01_Intro_to_Time_Series_Classification.ipynb

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   "metadata": {},
   "source": [
    "created by Ignacio Oguiza - email: oguiza@gmail.com"
   ]
  },
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    "%%javascript\n",
    "utils.load_extension('collapsible_headings/main')\n",
    "utils.load_extension('hide_input/main')\n",
    "utils.load_extension('autosavetime/main')\n",
    "utils.load_extension('execute_time/ExecuteTime')\n",
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   "source": [
    "## 00. Purpose 😇"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "The purpose of this notebook is to show you how you can create a simple, state-of-the-art time series classification model using the great **fastai-v1** library in 4 steps:\n",
    "1. Import libraries\n",
    "2. Prepare data\n",
    "3. Build learner\n",
    "4. Train model\n",
    "\n",
    "In general, there are 3 main ways to classify time series, based on the input to the neural network:\n",
    "\n",
    "- raw data\n",
    "\n",
    "- image data (encoded from raw data)\n",
    "\n",
    "- feature data (extracted from raw data)\n",
    "\n",
    "In this notebook, we will use the first approach. We will cover other approaches in future notebooks.\n",
    "\n",
    "Throughout the notebook you will see this ✳️. It means there's some value you need to select."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 01. Import libraries 📚"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "There are some dependencies you need to have installed to be able to run this repo. If you don't have these packages you will need to install them: \n",
    "- pip install Cython\n",
    "- pip install pyunpack\n",
    "- pip install PyWavelets\n",
    "- pip install pyts\n",
    "- pip install fire\n",
    "- pip install nvidia-ml-py3"
   ]
  },
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   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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      "text/html": [
       "<style>.container { width:100% !important; }</style>"
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     "text": [
      "/home/oguizadl/timeseriesAI\n",
      "fastai : 1.0.58\n",
      "torch  : 1.3.0\n",
      "device : cuda\n"
     ]
    }
   ],
   "source": [
    "import fastai, os\n",
    "from fastai_timeseries import *\n",
    "from torchtimeseries.models import *\n",
    "path = Path(os.getcwd())\n",
    "print(path)\n",
    "print('fastai :', fastai.__version__)\n",
    "print('torch  :', torch.__version__)\n",
    "print('device :', device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 02. Prepare data 🔢"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download data ⬇️"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we'll use one of the most widely used time series classification databases: UEA & UCR Time Series Classification Repository. As of Sep 2019 it contains 128 univariate datasets and 30 multivariate datasets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "text": [
      "['ACSF1',\n",
      " 'Adiac',\n",
      " 'AllGestureWiimoteX',\n",
      " 'AllGestureWiimoteY',\n",
      " 'AllGestureWiimoteZ',\n",
      " 'ArrowHead',\n",
      " 'AsphaltObstacles',\n",
      " 'BME',\n",
      " 'Beef',\n",
      " 'BeetleFly',\n",
      " 'BirdChicken',\n",
      " 'CBF',\n",
      " 'Car',\n",
      " 'Chinatown',\n",
      " 'ChlorineConcentration',\n",
      " 'CinCECGTorso',\n",
      " 'Coffee',\n",
      " 'Computers',\n",
      " 'CricketX',\n",
      " 'CricketY',\n",
      " 'CricketZ',\n",
      " 'Crop',\n",
      " 'DiatomSizeReduction',\n",
      " 'DistalPhalanxOutlineAgeGroup',\n",
      " 'DistalPhalanxOutlineCorrect',\n",
      " 'DistalPhalanxTW',\n",
      " 'DodgerLoopDay',\n",
      " 'DodgerLoopGame',\n",
      " 'DodgerLoopWeekend',\n",
      " 'ECG200',\n",
      " 'ECG5000',\n",
      " 'ECGFiveDays',\n",
      " 'EOGHorizontalSignal',\n",
      " 'EOGVerticalSignal',\n",
      " 'Earthquakes',\n",
      " 'ElectricDevices',\n",
      " 'EthanolLevel',\n",
      " 'FaceAll',\n",
      " 'FaceFour',\n",
      " 'FacesUCR',\n",
      " 'FiftyWords',\n",
      " 'Fish',\n",
      " 'FordA',\n",
      " 'FordB',\n",
      " 'FreezerRegularTrain',\n",
      " 'FreezerSmallTrain',\n",
      " 'Fungi',\n",
      " 'GestureMidAirD1',\n",
      " 'GestureMidAirD2',\n",
      " 'GestureMidAirD3',\n",
      " 'GesturePebbleZ1',\n",
      " 'GesturePebbleZ2',\n",
      " 'GunPoint',\n",
      " 'GunPointAgeSpan',\n",
      " 'GunPointMaleVersusFemale',\n",
      " 'GunPointOldVersusYoung',\n",
      " 'Ham',\n",
      " 'HandOutlines',\n",
      " 'Haptics',\n",
      " 'Herring',\n",
      " 'HouseTwenty',\n",
      " 'InlineSkate',\n",
      " 'InsectEPGRegularTrain',\n",
      " 'InsectEPGSmallTrain',\n",
      " 'InsectWingbeatSound',\n",
      " 'ItalyPowerDemand',\n",
      " 'LargeKitchenAppliances',\n",
      " 'Lightning2',\n",
      " 'Lightning7',\n",
      " 'Mallat',\n",
      " 'Meat',\n",
      " 'MedicalImages',\n",
      " 'MelbournePedestrian',\n",
      " 'MiddlePhalanxOutlineAgeGroup',\n",
      " 'MiddlePhalanxOutlineCorrect',\n",
      " 'MiddlePhalanxTW',\n",
      " 'MixedShapesRegularTrain',\n",
      " 'MixedShapesSmallTrain',\n",
      " 'MoteStrain',\n",
      " 'NonInvasiveFetalECGThorax1',\n",
      " 'NonInvasiveFetalECGThorax2',\n",
