attentive-neural-processes/smartmeters-lstm-deterministic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This notebook uses pytorch lightning & optuna & an LSTM\n",
    "\n",
    "This notebook trains an LSTM on timeseries data from smartmeters.\n",
    "\n",
    "It uses pytorch lighting for the training loop. And Optuna for the hyperparameter optimisation.\n",
    "\n",
    "It also pushes results to the tensorboard hyperparameter dashboard for examination.\n",
    "\n",
    "- https://github.com/optuna/optuna/blob/master/examples/pytorch_lightning_simple.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T02:36:50.055558Z",
     "start_time": "2020-02-16T02:36:49.665610Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T02:36:52.060467Z",
     "start_time": "2020-02-16T02:36:50.058998Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys, re, os, itertools, functools, collections\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import collections\n",
    "from pathlib import Path\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "import pytorch_lightning as pl\n",
    "import optuna\n",
    "from optuna.integration import PyTorchLightningPruningCallback\n",
    "\n",
    "\n",
    "import math\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T02:36:52.110420Z",
     "start_time": "2020-02-16T02:36:52.063895Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T02:36:52.251225Z",
     "start_time": "2020-02-16T02:36:52.113801Z"
    }
   },
   "outputs": [],
   "source": [
    "from src.models.model import LatentModel\n",
    "from src.data.smart_meter import collate_fns, SmartMeterDataSet, get_smartmeter_df\n",
    "# from src.plot import plot_from_loader\n",
    "from src.models.lstm import SequenceDfDataSet, LSTM_PL, plot_from_loader\n",
    "from src.dict_logger import DictLogger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T02:36:52.301399Z",
     "start_time": "2020-02-16T02:36:52.253328Z"
    }
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
    "logger = logging.getLogger(\"smartmeters.ipynb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T02:36:52.350206Z",
     "start_time": "2020-02-16T02:36:52.303615Z"
    }
   },
   "outputs": [],
   "source": [
    "# Params\n",
    "device='cuda'\n",
    "use_logy=False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load kaggle smart meter data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T02:37:01.154608Z",
     "start_time": "2020-02-16T02:36:52.352543Z"
    }
   },
   "outputs": [],
   "source": [
    "df_train, df_test = get_smartmeter_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T02:37:01.885138Z",
     "start_time": "2020-02-16T02:37:01.158353Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6537185828>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ujNjYcOhpIFzKhHAPC/b8YBhGMhHR0KvqZGBHpHZh6AKsUNVVqnoQ+AS4qgL7iUh5DG9ZXp5wHXTrtRuGkez45aPvKiLzROQbEenglrUC1nvaZLhlYRGR3iKSLiLpPmkKy4G8gqjamdvfMIxUwQ9DPxs4VlVPA14GvqrITlT1LVXtrKqdfdDk7rNk2fC50U+MMgzDSAUqbehVdY+q7nPfjwZqikgzYANwtKdpa7esSjNr7U7+OnSe5ckxDCNuVNrQi8iRIo6jQ0S6uPvcDswE2olIGxGpBVwPjKjs8cKxMHMPd36QTm5+dG4ZL14XjV/emkWZe3jgkzmFaZS93PrudD6fnVHqBC3DMAy/iTgzVkSGABcAzUQkA+gL1ARQ1TeA3wB/FJE8IBu4Xp3uap6I3AuMBaoDg1R1YSy+xITFmwFYvS2LE45oGKF1+Sir311aCuN7Pp7N6m1ZPNC9Hcc1b+CrHsMwjPIS0dCr6g0R6l8BXimlbjQwumLSEktZvXuxkVrDMJKI1JgZaxiGYZRKUiY1K40rX/mRS04+suI78KmjbgOthmEEiaTo0X8+K4O0PqPIyS17ADMnt4CR87zhk5ENrhR574+lDx21LBeP3QwMw4gXSWHo/zNhGQBb9x5IsJLyEc7Mm3/fMIx4kxSGPpHk5RewYVc2Q9PXl3yqKKVTbp11wzCCREr56GPBkyMX8eTIRdSvVR2AXftzo3bwWOfdMIwgUKV79AfzCrjzw/CpdXZn5xb5vD/C+EC0mG/eMIx4k1SG/oFP5vi6v8Ub97DKXegbivbAv56/sdTtIpnq0iZSGYZhJIKkMPQhAzx73a7YHieKNk8MX8COrANlbhDqtIeL4qnqg7H7D+bx16Hz2Jl1MNFSDKPKUKV99Nuzyh/FM27RZsYt2hxV2ypu08PyyYz1fD47g4Z1atDvyg6RNzAMo9IkRY++okRyh//+/Uqmvo/SQ3PN61O48e1plTtWimBOLcOIP1W6R18ehOiNVPEbzKy1O/2WYxiGETUp3aMvr+ukzJmscTh+WSzfvJehM9dHbhgnsg/ms277/kTLMAwjCgJv6Bdv3ONbaoLK4HdUZHl3d8l/JvPw5/OLlM1et5O0PqOYsboiS/pWjt6D0znv+Ynl3i4UXmrjF4YRPyIaehEZJCJbRGRBKfU3ich8EflZRKaIyGmeujVu+dyKrgXb878/FPm8fPPeiuwmrpQWK787O/rJVtHw4/JtAPywfKuPe42OH9xjG4YRfKLp0b8P9CijfjVwvqqeAgwA3ipWf6GqdvJrLdhL/jPZj93EheKuoFvfnZ4gJYZhVGUiGnpVnQyU6htQ1SmqGhptnIazNmxSUt7edmnul9LKl23eR7ZPM2yLk7krO7Czbm94axpvT15VpCwI7jjDqCr47aO/A/jG81mBcSIyS0R6l7WhiPQWkfSKunjCUV6757ffuPjusnPzyXPXkfXzUHPW7eIXA7/j0wAN1nqZumo7T49enGgZhlFl8c3Qi8iFOIb+EU/xuap6BtATuEdEzitte1V9S1U7h3PxVGTR70QSzQ3Gj7536GaxfIszbjFzjYVxGoZREl8MvYicCrwDXKWq20PlqrrB/bsF+BLoUpH9b9yd44fMiPjt+Yh3ZMnnszPie8BKMOin1YmWYBhVhkobehE5BvgCuEVVl3nK64tIw9B74FIgbOROULjZBksNw0hBogmvHAJMBU4UkQwRuUNE7haRu90mTwBNgdeKhVEeAfwoIvOAGcAoVR0Tg+9QKuUd+FyYuceX44ayV5Y14HjruzPYvKdyTyrJGIse0PFiw0hpIqZAUNUbItTfCdwZpnwVcFrJLeJHrCJcIlGYvbIMQzx3/S5en7TSEnsZhhFzLNdNJYgUzpi5K5v8gth1YZNsjNowjASR0ob+mVGJCenb4i5ifvv7M9m1PzdC64qx/2Be4aLphmEYZRH4XDeVIetgYlw3Ifbm5MVs33uyD+07SH7vL+dkMHlZ6SkZbPUtw4g/KW3oU5GTnxhDfoGyauu+REsJy58/ncetg2YkWoZhGB7M0MeQSP75ikTN7D+YT05uPrPXxW5y1PhFm7ktSmMd1LQLsSS/QNl3IHZPa4bhN2bofeL378/kvOfKn7a3ongTpoXGBADmFLsBZOzcz8LM3eXa9x8+TOf7ZVtp/7j/0bC5+cl/Y+jz+Xw69h1bJW9yRnJiht4nvluyhXU7Er8Qx9WvTSny+dx/TqTXSz9WaF/lDU8d9GPZs1237TvA82OXVkiLX2zanVNpt9dns5JnBrJhQIpH3aQqQZ0o1f/rRWXW3/mBb/nqKsw5z34LwJqBvRKsxDDihxn6JKB4FIsgURn7nARNGAuR1mcUN519TOHnTXHKWRQvVIN70zUML+a6CTjfLdkcNoolmnzuJ8XAx15ePpq+rvD9pkqmfDAMo2KYofeBX738Q+RGFWTzngMlykSsJxkEbCjWSBbM0PvAgg0VS4ZWvFeeuSubXz73HRt2ZVdKz+Nf+ZckNJZhhLPW7mRo+nqLXjGMGGOGvhL4bZ4+nbme9TuyGRrFSlFldegHT1vrm6Zh6WVrKc1Gb91b8kmkOA8Pm8fDw+YzOUkXGrcblAGwYVd24BdHisrQi8ggEdkiImG7iuLwkoisEJH5InKGp+42EVnuvm7zS3gqMOin1aT1GcUTw0vvgYcz6CKwO7vsHDo9XkzsIuqPfD4/YpvQzSAriScffTpzHQ98MifRMowEsTs7l24Dv/P1KToWRNujfx/oUUZ9T6Cd++oNvA4gIk2AvsDZOKtL9RWRxhUVm6p8ONXpgXv7hzuzDrInJ7wxF4TXJq0sc59LNu31RVvxPuvqbVmk9RlVpOzFCcu49+PZRcqiifgJTfoS4MOpa0jrM4qCGGb79IOpKwsXUEOBRz7/meFzMxMnyEgooU7KpKWl53cKAlEZelWdDOwoo8lVwIfqMA04XESOAi4DxqvqDlXdCYyn7BuGgdNjP33AeDoPmMCBvJKPhBUZiL3Fp9WzxizYVKLsxQnL+Xr+xnLvK+T6ePm7FTwxfCEAuQXxeQSes24nCzN3l9v9MnzuhsL35rkxkiUowi8ffSvA68zNcMtKKzfCUcxyHMwvoO+Ihb7s+ocA+8EXbfRnZa/y8NyYpfR66ceEzXL955gl/PnTuQk5tuE/Qc/KGpjBWBHpLSLpnqUIqxyhSyWaGPlUQMJ0hyYu2cq9H8+Oehbtgbx87h48i9XbsiK2XeRZKnLVNicNwrIoXVxDZqzjs2ID05X5cb8+aSVfztkQuaERaEK/1aif7goK4LunISu+HS+/DP0G4GjP59ZuWWnlJVDVt1S1s6p29klT0hFK+LVlb9kTiyp6Gzi139hyJzjzMmXFNv45ZkmFt4+Gu/83i6/nb2TC4s3MWF2Wt9Bh+qodjFm4qcwB7RCXv3RovkO4+Qll8egXP/PQsPlJ86huBJS1P8Lk52DEfXE9rF+GfgRwqxt9cw6wW1U3AmOBS0WksTsIe6lblhL47aOdtHQLUHQ2aTjyK3jgPTl5RRKc7cnJZfu+6Azelr053PhOST//cX8fXSEtEDly6IvZkd0qIcMb6ZTs9mmlrxVbDiVEK37M7fsOsCPrYOHnjbuz6frst6zbXnayu0e/mM+b3zuD69kH89m4u3LzKAx/2JOT638akXz3OsyNbwLEaMMrhwBTgRNFJENE7hCRu0XkbrfJaGAVsAJ4G/gTgKruAAYAM91Xf7fMCEO0kTKdB0zw5Xhdn/mWM58qe18hY/Z9OaMKpniiUypKNPez0KNzQRmN127P4rT+4yqtB2BNKUZ72KwMznxqAmcMGF9Y9sXsDWzcncN5z0/knR9WlbrPITPW8+w3zpPSTe9Mo+uz3/mi1agcp/YbV+QpMByFHY2o93rIQUtBPiwaHpdR/Wijbm5Q1aNUtaaqtlbVd1X1DVV9w61XVb1HVduq6imqmu7ZdpCqHu++3ovVF6lK7K1k3Pn6Hfu5a3B64VKL3y3ZHHGbWK19WxbhjHdBgfLVnA3kuRNUqnl69Ms376Vj37FkFptZXJpx9pO/fTavzPpo0zPPXrer8P3v3pvBs98kZt1jw2HV1tLHfsYu3MRHxScn/vgf2PRz5B2LwE//haG3wqKvYNk4eK4t5LrXboG/TxKBGYyt6sxbvytyI594cuRCxi48ZNx//346GTvDG8MdWQe5a3A6T4+Ov8EJF1L/xZwNPPjpXN4J5b6XUFvlf9PWsu9AHuMWFg0BnbWmcg+R3lmP3hm/5emIeX37eVHMoly+eS+Tlm7lze9XsXlPDr949tuoBpyN+HHX4Fm89N0KwL0WVnwLE/rBG+eWvpH3mtnjDldmbYPxj8P+bbBzDezeAP2bwOwPfdNqhj4gXPXqT3E71oTFW0qUrdiyj4HfLCkRV/7KxBVFbgrxJFyM+44sx9Bucw1uNdeCKocGWItvFfoxlkV+gfLW5JVhfbK3vzcz7DZ5FYz5XxvFAjX7PQvbj5yXSebuHAZP9S+1RTJz/5A5XPSvSYmWUQyFrFLcm6rw1gWOm8brugkx+m9F229f7vz9+TPf1JmhNwD400ezeeP7lSze6M+MWj8oy+8eIvRzUVXGeHryfxk6l5e/XR5x+3d+XM1Xczbw1ZwNPDN6Cf+ZsKxI/ay1O/lxRfhQuMfKMe29MiGzT40y942XEfMyWRXHp5t/j1vK/oOR3KWl/H8n/wv+dw1kzoHP7/Q0F9jqced5r/UY+Oxt4ZFKEO7fEfQp/KUR6kEGaeKH91TmFyjjFxV9spiychs3vu1EAhX/nXwx23ksvq97u4jHedAzcWlfzqEf9MSlW0rtzQN8E2aWsJdoQjG96SS2RYiA2pF1gGWb93LCEQ0j79jwjZe/W0FegfJIj5NKbdO9YAqsDBMl9t2Aop+9F+oaz0DvtpDRF1j6jfP2wF7o1wiufBnOuBUy0qFuY2ja9tB2ezc7YwLtLi7zO5ih95n/TbfHa78YMS+Tbsc3BWDn/lwGfrOEIw6rDTghpq9OPOSS8fb+K3OrUpywz4HfLCataf0y2x4Mk56iNKIx+t6cOeG+w1dzM/lqbibLnupJrRr2MO43I+dl0unowzm6Sb0SdQdyi/6vPyr2O/9nwb8hch4/wrpuijPjTefvRneHI+6DRq1h8NXO5367Yf0MaNwGBl3q+PX7lT0/xq4Wn8nYmdwx0EHL3/LI5z/zyOc/s8E9ryE//Hs/reGnFUUTjIXj3+PKvxj5axNXMGTGel/TPQPMXLODtyevKvUcD4iw5m6If49P7ALrqcp9Q+Zw5Ss/llr/7eLNpPUZxZY9OfzjywhuuymvwL6SY2F86/bwS7vz53nsh3rGi0JGHmDWB/DuJfD2hY6RjwLr0VeCcP+qtyaXHi9tVJxILiXverTeDJMvRzEQ60U4dNPw86YnwLVvTAXgwpNaRGw/tYx5CJm7bElGPxmzYCNz1zs94p2lhBErTlQXwIJis8ubs7PkBuP+AcvCLOW52Q29XF7KvI63LogseOT9zt/dkdetCGE9+kow+ufyZ2w0KkYko7vRY+gnLK54lNBH09cVRt5UNKomEsXHGsJRVqoJW/DEX+7+nxOIUBZl3Xhn1rknfMWBOCbr++z2MqvN0FeCvCQdeC2Ln0qJMEk08TzTofUBypsPJ8QL45eR1mdUkdQH3gRusc4XVFUYGmH1Mz/xa32HmLHwizKrzdAbRQhNxQ8aydSLfckN6/xuSRgfrQ+Ey/pZFXl42Py45gVaX97xt+IZKhN4DZuhN5KCJLLzhWzxzKL1c5H1ZLrpxZrcvMqdi9JmhIfDm9AuKvYUS9RbEP80IiHM0BtJgdm2omQfzOf1SSvJT0H3YTz500ezS5RFs7B9smFRN0ZS8Gkc/bFB5+v5GwuXbmzRsDbXnNk6wYqSg6wDeXw8fR13nNuGam42vHBzIR74ZA5r45AIL56YoTeMJCYnz+d86SlEfoHS9u+jefLKDlxzZms69nWWwji6SV16dDyq1O0ipdieHsWCOEEj2nz0PURkqYisEJE+Yer/IyJz3dcyEdnlqcv31I3wU7xhVHWquktLUdbv2E9an1GMKrZAfSjr6DOjF3PB8xMLyxdv3EvXZ79l8wu4TnMAAB+ASURBVJ6KzUd48/vkmysTsUcvItWBV4FLcBb3nikiI1S1cBqfqv7Z0/4+4HTPLrJVtZN/kg3DMA6x0F0LePjcDfQ6NXxPfdu+Qyt//deNiip+Y0hlounRdwFWqOoqVT0IfAJcVUb7G4AhfogzDKNsHvtqQWEUzsqt+6pcRM7a7fu552NnQLW0b36glJxEizfuIStiVsrUIBpD3wrwjoRluGUlEJFjgTaAdy20OiKSLiLTROTXpR1ERHq77dJLa2MYRkmyc/OZtXYH3f/9feFkr6rC48MXlIg8Gj53A3OjWMjns1kZrN+R3LmposXv8MrrgWGq3mw8HKuqnYEbgRdFpG24DVX1LVXt7LY1DCNKBClc8q7viIUJVhNfvNExoWlkD3wyl1+/+lNU6xlUFaIx9BuAoz2fW7tl4bieYm4bVd3g/l0FTKKo/94wjEoycl5mEaMWTW82FRm3aDNPeTKAnvzE2ASqCRbRGPqZQDsRaSMitXCMeYnoGRE5CWgMTPWUNRaR2u77ZkA3ILpcrIZhRMXDn88v4rIJtxxiVaFwLWGjCBGjblQ1T0TuBcYC1YFBqrpQRPoD6aoaMvrXA59o0dGg9sCbIlKAc1MZ6I3WMQzDH6Kdzfn4VwsY/fNGjmten8/u/kWMVfnPN5YxtkJENWFKVUcDo4uVPVHsc78w200BTqmEPsMwfCS0mMr2rIMRWgaTP4ZJWWBExmbGVpIR8zIjNzKMGLMlBvlZ9h3Io36t6oHIlnnfkDlc3D7ygi1GeCypWSW5f8icREswjCL4YZY37c6hY9+xvP1DMGaBjpyXyQOfzI3c0AiLGXrDqMIcyMsvTBXgZcMuJ2zxmwWb4i3JiAHmujGMKkDXZ7/l2Kb1SpSf+NgYjm5Slx8evqhIeSikIvFOG9iRpOMJQcIMvWGkGL99axoAsx67mKYNagPOmrredXUB+rmTq8LNDg2FzgXBP//kyKo1CSwWmOvGMFKUBZl7KChQhs4Mn8v//SlrIu6jomb+xQnLmL1uZwW3Lkq4nPFG+TBDbxgpysQlWzju76N5+PP55d62tOwBW/ceYMiMdRG3f3HCcv7vtSnlPm44Zq3154ZRlTFDbxgpSjQ99hDFJ1yF5j0W99zcNTidR7/4uVxrrUbDnpxc/jV2KXlhBoZjETpa1TBDbxgGQ4st1Viajz400erViSt58/uVEfc7cckW7oliktNfPp3LKxNX8NSoxdEJNsqFGXrDMFi5dR8/Lt8GwLiFmxi3cDMAM1bv4G+fzStsFzL7Q2as49lvlkTc7+3vz2RUGWkL9h3IY9CPq5mweAvgPIWE69UblcMMvWEYfDF7Aze/O538AqX34FkM+ulQcrBhszJI6zMKKNnDf/SL+dw1OJ2CgoqlBB4wchH9vy6a/uqvn80j60Aed36QzsbdVSNffKyx8ErDMAopaybsmAUbS0ThDJnhuHw69htLn54ncUOXY1i9LSvq4+3KLhkjP3xuJt2Ob8aExZuZtTb5FuIOImboDcMo5JMyImpWbs0qNd5y/8F8nhi+kP0H8xkYxqXz5ZwMWjeux1lpTQDHZTN91Xa+W7Il7P5CTwg79+eW8xsY4TBDbxhGIdll5LIfMmMdtWuU7e3dlxN+DdY/f+r4+dcM7AXAw8PmMfrn0tMr9Pni50hSjXIQlY9eRHqIyFIRWSEifcLU/05EtorIXPd1p6fuNhFZ7r5u81O8YRj+kptfuq89Y2e206svg2rVyp5iFRpoLcvIG/4TsUcvItWBV4FLcBYGnykiI8IsIPKpqt5bbNsmQF+gM07E1ix3W5sBYRgBpLJ5ZV76dnmZ9SPmZbJ5j8XFx5toXDddgBXumq+IyCfAVUS3JOBlwHhV3eFuOx7oQbF1ZQ3DqBr8Zei8yI0M34nGddMK8M6myHDLinONiMwXkWEiElpMPNptEZHeIpIuIulRaDIMwzCixK84+pFAmqqeCowHPijvDlT1LVXtrKqdfdJkGIZhEJ2h3wAc7fnc2i0rRFW3q2rI8fYOcGa02xqGYRixJRpDPxNoJyJtRKQWcD0wwttARI7yfLwSCCWsGAtcKiKNRaQxcKlbZhiGYcSJiIOxqponIvfiGOjqwCBVXSgi/YF0VR0B3C8iVwJ5wA7gd+62O0RkAM7NAqB/aGDWMAzDiA+ipSWeTiC1j2qnR932YqJlGIZhRGRNnRsTLQEAeXLPrNLGOC2pWQpxWB2b6GwYRknM0Cc5L99wOv+4vD0AXdo0TbAawzCCiHUBk5wrTmuJqnLHuW14aFj5l4wzDCP1sR59CiAiVKsmJZZ9Cwq3dj020RIMo0pjhj6F6NnxyIht+vQ8qcjnf117WtT7v797O/57fSeuOK1l1Nt8dOfZ9L+qIycd2TDqbQzD8Bcz9EnIZ3d3pUXD2iXKu7c/gjUDezHi3m4A3Nb1WGb8vTsXt29R2KaGJ7vgm7ecyW/ObF3qcV64ruhN4MHu7biqUysuPfmIsO2bNahF+mMX85/fHtqu2/HNAHjiipOj+GaGYcQCM/RJyFlpTZj6aHeWDOgRtv7U1oezZmAvnryqIy0Oq8M7t51VWHfmsY0BeP/2s7isQ8kngNXPXs7lpzjltTy5xzu2OqwwBa3XRfTRnWcXvv/xkYto1qA2V58e5uYRvChew6gy2GBsknHj2ccAUL2aUL1a9ai3m/vEJeTkFnBkozosfaoHtWsc2nbNwF58v2wrrQ6vg4gg7jJCqrCo/2XUqFatiNE//RjnZvHe786i2/HNeP43p/LM6MXUrH6oTc3qwm1d0wo/H16vVoW+r2EYlccmTCUZ3/71fNo2bxDTY6zZlsXDw+Yz6PazaFDbv77AP8cs4fVJK33bn2EEAZswZSQlac3qM/Turr4aeYBHepwUuZFhGL5jht4wDCPFMUOfZDSrXzLaJpm458K2iZZgGFUOM/RJxJIBPWhUr2aiZVSKhy47iQ9/3yXRMgyjShGVoReRHiKyVERWiEifMPV/EZFF7lKC34rIsZ66fBGZ675GFN/WiJ46NaOPsgky553QnIVPXsYlpcTjG4bhLxENvYhUB14FegInAzeISPHZL3OAzu5SgsOA5zx12arayX1d6ZNuI8mpX7sGb9+auFUjxzz4y4Qd2zDiTTQ9+i7AClVdpaoHgU+Aq7wNVHWiqu53P07DWTLQcFnU/zLWDOzFgF93TLQUw6VR3Yq7wEKTzgwjWYjG0LcC1ns+Z7hlpXEH8I3ncx0RSReRaSLy6wpoTHrq1XLCFI9pUi/BSoLH0qcOze6d3+9SXrvpjLgct6AS00eevrojZ7dpErHdDV0OLZfcJa0JXY8rO410m2b1S5T1veJkftHW0k8blcPXwVgRuRnoDDzvKT7WDeK/EXhRRMKGXYhIb/eGkO6npiBxrpv3JRxXdYo+UVgqUbtGde7v3o7h93TjsDo1ufyUoyJvVEHuPLdN4fum9Ss2U/fPF5/AiUc05L3bzyrMynlx+yOY/fglJdo+eWVHJvzlfL6+71yG3t2VIb3PYcY/ugMw5A/nUKdm0Z/f3911BUK8fMPp3N6tDR//4RzWDOzFhSc2B+AC968RDO45eH/FNmwev3klEWfGikhXoJ+qXuZ+fhRAVZ8t1u5i4GXgfFXdUsq+3ge+VtVhZR0zVWbGXtbhCM45rim3dztkYDo/NYFt+w4Uabfi6Z6MXrCJ+4fMKXN/awb2ionOoFFQoMzL2MXVr03xbZ8f3Xk23Y5vxqLMPbQ4rDbNGjhhqjm5+Zz0+BgAOrQ8jIWZe0rdx6j7z6VDy0aFn/PyC/hyzgauOaM11aoJ63fsZ/W2LF76djn9ruxAx1aNSt0XgKrS5tHRAMz4R3eaN6jN4GlrSWtan7PSmlC3VtHB95zcfDJ27uf4Fg1Zuz2L85+fxFu3nEnvwbMqdE6M8jHzHxdz1tMTipS9euMZjJyXyX9rvkztJV+Wb4fn/AnSB0FejvP57Lth+hvQuA3sXO2UnXIt/PyZ877XCzDjbajdADJmlthdWTNjozH0NYBlQHdgA85C3zeq6kJPm9NxBmF7qOpyT3ljYL+qHhCRZsBU4CpVXVTWMZPd0Pe/qgPXnNGa+mFmlubmF9DuH45n66UbTudgXgG/ObM1o+Zv5J6PZ5e536pi6EOk9RkVtnzArzvy+FcLot7PuD+fxwlHlJ4mecqKbdz4znT69DyJkfMyWZi5hy5pTZixpug69qufvRwJYNL/zF3ZXPvGVDbsyk60lJRmzcBeJa7JwXd04Zft3CesjHSo19TJ+rdvK7x7MbTt7hjwFRPg4n6waDh8/aBj3P+eCbXqQ34e5GZBrQZwcB/UaQQTn4WFX8C9JQ06c4fAV3c77zv8H7S9CEbcW6ahjzjHXVXzROReYCxQHRikqgtFpD+QrqojcFw1DYDP3B/COjfCpj3wpogU4LiJBkYy8snKcc3rc+VpLXnvpzXc6knmVZxQ4q9GdWtypSeve73aTu/tt52PZu76XSzdvLew7pmrT6FV47qxER5gnv/NqSVWzZr80IUc07Qee7JzeX7sUgBuOvsYPpq+rrDNRSe14LslzkNl3ytOLtPIA/zi+GYMu7srZxzTmN93a8OExZvp2fFIRIQflm/llndnMOzuroE08gAtD6/LT30uou/wBXwwdW2i5SQ1t5zjuOMGT3PO4/knNOf7ZVtJa+qMr13cvgUTFh9yWISeDAFo7bGxhx8L3fvC6bdAg+ZwwqVOeacboN2lkLPLMfIA1WtAdffpr47798JHnVc4TrkWVk+GLn+AVmdAQT5kzgZK7xxbUjOfOO3owxl+T7eo2q7cuo/G9WrRxOMnVlXen7KGazsfTX6+clr/cYV1Va0nX5ydWQf5cs4Gbu+WVsLYbtt3gGYNajNx6RaGpWfwh/OO46QjG/Ldki386aPZviSBy8nNT4o5DAszd9PrpR8BZy2CD6auZfAdXbjl3RkA9LviZPqNjL6fdcIRDVi2eR/v/e4sbn8/TM8yAu1aNGDYH3/Bgg27uemd6UXqXrnxdFo3rkfmrmz+9NFsPvh9F45rVp//TFhGvVrVefLKjrT9++gi2zx3zals2pPDC+OXFSnv0qYJObn5zM/YHVbHqzeeEfFpefg93Vi7Y39h5+vf45ZywYktOLpxXbo88y33XXQ8f730RPbm5PLlnA20a9GQWjWEM4+NPCgfL0Sk4q6bRJCMhv6kIxsy5sHzfNtf6BFx6F1d6RJFhIdhgHPdtD/qMEbffy4F6qSzXrBhNz8s38YfL2jLgK8XkbFzP2MXbgacdNIDrurI3pw8FmTuZvjcTACWP92T/ALlYH4Bh9WpyeRlW7l10IyIx/+k9zm0P+qwsOGroWu6R4cjeeOWMwvLc/MLiqS4DvHCuKW89N0Krj2zNX+59ASOalSX/AJlyaY9dGjZqHB/K5+5nAJV0tfs5LVJK/hh+bbCfTx9dUduOvtYvpidwV+GzmPcn8+jeYPanD5gPOCEPucVKIfVKT3cdvu+AzSuV6twPYagYoY+Dpx81GGMfsC/STj3D5nDiHmZfH3fuREH9QwjRG5+AdVEqF5Bo5RfoOTk5ocdX8o+mM/G3dlc9O/vOfPYxtx70fF0a9uMGtWEzXtzaFinZpkZT/fm5PL0qMU80uMkGkcR9bTvQB7Pjl7MP3q1LwxR9tLn8/m0bd6AP5x3XJHycQs30XvwLIbf043Tjj487L7HLNhEbn5BuZbFDDpm6ONAx1aH8fV9/hn6fQfy+G7JliJ+fMMwjNIoy9BbUjOfqObzQF2D2jXMyBuG4Qtm6H0iqBEZhmEYZuh9ombAB2oMw6i6mKH3iRrVzdAbhhFMzND7RI1qdioNwwgmZp18wu+FtA3DMPzCDL1P/KNX+8iNDMMwEoAZep842nLNG4YRUMzQG4ZhpDhm6A3DMFIcM/Q+YGuIGoYRZKIy9CLSQ0SWisgKEekTpr62iHzq1k8XkTRP3aNu+VIRucw/6f5wcfsWzPh7d/57facK7+P1m+OzzqlhGEZFiBgTKCLVgVeBS3AWBp8pIiOKLSByB7BTVY8XkeuBfwK/FZGTgeuBDkBLYIKInKCq+eUROfbB8/jdezPYuDuH535zKhec0JyFG/ewe38uU1dup26t6vS7sgOvT1rJ5j05bNydze9+0Yajm9SlZaO6jFnoLNP3n992YuLSLWzZc4A7ftmGxRv38KcLjgfgqk6tuKpTK3Zn53Lak+OKHP/jP5zNx9PX0bR+LfYfzOezWRmc1roR8zJ288PDF9KiYZ3yfB3DMIy44suasSIy1m0z1V16cBPQHOjjbettV9YxO3furOnpiVsjPL9Ayc7NLzM2Pje/gNz8grDpUw3DMOJNZbNXtgLWez5nuGVh26hqHrAbaBrltoGjejWJOAGqZvVqZuQNw0gKAjMYKyK9RSRdRNK3bt2aaDmGYRgpQzSGfgNwtOdza7csbBvXddMI2B7ltgCo6luq2llVOzdv3jw69YZhGEZEojH0M4F2ItJGRGrhDK6OKNZmBHCb+/43wHfqOP9HANe7UTltgHZA5IUnDcMwDN+I6GRW1TwRuRcYC1QHBqnqQhHpD6Sr6gjgXWCwiKwAduDcDHDbDQUWAXnAPeWNuDEMwzAqRyDXjE101I1hGEayYWvGGoZhVGEC2aMXka3A2iibNwO2xVBORQmiLtMUPUHUZZqipyrqOlZVw0ayBNLQlwcRSS/tcSWRBFGXaYqeIOoyTdFjuopirhvDMIwUxwy9YRhGipMKhv6tRAsohSDqMk3RE0Rdpil6TJeHpPfRG4ZhGGWTCj16wzAMowzM0BuGYaQ4ZugriIhIojUkC3auosfOVfTYuYoeM/QV53AozNYZGETkRhE5zX0flB9C4RJcAdIEgIgE7TfQAApXdgsEInKliLRNtI4wFJ6jIF1XAbymgm3oReTXIjIg0Tq8iEgjd6WsMVC40ErCEZGLReQH4EXgdABN8Ei7iFwqIlOAV0TkpiBogkLD9ZdE6wghDi1EZBLwDkAQkv+519RUnKSFRyVaTwgR6SUiE4AXROQ8SPx1FbRrqjiBM/TuRV9dRO4E/gX0EZFfJlqXh2xgF9BRRK6FxPW+3HNV180Q+hjwFDAMqJdIXe6xmwP9geeAj3DWEH7UrUvIdSciNUTkEeAl4F8i0klVCxLde3aNVI77OlVEekJizpN7TTUQkZE419RjwDTg2ERpKqYvDXgaeBlYDPR2bUWizlcgr6kSqGogX8AFQEPgD8CkROtxNVUHjgD+DPwK2OSpkwTqusrz/mZgaoLPkwAdgTc9ZSfjpLBulsjzBfwax5X0IDA9kefJo6mae34GAlcl+v/navqt5/29wNBEa3K1dAdecd/Xce3EPKCxWxb36yqI11TxV2B69CJyv4i8Hbo7A9+r6l5VfRuoLyJ3uO3iptmj6fciIuo8Tu8Beqnq18B8EXlCRDqqqsbLT+jR9QcAVR3ullcHVgMLReTosvYRA023icglrh4F9gG/EJEmbtkiYChOTyyeuu4XkYEicp1bNEpVc1T1RaCFiNzotquZAE3XAKhqAZAJnAD8BGwUkbtFpF0CNF3ravrULa8G7ATWi0jteOnx6PqNiJztKcoArhGR2u7/cRIwBXgijpoCd01FJNF3GveO+Ducx8MewPfAo0BbT31PYCHuXTtBmv4OtAVaAE+5bX6Ps6BKuvu5ZoJ0HeepPwVnVbCGcTpPjXHcRRuB+UB1T92HwOBibacDbeKgS3CevH7CWfVssXvuWnjaXA1siOM1VZqmJkBnoK/b7m9AFjDS/VwjAZqae9r8AlgSr/PkHrOFe31nAl8B1YpdVy969J/mXoNHVLVrKtpXUHr03YF/quoY4K84j0E3hSpV9RsO+eMahnodcdZUG7gWx0ffU0TGAfcD33EopXI8BmaL66qF464BQFV/xvH1Xh8HLajqTmAc0B6YRdGe1b1ADxE5y/2chfOYfTAOuhS4EHhMVYfh/EBPBS7ztPkSWCYifwNn8DEBmjoBlwCbgF+KyGjgdhxjssrdNGYDs6VoOg2nIxFqMwXIEJErY6UjjK4twHBXx0bgLk/1k8CvRKSDqz8H2IvzFBlLTYG7pqIl0QMroePPwfF5o6rpwFSglYh08zR/BHgWWA4cmSBNxwHnAuOBGaraSVUvBS4QkTbuhRBvXdNwztW5bjvBWfaxTqxdSZ79f6iqu4DXgP8TkWNdfXtwfpSPi8htOAN7HYjxD9JzrtKBX7paxuBcOx1E5ERP8z8Cz4nIJqBVAjQtxTGsp+O4JWaqagecG/UFItIqVtdVGZqW4Zynk9x2hwFLgNxY6ChD18s4y5COA3qJyFGuxpU4kUCvudf9zThPAAVx0BSYa6o8xNXQFzc86vgmwem9VAuFSgELcO7iLd3tjscxIl8BZ6iqb37ecmhaiPNDbAg8oaqPeTY7RlVX+6WpnLoW4Dzehn4EinPRZ/ltIMJoUvdvjvt3JvANTlREqM0rOCGfZ+JEblyrqrt91lUkwsFzrlYADUXkFPfz90AjnP8hItIJeBv4HOe6+iABmia7erYAd6tqX7f9DqCbqm5IgKbQeWrgttsDtMYJRPCd0nSpaq464ctTcG40D3jaPItj7O8ATgTuUNVsHzU18moLwjVVGeJi6EWki4i8DTwiTthdqDz0D16OY0h/KyLVVTUD56JKc+t3A/eq6v+pamaCNK3HufEcq6oHxQkBrQagqll+aKqgrgycJ5w0z27+pqqD4qCpWpjB8VeA40Wkg4gcISLHq+p3wJ9V9Ta//n/u8TuLyGDgCfFM6JFDk9hm4LjTLhWRGuoMCLfC8YcDbAf+pKrX+nhdlVfTQpwb4OmqmuNeVwKgqr48+fhwngCuV9X3/dAThS4p1qnYBowAThCR1uLMOWisqh8Cd6nqdaq6yQc91UTkMBH5GidcEnXnM3h+f3G/pvwgpobevWifxUnN+RNwBtBXRI6AIpNC9gI/4PjB/+WOVjfGOWmo6lZVXR4ATYd7NOV77vKJ1lV4rty2vvjAo9BUoE7McF0RCfX+1gFfAj/j9HYOK6bfD13VROQV4E3gW5ynmX6ujmpuLxBVXYHzqN0W6ONufgB3TEVV17tjGkHQtMatz/frScwvTW6bHD80RalLVVVFpLY40TX5qjoZp4OzAOe6aubq8m28x/0978UZ92olIr919dYIXb/xvKZ8RWM7Sl0T+BNwgvu5Fc5JSvO0eRL4DDgJ5x/+Po7f+U08ERyprCmouqLU1Bf4AjjV/XwDzkX/HDGMQgKuAQ5337fDicSo5akfgPNon+aerxE4g8Vv4ongME3x1xSlrieBwaFrDbgbx731zxhfV+1xJvhd4Z6Lhp66hJwrX75XDE7UOR7DUN3zz6zt/v0K6Oy+PxX4mKKhlNXwOTQwiJqCqssHTecQg/BJr65i5RfjzFQejzOT+mTgPFfX8Z52DULfxTTFV5NPui72fo7BtR5am6Mm8B5O0MB/gftwXGvnxutcxeLl50k7HBiF8+jzGNAgTJuGOOF1LcPU+X5HDKKmoOryQVOsnnSK66rvlod+mJ2By933/YFncAbH43muTFPsdMXiSTWsJreuK/Bf931vYCsw0vt7iNW5iuXLTx99fZywvvvc9+Hy03QBFqpqpjj5NNqBM/iiPvq7A64pqLoqqylWsd7FdRVJYqWq6ao62m07Gsdw7HB1VYvTuTJNsdMVi+sqrCaXdThRNZ8CDwOzgRXqDozH+FzFjEoZehG5VUTOF5HD1AkDewtnmnsOcLaIhMIjQ6P7jXGmUt+OM3uzE/ibeS6ImoKqK4iayqMrDGfihJqGBs78HCw3TUmsqxyaGgPNcSawnY4zNnCiiLT3W1NcqcBjj+AMBE7EGTF/C2fwopmnTTcc/9bNxbYdjDOp4T3cwTs/XkHUFFRdQdRUGV04UT2X4Nx4RhLGD2yaYqspqLrKqekWT5m3vgHQxM9zlYhXuXr04sRtK46vdoOqdseZBbYDz+rmqvoTTmjWSW5cagO3ahRwnarerqrzy3PsZNIUVF1B1FQJXY1EpI46k3kUJ//QFaq6zDTFT1NQdVVA04mupvqqus0NLa6mqvvUmbyW3ER5Z6yOM0jyT+B8nNCjDzz11XAedc4vdid8EedOvRk4ys87VBA1BVVXEDX5qKvEwLBpir2moOqqpKYZsTpXiX5F7NGLyPk4saKNcab/DsDJeXGhiHSBQr9VP/cVohdODPZc4BRV3RjpWNESRE1B1RVETT7r8nOmrWlKYl0+aJrnt6bAEMUd8pcU9V+9hvMI9DtglucueSTO4EaaW3YVcF4s7k5B1BRUXUHUFFRdpim5dQVRU1Be0Zy8ejjT7au7n28CnnXfzwXuc993BobERXQANQVVVxA1BVWXaUpuXUHUFJRXRNeNqu5X1QN6KJ71EpxJBODkzW4vThKgITgxpyWyHPpNEDUFVVcQNQVVl2lKbl1B1BQUakRu4iBO9jbFySo5wi3ei7PCUUdgtbopVdW9bcaaIGoKqq4gagqqLtOU3LqCqCnRlCe8sgAnD8Q2nJXqvwYeBwpU9Uf1MW92kmsKqq4gagqqLtOU3LqCqCmxlMfPg5MEqAD4ESfRf8J9T0HUFFRdQdQUVF2mKbl1BVFTIl+hxEJRISKtgVuAF1T1QHluKLEiiJogmLqCqAmCqcs0RU8QdQVRUyIpl6E3DMMwko+ELg5uGIZhxB4z9IZhGCmOGXrDMIwUxwy9YRhGimOG3jAMI8UxQ28YhpHimKE3DMNIcf4fTopKzmxkRmIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show split\n",
    "df_train['energy(kWh/hh)'].plot(label='train')\n",
    "df_test['energy(kWh/hh)'].plot(label='test')\n",
    "plt.title('energy(kWh/hh)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T00:24:15.905017Z",
     "start_time": "2020-02-16T00:24:15.747859Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train helpers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T02:37:01.954099Z",
     "start_time": "2020-02-16T02:37:01.888477Z"
    }
   },
   "outputs": [],
   "source": [
    "def main(trial, train=True):\n",
    "    # PyTorch Lightning will try to restore model parameters from previous trials if checkpoint\n",
    "    # filenames match. Therefore, the filenames for each trial must be made unique.\n",
