat_var:\n",
    "            count = L()\n",
    "            for i in [list(self.item.df[cat].unique()) for o in dsrc]:\n",
    "                count.append(*i)\n",
    "            self.o2i[cat] = {k:i for i,k in enumerate(count.unique())}            \n",
    "            self.vocab[cat] = {v:k for k,v in self.o2i[cat].items()}\n",
    "\n",
    "    def encodes(self, o:Days): \n",
    "        for c in self.item.cat_var:\n",
    "            o.df[c] = o.df[c].map(self.o2i[c])\n",
    "        res = o.df[self.item.cols].values[None,:]\n",
    "#         print(res.shape) [1, 100, 6]\n",
    "        return TensorSequenceX(tensor(res))\n",
    "    \n",
    "    def decodes(self, o:TensorSequenceX):         \n",
    "        o = pd.DataFrame(o.numpy()[0,:],columns = self.item.cols )\n",
    "        for c in self.item.cat_var:\n",
    "            o[c] = o[c].map(self.vocab[c])\n",
    "        return Days(o,**self.item.repr())\n",
    "    \n",
    "    def get_vocab_sz(self):\n",
    "        return [len(self.vocab[v]) for v in self.item.cat_var]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'city': {0: 'ADM'}, 'loc': {0: 'blm'}} {'city': {'ADM': 0}, 'loc': {'blm': 0}}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1, 1]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NOTE must be right order\n",
    "num = Numericalize()\n",
    "num.setup(items.copy())\n",
    "print(num.vocab, num.o2i)\n",
    "num.get_vocab_sz()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning target not in Index(['datetime', 'city', 'loc', 'temp', 'pressure', 'humidity'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "_df = make_fake_data(1,locations=1)\n",
    "it = Days(_df.copy(), ['target'],['loc','city'],['temp','pressure','humidity'])\n",
    "_it = num(Dt(it.copy()))\n",
    "df = Dt.decode(num.decode(_it )).df\n",
    "test_eq(_df[it.cols],df[it.cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "class TensorSequenceY(TensorBase):pass\n",
    "class Label(Transform):    \n",
    "    def setup(self, dsrc):\n",
    "        if dsrc is None: return\n",
    "        self.item = dsrc[0]\n",
    "        \n",
    "    def encodes(self, o:Days): \n",
    "        print(o.df)\n",
    "        return TensorSequenceY(tensor(o.df[self.item.y_names].values[None,:]))\n",
    "    \n",
    "    def decodes(self, o:TensorSequenceY):         \n",
    "        o = pd.DataFrame(o[0,:].numpy(),columns = self.item.y_names )\n",
    "        return o\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.Days"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(items[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label: False (object,object) -> L \n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'L' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-66-d6d7bea00863>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/devtools/fastai_dev/fastai2/core/transform.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x, **kwargs)\u001b[0m\n\u001b[1;32m     85\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0muse_as_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mifnone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_item_force\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_item\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'encodes'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     88\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecode\u001b[0m  \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'decodes'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msetup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitems\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/devtools/fastai_dev/fastai2/core/transform.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, fn, x, split_idx, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msplit_idx\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit_idx\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit_idx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_as_item\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mretain_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/devtools/fastai_dev/fastai2/core/transform.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, f, x, **kwargs)\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mretain_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturns_none\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0madd_docs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTransform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Delegate to `decodes` to undo transform\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msetup\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Delegate to `setups` to set up transform\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/devtools/fastai_dev/fastai2/core/dispatch.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     96\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minst\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMethodType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__get__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mowner\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'L' object is not callable"
     ]
    }
   ],
   "source": [
    "label = Label(items[:10])\n",
    "print(label)\n",
    "label(items[0])\n",
    "#TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "#export\n",
    "def pad_input(samples, pad_idx=1, pad_fields=0, pad_first=False, backwards=False):\n",
    "    \"Function that collect samples and adds padding. Flips token order if needed\"\n",
    "    pad_fields = L(pad_fields)\n",
    "    max_len_l = pad_fields.map(lambda f: max([len(s[f]) for s in samples]))\n",
    "    if backwards: pad_first = not pad_first\n",
    "    def _f(field_idx, x):\n",
    "        if field_idx not in pad_fields: return x\n",
    "        idx = pad_fields.items.index(field_idx) #TODO: remove items if L.index is fixed\n",
    "        sl = slice(-len(x), sys.maxsize) if pad_first else slice(0, len(x))\n",
    "        pad =  x.new_zeros(max_len_l[idx]-x.shape[0])+pad_idx\n",
    "        x1 = torch.cat([pad, x] if pad_first else [x, pad])\n",
    "        if backwards: x1 = x1.flip(0)\n",
    "        return retain_type(x1, x)\n",
    "    return [tuple(map(lambda idxx: _f(*idxx), enumerate(s))) for s in samples]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor([1, 2, 3]), tensor([6, 0, 0])),\n",
       " (tensor([4, 5, 0]), tensor([4, 5, 0])),\n",
       " (tensor([6, 0, 0]), tensor([1, 2, 3]))]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_eq(\n",
    "    pad_input(\n",
    "        [(tensor([1, 2, 3]), 1), (tensor([4, 5]), 2), (tensor([6]), 3)], pad_idx=0\n",
    "    ),\n",
    "    [(tensor([1, 2, 3]), 1), (tensor([4, 5, 0]), 2), (tensor([6, 0, 0]), 3)],\n",
    ")\n",
    "pad_input(\n",
    "        [\n",
    "            (tensor([1, 2, 3]), (tensor([6]))),\n",
    "            (tensor([4, 5]), tensor([4, 5])),\n",
    "            (tensor([6]), (tensor([1, 2, 3]))),\n",
    "        ],\n",
    "        pad_idx=0,\n",
    "        pad_fields=[0,1],\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "class SeqLen(Transform):\n",
    "    def __init__(self,len=73):\n",
    "        self.len = len\n",
    "        \n",
    "    def encodes(self, o):   \n",