      " 'OSULeaf',\n",
      " 'OliveOil',\n",
      " 'PLAID',\n",
      " 'PhalangesOutlinesCorrect',\n",
      " 'Phoneme',\n",
      " 'PickupGestureWiimoteZ',\n",
      " 'PigAirwayPressure',\n",
      " 'PigArtPressure',\n",
      " 'PigCVP',\n",
      " 'Plane',\n",
      " 'PowerCons',\n",
      " 'ProximalPhalanxOutlineAgeGroup',\n",
      " 'ProximalPhalanxOutlineCorrect',\n",
      " 'ProximalPhalanxTW',\n",
      " 'RefrigerationDevices',\n",
      " 'Rock',\n",
      " 'ScreenType',\n",
      " 'SemgHandGenderCh2',\n",
      " 'SemgHandMovementCh2',\n",
      " 'SemgHandSubjectCh2',\n",
      " 'ShakeGestureWiimoteZ',\n",
      " 'ShapeletSim',\n",
      " 'ShapesAll',\n",
      " 'SmallKitchenAppliances',\n",
      " 'SmoothSubspace',\n",
      " 'SonyAIBORobotSurface1',\n",
      " 'SonyAIBORobotSurface2',\n",
      " 'StarLightCurves',\n",
      " 'Strawberry',\n",
      " 'SwedishLeaf',\n",
      " 'Symbols',\n",
      " 'SyntheticControl',\n",
      " 'ToeSegmentation1',\n",
      " 'ToeSegmentation2',\n",
      " 'Trace',\n",
      " 'TwoLeadECG',\n",
      " 'TwoPatterns',\n",
      " 'UMD',\n",
      " 'UWaveGestureLibraryAll',\n",
      " 'UWaveGestureLibraryX',\n",
      " 'UWaveGestureLibraryY',\n",
      " 'UWaveGestureLibraryZ',\n",
      " 'Wafer',\n",
      " 'Wine',\n",
      " 'WordSynonyms',\n",
      " 'Worms',\n",
      " 'WormsTwoClass',\n",
      " 'Yoga']\n"
     ]
    }
   ],
   "source": [
    "pprint.pprint(get_UCR_univariate_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-04T13:05:56.695040Z",
     "start_time": "2019-11-04T13:05:56.670255Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#pprint.pprint(get_UCR_multivariate_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the case of UCR data it's very easy to get data loaded. Let's select a dataset. You can modify this and select any one from the previous lists (univariate of multivariate)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-04T13:05:59.101730Z",
     "start_time": "2019-11-04T13:05:59.076305Z"
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   },
   "outputs": [],
   "source": [
    "# dataset id\n",
    "dsid = 'ChlorineConcentration'   # ✳️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-04T13:06:01.630739Z",
     "start_time": "2019-11-04T13:06:00.749850Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: ChlorineConcentration\n",
      "Download and decompressing data...\n",
      "...data downloaded and decompressed\n",
      "Successfully extracted dataset\n",
      "X_train: (467, 1, 166)\n",
      "y_train: (467,)\n",
      "X_valid: (3840, 1, 166)\n",
      "y_valid: (3840,) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train, y_train, X_valid, y_valid = get_UCR_data(dsid, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "☣️ **Something very important when you prepare your own data is that data needs to be in a 3-d array with the following format:**\n",
    "\n",
    "1. Samples\n",
    "2. Features\n",
    "3. Sequence length (aka time steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All UEA & UCR Time Series Classification data have already been split between train and valid. When you use your own data, you'll have to split it yourself. We'll see examples of this in future notebooks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Prepare databunch 💿"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "You always need to define the bs at the time of creating the databunch, the object that contains all data required.\n",
    "\n",
    "It's also best practice to scale the data using the train stats. There are several options available: \n",
    "\n",
    "1. standardization or normalization.\n",
    "\n",
    "2. calculate them based on all samples, per channel or per sample. \n",
    "\n",
    "3. scale range (for normalization only).\n",
    "\n",
    "The most common practice is to standardize data per channel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-04T13:07:43.384730Z",
     "start_time": "2019-11-04T13:07:43.356559Z"
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    "hidden": true
   },
   "outputs": [],
   "source": [
    "bs = 64                            # ✳️\n",
    "seed = 1234                        # ✳️\n",
    "scale_type = 'standardize'         # ✳️ 'standardize', 'normalize'\n",
    "scale_by_channel = True            # ✳️ \n",
    "scale_by_sample  = False           # ✳️ \n",
    "scale_range = (-1, 1)              # ✳️ for normalization only: usually left to (-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-21T20:44:40.751428Z",
     "start_time": "2019-09-21T20:44:40.716504Z"
    },
    "hidden": true
   },
   "source": [
    "Now, the last step in data preparation is to prepare a databunch.\n",
    "Time series data may come as numpy arrays, pandas dataframes, etc.\n",
    "The 2 most common ways to load data into a databunch will be from a numpy array/ torch tensors or a pandas dataframe. Let's see how we'd work in either case. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### From 3D numpy arrays/ torch tensors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T11:18:35.341802Z",
     "start_time": "2019-09-30T11:18:35.304859Z"
    },
    "hidden": true
   },
   "source": [
    "1) You need to first create ItemLists from TimeSeriesList (custom type of ItemList built for Time Series)\n",
    "\n",