    "    \n",
    "    checkpoint_callback = pl.callbacks.ModelCheckpoint(\n",
    "        os.path.join(MODEL_DIR, name, 'version_{}'.format(trial.number), \"chk\"), monitor='val_loss', mode='min')\n",
    "\n",
    "    # The default logger in PyTorch Lightning writes to event files to be consumed by\n",
    "    # TensorBoard. We create a simple logger instead that holds the log in memory so that the\n",
    "    # final accuracy can be obtained after optimization. When using the default logger, the\n",
    "    # final accuracy could be stored in an attribute of the `Trainer` instead.\n",
    "    logger = DictLogger(MODEL_DIR, name=name, version=trial.number)\n",
    "#     print(\"log_dir\", logger.experiment.log_dir)\n",
    "\n",
    "    trainer = pl.Trainer(\n",
    "        logger=logger,\n",
    "        val_percent_check=PERCENT_TEST_EXAMPLES,\n",
    "        checkpoint_callback=checkpoint_callback,\n",
    "        max_epochs=trial.params['max_nb_epochs'],\n",
    "        gpus=-1 if torch.cuda.is_available() else None,\n",
    "        early_stop_callback=PyTorchLightningPruningCallback(trial, monitor='val_loss')\n",
    "    )\n",
    "    model = LSTM_PL(trial.params)\n",
    "    if train:\n",
    "        trainer.fit(model)\n",
    "    return model, trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T02:37:02.018283Z",
     "start_time": "2020-02-16T02:37:01.956427Z"
    }
   },
   "outputs": [],
   "source": [
    "def add_suggest(trial):\n",
    "    trial.suggest_loguniform(\"learning_rate\", 1e-5, 1e-2)\n",
    "    trial.suggest_uniform(\"lstm_dropout\", 0, 0.75)\n",
    "    trial.suggest_categorical(\"hidden_size\", [1, 2, 4, 8, 16, 32, 64, 128])    \n",
    "    trial.suggest_categorical(\"lstm_layers\", [1, 2, 4, 8])    \n",
    "    trial.suggest_categorical(\"bidirectional\", [False, True])    \n",
    "    \n",
    "    # constants\n",
    "    trial.suggest_int(\"window_length\", 24 * 4, 24 * 4)\n",
    "    trial.suggest_int(\"target_length\", 24*4, 24*4)\n",
    "    trial.suggest_int(\"max_nb_epochs\", 20, 20)\n",
    "    trial.suggest_int(\"num_workers\", 4, 4)\n",
    "    trial.suggest_int(\"grad_clip\", 40, 40)\n",
    "    trial.suggest_int(\"vis_i\", 670, 670)\n",
    "    trial.suggest_int(\"input_size\", 17, 17)\n",
    "    trial.suggest_int(\"batch_size\", 16, 16)    \n",
    "    return trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T02:37:02.081996Z",
     "start_time": "2020-02-16T02:37:02.020607Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "def objective(trial):\n",
    "    # see https://github.com/optuna/optuna/blob/cf6f02d/examples/pytorch_lightning_simple.py\n",
    "    trial = add_suggest(trial)\n",
    "\n",
    "    \n",
    "    print('trial', trial.number, 'params', trial.params)\n",
    "    \n",
    "    model, trainer = main(trial)\n",
    "    \n",
    "    # also report to tensorboard & print\n",
    "    print('logger.metrics', model.logger.metrics[-1:])\n",
    "    model.logger.experiment.add_hparams(trial.params, logger.metrics[-1])\n",
    "    model.logger.save()\n",
    "    \n",
    "    return model.logger.metrics[-1]['val_loss']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T02:37:02.139269Z",
     "start_time": "2020-02-16T02:37:02.084857Z"
    }
   },
   "outputs": [],
   "source": [
    "PERCENT_TEST_EXAMPLES = 0.3\n",
    "EPOCHS = 2\n",
    "DIR = Path(os.getcwd())\n",
    "MODEL_DIR = DIR/ 'optuna_result'/ 'lstm'\n",
    "name = \"lstm1\"\n",
    "MODEL_DIR.mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T02:37:02.198129Z",
     "start_time": "2020-02-16T02:37:02.141432Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now run `tensorboard --logdir /media/wassname/Storage5/projects2/3ST/attentive-neural-processes/optuna_result/lstm\n"
     ]
    }
   ],
   "source": [