    "        a = torch.zeros(self.len,o.shape[1])\n",
    "        _len = min([self.len,o.shape[0]])\n",
    "        a[:_len,:] = o[:_len,:]\n",
    "        return a\n",
    "    \n",
    "    def decodes(self, o):\n",
    "        return o\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "seqlen = SeqLen(73)\n",
    "test_eq(seqlen(tensor(np.random.rand(50,3))).shape,(73,3))\n",
    "test_eq(seqlen(tensor(np.random.rand(50,8))).shape,(73,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "del seqlen\n",
    "seqlen = SeqLen(10)\n",
    "test_eq(seqlen(tensor(np.random.rand(50,3))).shape,(10,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-55-e21c094c23cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitems\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "it = items[0]\n",
    "it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df = make_fake_data(100,locations=4,l=140,jumps=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "items = []\n",
    "for l in set(df['loc']):  \n",
    "    _df = df[df['loc'] == l]  \n",
    "    items += L(make_interval(_df)).map(lambda x: Days(x, ['target'], ['city','loc'], ['temp','pressure','humidity']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test set length\t\t 0.07974683544303797 882000\n",
      "Validation set length\t 0.1759493670886076 1946000\n",
      "Train set length\t 0.7443037974683544 8232000\n",
      "11060000 882000\n"
     ]
    }
   ],
   "source": [
    "splits = split_idxs(items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tako/devtools/fastai_dev/env/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "dsrc = DataSource(items, [[Change_dt(), Numericalize(),SeqLen(73)],\n",
    "                          [Label(), SeqLen(73)]\n",
    "                         ],\n",
    "                  splits=splits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "The expanded size of the tensor (73) must match the existing size (6) at non-singleton dimension 1.  Target sizes: [1, 73].  Tensor sizes: [73, 6]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-4e05be870f9d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdsrc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/devtools/fastai_dev/fastai2/data/core.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, it)\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtl\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtl\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtls\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    206\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/devtools/fastai_dev/fastai2/data/core.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtl\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtl\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtls\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    206\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/devtools/fastai_dev/fastai2/data/core.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m    180\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_after_item\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_after_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_after_item\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[0;31m#Cell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/devtools/fastai_dev/fastai2/data/core.py\u001b[0m in \u001b[0;36m_after_item\u001b[0;34m(self, o)\u001b[0m\n\u001b[1;32m    168\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitems\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtfms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtfms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdo_setup\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplits\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit_idx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m_after_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtfms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    171\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34mf\"{self.__class__.__name__}: {self.items}\\ntfms - {self.tfms.fs}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/devtools/fastai_dev/fastai2/core/transform.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, o)\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcompose_tfms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtfms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit_idx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34mf\"Pipeline: {self.fs}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/devtools/fastai_dev/fastai2/core/transform.py\u001b[0m in \u001b[0;36mcompose_tfms\u001b[0;34m(x, tfms, is_enc, reverse, **kwargs)\u001b[0m\n\u001b[1;32m    147\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtfms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_enc\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/devtools/fastai_dev/fastai2/core/transform.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x, **kwargs)\u001b[0m\n\u001b[1;32m     85\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0muse_as_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mifnone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_item_force\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_item\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'encodes'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     88\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecode\u001b[0m  \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'decodes'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msetup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitems\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m~/devtools/fastai_dev/fastai2/core/transform.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, f, x, **kwargs)\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mretain_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturns_none\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0madd_docs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTransform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Delegate to `decodes` to undo transform\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msetup\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Delegate to `setups` to set up transform\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/devtools/fastai_dev/fastai2/core/dispatch.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     96\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minst\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMethodType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__get__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mowner\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-52-5a6131db2801>\u001b[0m in \u001b[0;36mencodes\u001b[0;34m(self, o)\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0m_len\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0m_len\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0m_len\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: The expanded size of the tensor (73) must match the existing size (6) at non-singleton dimension 1.  Target sizes: [1, 73].  Tensor sizes: [73, 6]"
     ]
    }
   ],
   "source": [
    "dsrc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": []
  }
 ],
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