    "2) You need to label the ItemLists. You'll find a lot of information [here](https://docs.fast.ai/data_block.html)\n",
    "\n",
    "3) You enter the train bs and val_bs and crate the databunch object. \n",
    "\n",
    "4) You add features and seq_len."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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     "end_time": "2019-11-04T13:08:02.913725Z",
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    "hidden": true,
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TSDataBunch;\n",
       "\n",
       "Train: LabelList (467 items)\n",
       "x: TimeSeriesList\n",
       "TimeSeries(ch=1, seq_len=166),TimeSeries(ch=1, seq_len=166),TimeSeries(ch=1, seq_len=166),TimeSeries(ch=1, seq_len=166),TimeSeries(ch=1, seq_len=166)\n",
       "y: CategoryList\n",
       "0,1,0,1,2\n",
       "Path: .;\n",
       "\n",
       "Valid: LabelList (3840 items)\n",
       "x: TimeSeriesList\n",
       "TimeSeries(ch=1, seq_len=166),TimeSeries(ch=1, seq_len=166),TimeSeries(ch=1, seq_len=166),TimeSeries(ch=1, seq_len=166),TimeSeries(ch=1, seq_len=166)\n",
       "y: CategoryList\n",
       "2,2,0,1,2\n",
       "Path: .;\n",
       "\n",
       "Test: None"
      ]
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     "execution_count": 11,
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     "output_type": "execute_result"
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   ],
   "source": [
    "db = (ItemLists('.', TimeSeriesList(X_train), TimeSeriesList(X_valid))\n",
    "      .label_from_lists(y_train, y_valid)\n",
    "      .databunch(bs=min(bs, len(X_train)), val_bs=min(len(X_valid), bs * 2), num_workers=cpus, device=device)\n",
    "      .scale(scale_type=scale_type, scale_by_channel=scale_by_channel, \n",
    "             scale_by_sample=scale_by_sample,scale_range=scale_range)\n",
    "     )\n",
    "db"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### From pandas dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Let's now extract data from a pandas dataframe. Since we don't have the UCR data available as a dataframe, we'll first need to create and save it. You won't need to do this when you have a time series dataframe."
   ]
  },
  {
   "cell_type": "code",
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    "ExecuteTime": {
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       "      <td>-0.586417</td>\n",
       "      <td>-0.584654</td>\n",
       "      <td>-0.585361</td>\n",
       "      <td>-0.583648</td>\n",
       "      <td>-0.582933</td>\n",
       "      <td>-0.583448</td>\n",
       "      <td>-0.585703</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.598846</td>\n",
       "      <td>-0.596118</td>\n",
       "      <td>-0.594087</td>\n",
       "      <td>-0.598232</td>\n",
       "      <td>-0.604513</td>\n",
       "      <td>-0.607462</td>\n",
       "      <td>-0.606236</td>\n",
       "      <td>-0.602293</td>\n",
       "      <td>-0.600885</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.514671</td>\n",
       "      <td>-0.518640</td>\n",
       "      <td>-0.521285</td>\n",
       "      <td>-0.522843</td>\n",
       "      <td>-0.529080</td>\n",
       "      <td>-0.589589</td>\n",
       "      <td>-0.735513</td>\n",
       "      <td>-0.963720</td>\n",
       "      <td>-1.168972</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.566254</td>\n",
       "      <td>-1.210887</td>\n",
       "      <td>-0.879528</td>\n",
       "      <td>-0.638007</td>\n",
       "      <td>-0.532520</td>\n",
       "      <td>-0.577928</td>\n",
       "      <td>-0.628697</td>\n",
       "      <td>-0.632625</td>\n",
       "      <td>-0.606548</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.718601</td>\n",
       "      <td>-0.721093</td>\n",
       "      <td>-0.717955</td>\n",
       "      <td>-0.722386</td>\n",
       "      <td>-0.728969</td>\n",
       "      <td>-0.722774</td>\n",
       "      <td>-0.730497</td>\n",
       "      <td>-0.737313</td>\n",
       "      <td>-0.739868</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.680790</td>\n",
       "      <td>-0.686406</td>\n",
       "      <td>-0.704122</td>\n",
       "      <td>-0.722950</td>\n",
       "      <td>-0.708337</td>\n",
       "      <td>-0.690788</td>\n",
       "      <td>-0.692195</td>\n",
       "      <td>-0.702812</td>\n",
       "      <td>-0.701643</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   feat         0         1         2         3         4         5         6  \\\n",
       "0   0.0 -0.372758 -0.367844 -0.378445 -0.386751 -0.417101 -0.447204 -0.423585   \n",
       "1   0.0 -0.547370 -0.546334 -0.549748 -0.546891 -0.550253 -0.548429 -0.549099   \n",
       "2   0.0 -0.587062 -0.587322 -0.586417 -0.584654 -0.585361 -0.583648 -0.582933   \n",
       "3   0.0 -0.514671 -0.518640 -0.521285 -0.522843 -0.529080 -0.589589 -0.735513   \n",
       "4   0.0 -0.718601 -0.721093 -0.717955 -0.722386 -0.728969 -0.722774 -0.730497   \n",
       "\n",
       "          7         8  ...        42        43        44        45        46  \\\n",
       "0 -0.318506 -0.144364  ... -0.477529 -0.487402 -0.485995 -0.480247 -0.496073   \n",
       "1 -0.556074 -0.555707  ... -0.530492 -0.537032 -0.528957 -0.520373 -0.530154   \n",
       "2 -0.583448 -0.585703  ... -0.598846 -0.596118 -0.594087 -0.598232 -0.604513   \n",
       "3 -0.963720 -1.168972  ... -1.566254 -1.210887 -0.879528 -0.638007 -0.532520   \n",