    "print(f\"now run `tensorboard --logdir {MODEL_DIR}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[I 2020-01-29 08:42:54,398] Finished trial#8 resulted in value: 0.005635036621242762. Current best value is 0.005635036621242762 with parameters: {'batch_size': 16, 'bidirectional': False, 'grad_clip': 40, 'hidden_size': 128, 'input_size': 17, 'learning_rate': 0.00019134834148401144, 'lstm_dropout': 0.4080689425353674, 'lstm_layers': 8, 'max_nb_epochs': 20, 'num_workers': 4, 'target_length': 24, 'vis_i': 670, 'window_length': 48}."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-01T12:52:22.492191Z",
     "start_time": "2020-02-01T12:52:22.416653Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T01:47:59.598921Z",
     "start_time": "2020-02-16T01:47:02.100Z"
    }
   },
   "source": [
    "Note that the LSTM has access to the y values for the first half of the plot (the context) to match the NP setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T04:08:58.961223Z",
     "start_time": "2020-02-16T04:08:58.864759Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/callbacks/pt_callbacks.py:239: UserWarning:\n",
      "\n",
      "Checkpoint directory /media/wassname/Storage5/projects2/3ST/attentive-neural-processes/optuna_result/lstm/lstm1/version_109/chk exists and is not empty with save_top_k != 0.All files in this directory will be deleted when a checkpoint is saved!\n",
      "\n",
      "INFO:root:gpu available: True, used: True\n",
      "INFO:root:VISIBLE GPUS: 0\n"
     ]
    }
   ],
   "source": [
    "trial = optuna.trial.FixedTrial(\n",
    "    params={\n",
    "        'number': 45,\n",
    "        'batch_size': 16,\n",
    "        'bidirectional': False,\n",
    "        'grad_clip': 40,\n",
    "        'hidden_size': 128,\n",
    "        'input_size': 17,\n",
    "        'learning_rate': 2e-5,\n",
    "        'lstm_dropout': 0.4,\n",
    "        'lstm_layers': 8,\n",
    "        'max_nb_epochs': 20,\n",
    "        'num_workers': 4,\n",
    "        'target_length': 24*4,\n",
    "        'vis_i': '670',\n",
    "        'window_length': 24*4\n",
    "    })\n",
    "trial = add_suggest(trial)\n",
    "trial.number = 109\n",
    "model, trainer = main(trial, train=False)\n",
    "trainer.fit(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T02:16:06.159986Z",
     "start_time": "2020-02-16T02:16:06.045538Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T04:10:17.489144Z",
     "start_time": "2020-02-16T04:09:03.917103Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:\n",
      "            Name     Type Params\n",
      "0         _model  LSTMNet    1 M\n",
      "1   _model.lstm1     LSTM  999 K\n",
      "2  _model.linear   Linear  129  \n",
      "INFO:root:model and trainer restored from checkpoint: /media/wassname/Storage5/projects2/3ST/attentive-neural-processes/optuna_result/lstm/lstm1/version_109/chk/_ckpt_epoch_3.ckpt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b51a0a6c1a704a6c89d712401a461b7b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Testing', layout=Layout(flex='2'), max=234.0, style=Progr…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "304562568c6e4a14bd47be59577e05b5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 10973, {'val_loss': '0.015320336446166039'}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# test\n",
    "trainer.test(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T04:10:18.042094Z",
     "start_time": "2020-02-16T04:10:17.491821Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ba1a3bfe5284a898e91b6ca883f4dc5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "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": [
    "loader = model.val_dataloader()[0]\n",
    "dset_test = loader.dataset\n",
    "label_names = dset_test.label_names\n",
    "plot_from_loader(loader, model, vis_i=670)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-27T09:03:28.792465Z",
     "start_time": "2020-01-27T09:03:28.346446Z"
    }
   },
   "source": [
    "# Hyperparam opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T04:08:45.711767Z",
     "start_time": "2020-02-16T02:36:49.800Z"
    }
   },
   "outputs": [],
   "source": [
    "import argparse \n",
    "\n",
    "parser = argparse.ArgumentParser(description='PyTorch Lightning example.')\n",
    "parser.add_argument('--pruning', '-p', action='store_true',\n",
    "                    help='Activate the pruning feature. `MedianPruner` stops unpromising '\n",
    "                         'trials at the early stages of training.')\n",
    "args = parser.parse_args(['-p'])\n",
    "\n",
    "pruner = optuna.pruners.MedianPruner() if args.pruning else optuna.pruners.NopPruner()\n",
    "\n",
    "study = optuna.create_study(direction='minimize', pruner=pruner, storage=f'sqlite:///optuna_result/{name}.db', study_name='no-name-b60e37fc-4ab6-4793-8a0a-c87a1b40c5c0', load_if_exists=True)\n",
    "\n",
    "# shutil.rmtree(MODEL_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-27T23:38:16.813496Z",
     "start_time": "2020-01-27T23:38:16.766674Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T04:08:45.713036Z",
     "start_time": "2020-02-16T02:36:49.800Z"
    }
   },
   "outputs": [],
   "source": [
    "study.optimize(objective, n_trials=200, timeout=6000)\n",
    "\n",
    "print('Number of finished trials: {}'.format(len(study.trials)))\n",
    "\n",
    "print('Best trial:')\n",
    "trial = study.best_trial\n",
    "\n",
    "print('  Value: {}'.format(trial.value))\n",
    "\n",
    "print('  Params: ')\n",
    "for key, value in trial.params.items():\n",
    "    print('    {}: {}'.format(key, value))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T04:08:45.714257Z",
     "start_time": "2020-02-16T02:36:49.800Z"
    }
   },
   "outputs": [],
   "source": [
    "study.optimize(objective, n_trials=200, timeout=6000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T04:08:45.715466Z",
     "start_time": "2020-02-16T02:36:49.800Z"
    }
   },
   "outputs": [],
   "source": [
    "df = study.trials_dataframe(attrs=('number', 'value', 'params', 'state'))\n",
    "df.sort_values('value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T04:08:45.716663Z",
     "start_time": "2020-02-16T02:36:49.800Z"
    }
   },
   "outputs": [],
   "source": [
    "model, trainer = main(trial, train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T04:08:45.717955Z",
     "start_time": "2020-02-16T02:36:49.800Z"
    }
   },
   "outputs": [],
   "source": [
    "loader = model.val_dataloader()[0]\n",
    "dset_test = loader.dataset\n",
    "label_names = dset_test.label_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T04:08:45.719361Z",
     "start_time": "2020-02-16T02:36:49.800Z"
    }
   },
   "outputs": [],
   "source": [
    "x_rows, y_rows = dset_test.iloc(10)\n",
    "x_rows.loc[x_rows.index[model.hparams.window_length:], dset_test.label_names] = 0.\n",
    "x_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T04:08:45.720669Z",
     "start_time": "2020-02-16T02:36:49.800Z"
    }
   },
   "outputs": [],
   "source": [
    "x_rows.loc[x_rows.index[model.hparams.window_length:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T04:08:45.721941Z",
     "start_time": "2020-02-16T02:36:49.800Z"
    }
   },
   "outputs": [],
   "source": [
    "self=dset_test\n",
    "idx=10\n",
    "k = idx + self.hparams.window_length + self.hparams.target_length\n",
    "j = k - self.hparams.target_length\n",
    "i = j - self.hparams.window_length\n",
    "idx, k, j, i, self.hparams.window_length + self.hparams.target_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T04:08:45.723150Z",
     "start_time": "2020-02-16T02:36:49.800Z"
    }
   },
   "outputs": [],
   "source": [
    "j-i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-16T04:08:45.724301Z",
     "start_time": "2020-02-16T02:36:49.800Z"
    }
   },
   "outputs": [],
   "source": [
    "self.hparams.window_length + self.hparams.target_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "file_extension": ".py",
  "kernelspec": {
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   "language": "python",
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
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  "mimetype": "text/x-python",
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
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