       "4 -0.737313 -0.739868  ... -0.680790 -0.686406 -0.704122 -0.722950 -0.708337   \n",
       "\n",
       "         47        48        49        50  target  \n",
       "0 -0.491603 -0.537007 -0.475939 -0.479505     0.0  \n",
       "1 -0.528187 -0.533726 -0.528338 -0.518618     1.0  \n",
       "2 -0.607462 -0.606236 -0.602293 -0.600885     1.0  \n",
       "3 -0.577928 -0.628697 -0.632625 -0.606548     0.0  \n",
       "4 -0.690788 -0.692195 -0.702812 -0.701643     1.0  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsid = 'NATOPS' \n",
    "X_train, y_train, X_valid, y_valid = get_UCR_data(dsid)\n",
    "for ch in range(X_train.shape[-2]):\n",
    "    data_ch = np.concatenate((np.full((len(np.concatenate((X_train, X_valid))), 1), ch),\n",
    "                              np.concatenate((X_train, X_valid))[:, ch], \n",
    "                              np.concatenate((y_train, y_valid))[:, None]), axis=-1)\n",
    "    if ch == 0: data = data_ch\n",
    "    else: data = np.concatenate((data, data_ch))\n",
    "df = pd.DataFrame(data, columns=['feat'] + list(np.arange(X_train.shape[-1]).astype('str')) + ['target'])\n",
    "df.to_csv(path/f'data/UCR/{dsid}/{dsid}.csv', index=False)\n",
    "pd.read_csv(path/f'data/UCR/{dsid}/{dsid}.csv')\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "You would actually start here, loading an existing dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-04T13:08:17.289349Z",
     "start_time": "2019-11-04T13:08:17.174120Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8640, 53)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>feat</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>...</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.372758</td>\n",
       "      <td>-0.367844</td>\n",
       "      <td>-0.378445</td>\n",
       "      <td>-0.386751</td>\n",
       "      <td>-0.417101</td>\n",
       "      <td>-0.447204</td>\n",
       "      <td>-0.423585</td>\n",
       "      <td>-0.318506</td>\n",
       "      <td>-0.144364</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.477529</td>\n",
       "      <td>-0.487402</td>\n",
       "      <td>-0.485995</td>\n",
       "      <td>-0.480247</td>\n",
       "      <td>-0.496073</td>\n",
       "      <td>-0.491603</td>\n",
       "      <td>-0.537007</td>\n",
       "      <td>-0.475939</td>\n",
       "      <td>-0.479505</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.547370</td>\n",
       "      <td>-0.546334</td>\n",
       "      <td>-0.549748</td>\n",
       "      <td>-0.546891</td>\n",
       "      <td>-0.550253</td>\n",
       "      <td>-0.548429</td>\n",
       "      <td>-0.549099</td>\n",
       "      <td>-0.556074</td>\n",
       "      <td>-0.555707</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.530492</td>\n",
       "      <td>-0.537032</td>\n",
       "      <td>-0.528957</td>\n",
       "      <td>-0.520373</td>\n",
       "      <td>-0.530154</td>\n",
       "      <td>-0.528187</td>\n",
       "      <td>-0.533726</td>\n",
       "      <td>-0.528338</td>\n",
       "      <td>-0.518618</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.587062</td>\n",
       "      <td>-0.587322</td>\n",
       "      <td>-0.586417</td>\n",
       "      <td>-0.584654</td>\n",
       "      <td>-0.585361</td>\n",
       "      <td>-0.583648</td>\n",
       "      <td>-0.582933</td>\n",
       "      <td>-0.583448</td>\n",
       "      <td>-0.585703</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.598846</td>\n",
       "      <td>-0.596118</td>\n",
       "      <td>-0.594087</td>\n",
       "      <td>-0.598232</td>\n",
       "      <td>-0.604513</td>\n",
       "      <td>-0.607462</td>\n",
       "      <td>-0.606236</td>\n",
       "      <td>-0.602293</td>\n",
       "      <td>-0.600885</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.514671</td>\n",
       "      <td>-0.518640</td>\n",
       "      <td>-0.521285</td>\n",
       "      <td>-0.522843</td>\n",
       "      <td>-0.529080</td>\n",
       "      <td>-0.589589</td>\n",
       "      <td>-0.735513</td>\n",
       "      <td>-0.963720</td>\n",
       "      <td>-1.168972</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.566254</td>\n",
       "      <td>-1.210887</td>\n",
       "      <td>-0.879528</td>\n",
       "      <td>-0.638007</td>\n",
       "      <td>-0.532520</td>\n",
       "      <td>-0.577928</td>\n",
       "      <td>-0.628697</td>\n",
       "      <td>-0.632625</td>\n",
       "      <td>-0.606548</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.718601</td>\n",
       "      <td>-0.721093</td>\n",
       "      <td>-0.717955</td>\n",
       "      <td>-0.722386</td>\n",
       "      <td>-0.728969</td>\n",
       "      <td>-0.722774</td>\n",
       "      <td>-0.730497</td>\n",
       "      <td>-0.737313</td>\n",
       "      <td>-0.739868</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.680790</td>\n",
       "      <td>-0.686406</td>\n",
       "      <td>-0.704122</td>\n",
       "      <td>-0.722950</td>\n",
       "      <td>-0.708337</td>\n",
       "      <td>-0.690788</td>\n",
       "      <td>-0.692195</td>\n",
       "      <td>-0.702812</td>\n",
       "      <td>-0.701643</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   feat         0         1         2         3         4         5         6  \\\n",
       "0   0.0 -0.372758 -0.367844 -0.378445 -0.386751 -0.417101 -0.447204 -0.423585   \n",
       "1   0.0 -0.547370 -0.546334 -0.549748 -0.546891 -0.550253 -0.548429 -0.549099   \n",
       "2   0.0 -0.587062 -0.587322 -0.586417 -0.584654 -0.585361 -0.583648 -0.582933   \n",
       "3   0.0 -0.514671 -0.518640 -0.521285 -0.522843 -0.529080 -0.589589 -0.735513   \n",
       "4   0.0 -0.718601 -0.721093 -0.717955 -0.722386 -0.728969 -0.722774 -0.730497   \n",
       "\n",
       "          7         8  ...        42        43        44        45        46  \\\n",
       "0 -0.318506 -0.144364  ... -0.477529 -0.487402 -0.485995 -0.480247 -0.496073   \n",
       "1 -0.556074 -0.555707  ... -0.530492 -0.537032 -0.528957 -0.520373 -0.530154   \n",
       "2 -0.583448 -0.585703  ... -0.598846 -0.596118 -0.594087 -0.598232 -0.604513   \n",
       "3 -0.963720 -1.168972  ... -1.566254 -1.210887 -0.879528 -0.638007 -0.532520   \n",
       "4 -0.737313 -0.739868  ... -0.680790 -0.686406 -0.704122 -0.722950 -0.708337   \n",
       "\n",
       "         47        48        49        50  target  \n",
       "0 -0.491603 -0.537007 -0.475939 -0.479505     0.0  \n",
       "1 -0.528187 -0.533726 -0.528338 -0.518618     1.0  \n",
       "2 -0.607462 -0.606236 -0.602293 -0.600885     1.0  \n",
       "3 -0.577928 -0.628697 -0.632625 -0.606548     0.0  \n",
       "4 -0.690788 -0.692195 -0.702812 -0.701643     1.0  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsid = 'NATOPS'   # ✳️\n",
    "df = pd.read_csv(path/f'data/UCR/{dsid}/{dsid}.csv')\n",
    "print(df.shape)\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "🔎 To create the TimeSeriesList, you need to select the columns that contain the time series only, neither the target, not the feature (for multivariate TS).\n",
    "\n",
    "🔎 You should use **label_cls=CategoryList** when labels are floats but it is a classification problem. Otherwise, the fastai library would take it as a regression problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "1) You need to first TimeSeriesList (custom type of ItemList built for Time Series) from the dataframe. As cols you should only enter the data from the time series (X values, not y).\n",
    "\n",
    "2) Then you split the TimeSeriesList into 2 lists (traina and valid). There are multiple ways to do that. More info [here](https://docs.fast.ai/data_block.html)\n",
    "\n",
    "3) You need to label the ItemLists. You'll find a lot of information [here](https://docs.fast.ai/data_block.html)\n",
    "\n",
    "4) You enter the train bs and val_bs and crate the databunch object. \n",
    "\n",
    "5) You add features and seq_len."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-04T13:13:13.458996Z",
     "start_time": "2019-11-04T13:13:12.848796Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TSDataBunch;\n",
       "\n",
       "Train: LabelList (288 items)\n",
       "x: TimeSeriesList\n",
       "TimeSeries(ch=24, seq_len=52),TimeSeries(ch=24, seq_len=52),TimeSeries(ch=24, seq_len=52),TimeSeries(ch=24, seq_len=52),TimeSeries(ch=24, seq_len=52)\n",
       "y: CategoryList\n",
       "1.0,1.0,0.0,1.0,2.0\n",
       "Path: .;\n",
       "\n",
       "Valid: LabelList (72 items)\n",
       "x: TimeSeriesList\n",
       "TimeSeries(ch=24, seq_len=52),TimeSeries(ch=24, seq_len=52),TimeSeries(ch=24, seq_len=52),TimeSeries(ch=24, seq_len=52),TimeSeries(ch=24, seq_len=52)\n",
       "y: CategoryList\n",
       "3.0,5.0,2.0,0.0,4.0\n",
       "Path: .;\n",
       "\n",
       "Test: None"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db = (TimeSeriesList.from_df(df, '.', cols=df.columns.values[1:-1], feat='feat')\n",
    "      .split_by_rand_pct(valid_pct=0.2, seed=seed)\n",
    "      .label_from_df(cols='target', label_cls=CategoryList)\n",
    "      .databunch(bs=bs,  val_bs=bs * 2,  num_workers=cpus,  device=device)\n",
    "      .scale(scale_type=scale_type, scale_by_channel=scale_by_channel, \n",
    "             scale_by_sample=scale_by_sample,scale_range=scale_range)\n",
    "     )\n",
    "db"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Visualize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T18:55:30.936900Z",
     "start_time": "2019-09-30T18:55:08.124442Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "db.show_batch()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 03. Build learner 🏗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T18:55:37.074722Z",
     "start_time": "2019-09-30T18:55:37.046315Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from torchtimeseries.models import *\n",
    "# Select one arch from these state-of-the-art time series/ 1D models:\n",
    "# ResCNN, FCN, InceptionTime, ResNet\n",
    "arch = InceptionTime                     # ✳️   \n",
    "arch_kwargs = dict()                     # ✳️ \n",
    "opt_func=Ranger                          # ✳️ a state-of-the-art optimizer\n",
    "loss_func = LabelSmoothingCrossEntropy() # ✳️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T18:55:59.377716Z",
     "start_time": "2019-09-30T18:55:40.763772Z"
    },
    "hidden": true,
    "hide_input": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InceptionTime(\n",
      "  (block): InceptionBlock(\n",
      "    (inc_mods): ModuleList(\n",
      "      (0): Inception(\n",
      "        (conv_layers): ModuleList(\n",
      "          (0): Conv1d(1, 32, kernel_size=(39,), stride=(1,), padding=(19,))\n",
      "          (1): Conv1d(1, 32, kernel_size=(19,), stride=(1,), padding=(9,))\n",
      "          (2): Conv1d(1, 32, kernel_size=(9,), stride=(1,), padding=(4,))\n",
      "        )\n",
      "        (maxpool): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
      "        (conv): Conv1d(1, 32, kernel_size=(1,), stride=(1,))\n",
      "        (bn): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (act): ReLU()\n",
      "      )\n",
      "      (1): Inception(\n",
      "        (bottleneck): Conv1d(128, 32, kernel_size=(1,), stride=(1,))\n",
      "        (conv_layers): ModuleList(\n",
      "          (0): Conv1d(32, 32, kernel_size=(39,), stride=(1,), padding=(19,))\n",
      "          (1): Conv1d(32, 32, kernel_size=(19,), stride=(1,), padding=(9,))\n",
      "          (2): Conv1d(32, 32, kernel_size=(9,), stride=(1,), padding=(4,))\n",
      "        )\n",
      "        (maxpool): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
      "        (conv): Conv1d(128, 32, kernel_size=(1,), stride=(1,))\n",
      "        (bn): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (act): ReLU()\n",
      "      )\n",
      "      (2): Inception(\n",
      "        (bottleneck): Conv1d(128, 32, kernel_size=(1,), stride=(1,))\n",
      "        (conv_layers): ModuleList(\n",
      "          (0): Conv1d(32, 32, kernel_size=(39,), stride=(1,), padding=(19,))\n",
      "          (1): Conv1d(32, 32, kernel_size=(19,), stride=(1,), padding=(9,))\n",
      "          (2): Conv1d(32, 32, kernel_size=(9,), stride=(1,), padding=(4,))\n",
      "        )\n",
      "        (maxpool): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
      "        (conv): Conv1d(128, 32, kernel_size=(1,), stride=(1,))\n",
      "        (bn): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (act): ReLU()\n",
      "      )\n",
      "      (3): Inception(\n",
      "        (bottleneck): Conv1d(128, 32, kernel_size=(1,), stride=(1,))\n",
      "        (conv_layers): ModuleList(\n",
      "          (0): Conv1d(32, 32, kernel_size=(39,), stride=(1,), padding=(19,))\n",
      "          (1): Conv1d(32, 32, kernel_size=(19,), stride=(1,), padding=(9,))\n",
      "          (2): Conv1d(32, 32, kernel_size=(9,), stride=(1,), padding=(4,))\n",
      "        )\n",
      "        (maxpool): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
      "        (conv): Conv1d(128, 32, kernel_size=(1,), stride=(1,))\n",
      "        (bn): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (act): ReLU()\n",
      "      )\n",
      "      (4): Inception(\n",
      "        (bottleneck): Conv1d(128, 32, kernel_size=(1,), stride=(1,))\n",
      "        (conv_layers): ModuleList(\n",
      "          (0): Conv1d(32, 32, kernel_size=(39,), stride=(1,), padding=(19,))\n",
      "          (1): Conv1d(32, 32, kernel_size=(19,), stride=(1,), padding=(9,))\n",
      "          (2): Conv1d(32, 32, kernel_size=(9,), stride=(1,), padding=(4,))\n",
      "        )\n",
      "        (maxpool): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
      "        (conv): Conv1d(128, 32, kernel_size=(1,), stride=(1,))\n",
      "        (bn): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (act): ReLU()\n",
      "      )\n",
      "      (5): Inception(\n",
      "        (bottleneck): Conv1d(128, 32, kernel_size=(1,), stride=(1,))\n",
      "        (conv_layers): ModuleList(\n",
      "          (0): Conv1d(32, 32, kernel_size=(39,), stride=(1,), padding=(19,))\n",
      "          (1): Conv1d(32, 32, kernel_size=(19,), stride=(1,), padding=(9,))\n",
      "          (2): Conv1d(32, 32, kernel_size=(9,), stride=(1,), padding=(4,))\n",
      "        )\n",
      "        (maxpool): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)\n",
      "        (conv): Conv1d(128, 32, kernel_size=(1,), stride=(1,))\n",
      "        (bn): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (act): ReLU()\n",
      "      )\n",
      "    )\n",
      "    (res_layers): ModuleList(\n",
      "      (0): None\n",
      "      (1): None\n",
      "      (2): Sequential(\n",
      "        (0): Conv1d(1, 128, kernel_size=(1,), stride=(1,))\n",
      "        (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "      (3): None\n",
      "      (4): None\n",
      "      (5): Sequential(\n",
      "        (0): Conv1d(128, 128, kernel_size=(1,), stride=(1,))\n",
      "        (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (act): ReLU()\n",
      "  )\n",
      "  (gap): AdaptiveAvgPool1d(output_size=1)\n",
      "  (fc): Linear(in_features=128, out_features=3, bias=True)\n",
      ")\n",
      "InceptionTime\n",
      "======================================================================\n",
      "Layer (type)         Output Shape         Param #    Trainable \n",
      "======================================================================\n",
      "Conv1d               [32, 166]            1,280      True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            640        True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            320        True      \n",
      "______________________________________________________________________\n",
      "MaxPool1d            [1, 166]             0          False     \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            64         True      \n",
      "______________________________________________________________________\n",
      "BatchNorm1d          [128, 166]           256        True      \n",
      "______________________________________________________________________\n",
      "ReLU                 [128, 166]           0          False     \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            4,128      True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            39,968     True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            19,488     True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            9,248      True      \n",
      "______________________________________________________________________\n",
      "MaxPool1d            [128, 166]           0          False     \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            4,128      True      \n",
      "______________________________________________________________________\n",
      "BatchNorm1d          [128, 166]           256        True      \n",
      "______________________________________________________________________\n",
      "ReLU                 [128, 166]           0          False     \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            4,128      True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            39,968     True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            19,488     True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            9,248      True      \n",
      "______________________________________________________________________\n",
      "MaxPool1d            [128, 166]           0          False     \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            4,128      True      \n",
      "______________________________________________________________________\n",
      "BatchNorm1d          [128, 166]           256        True      \n",
      "______________________________________________________________________\n",
      "ReLU                 [128, 166]           0          False     \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            4,128      True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            39,968     True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            19,488     True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            9,248      True      \n",
      "______________________________________________________________________\n",
      "MaxPool1d            [128, 166]           0          False     \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            4,128      True      \n",
      "______________________________________________________________________\n",
      "BatchNorm1d          [128, 166]           256        True      \n",
      "______________________________________________________________________\n",
      "ReLU                 [128, 166]           0          False     \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            4,128      True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            39,968     True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            19,488     True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            9,248      True      \n",
      "______________________________________________________________________\n",
      "MaxPool1d            [128, 166]           0          False     \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            4,128      True      \n",
      "______________________________________________________________________\n",
      "BatchNorm1d          [128, 166]           256        True      \n",
      "______________________________________________________________________\n",
      "ReLU                 [128, 166]           0          False     \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            4,128      True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            39,968     True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            19,488     True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            9,248      True      \n",
      "______________________________________________________________________\n",
      "MaxPool1d            [128, 166]           0          False     \n",
      "______________________________________________________________________\n",
      "Conv1d               [32, 166]            4,128      True      \n",
      "______________________________________________________________________\n",
      "BatchNorm1d          [128, 166]           256        True      \n",
      "______________________________________________________________________\n",
      "ReLU                 [128, 166]           0          False     \n",
      "______________________________________________________________________\n",
      "Conv1d               [128, 166]           256        True      \n",
      "______________________________________________________________________\n",
      "BatchNorm1d          [128, 166]           256        True      \n",
      "______________________________________________________________________\n",
      "Conv1d               [128, 166]           16,512     True      \n",
      "______________________________________________________________________\n",
      "BatchNorm1d          [128, 166]           256        True      \n",
      "______________________________________________________________________\n",
      "ReLU                 [128, 166]           0          False     \n",
      "______________________________________________________________________\n",
      "AdaptiveAvgPool1d    [128, 1]             0          False     \n",
      "______________________________________________________________________\n",
      "Linear               [3]                  387        True      \n",
      "______________________________________________________________________\n",
      "\n",
      "Total params: 406,307\n",
      "Total trainable params: 406,307\n",
      "Total non-trainable params: 0\n",
      "Optimized with 2ec68a320\n",
      "Using true weight decay as discussed in https://www.fast.ai/2018/07/02/adam-weight-decay/ \n",
      "Loss function : LabelSmoothingCrossEntropy\n",
      "======================================================================\n",
      "Callbacks functions applied \n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = arch(db.features, db.c, **arch_kwargs).to(device)\n",
    "learn = Learner(db, model, opt_func=opt_func, loss_func=loss_func)\n",
    "learn.save('stage_0')\n",
    "print(learn.model)\n",
    "print(learn.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 04. Train model 🚵🏼‍"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### LR find 🔎"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T18:56:57.568830Z",
     "start_time": "2019-09-30T18:56:41.459187Z"
    },
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n",
      "Min numerical gradient: 1.91E-02\n",
      "Min loss divided by 10: 1.58E-01\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": [
    "learn.load('stage_0')\n",
    "learn.lr_find()\n",
    "learn.recorder.plot(suggestion=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Train 🏃🏽‍♀️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T18:57:15.641127Z",
     "start_time": "2019-09-30T18:57:15.613239Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "epochs = 100         # ✳️ \n",
    "max_lr = 1e-2        # ✳️ \n",
    "warmup = False       # ✳️\n",
    "pct_start = .7       # ✳️\n",
    "metrics = [accuracy] # ✳️\n",
    "wd = 1e-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T19:00:11.264394Z",
     "start_time": "2019-09-30T18:57:19.189653Z"
    },
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.077266</td>\n",
       "      <td>1.052416</td>\n",
       "      <td>0.532552</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.034488</td>\n",
       "      <td>1.039472</td>\n",
       "      <td>0.532552</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.018629</td>\n",
       "      <td>1.026785</td>\n",
       "      <td>0.532552</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.004773</td>\n",
       "      <td>1.014207</td>\n",
       "      <td>0.552083</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.994561</td>\n",
       "      <td>1.017995</td>\n",
       "      <td>0.554688</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.981999</td>\n",
       "      <td>0.997145</td>\n",
       "      <td>0.560938</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.974646</td>\n",
       "      <td>0.985318</td>\n",
       "      <td>0.566146</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.959153</td>\n",
       "      <td>1.318609</td>\n",
       "      <td>0.556250</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.946758</td>\n",
       "      <td>1.198705</td>\n",
       "      <td>0.572917</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.933954</td>\n",
       "      <td>1.467916</td>\n",
       "      <td>0.558594</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.919337</td>\n",
       "      <td>4.369798</td>\n",
       "      <td>0.230729</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.909586</td>\n",
       "      <td>1.512861</td>\n",
       "      <td>0.361198</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.893209</td>\n",
       "      <td>1.304682</td>\n",
       "      <td>0.449740</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.877746</td>\n",
       "      <td>1.016618</td>\n",
       "      <td>0.617708</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.862883</td>\n",
       "      <td>1.780329</td>\n",
       "      <td>0.286458</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.843123</td>\n",
       "      <td>1.710761</td>\n",
       "      <td>0.595052</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.833958</td>\n",
       "      <td>1.053252</td>\n",
       "      <td>0.564844</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.820292</td>\n",
       "      <td>1.153844</td>\n",
       "      <td>0.595833</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.803552</td>\n",
       "      <td>1.498464</td>\n",
       "      <td>0.303906</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.788703</td>\n",
       "      <td>2.573632</td>\n",
       "      <td>0.265365</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.767888</td>\n",
       "      <td>1.772255</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.744519</td>\n",
       "      <td>2.625540</td>\n",
       "      <td>0.545833</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.728171</td>\n",
       "      <td>2.047049</td>\n",
       "      <td>0.343229</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.707364</td>\n",
       "      <td>0.969602</td>\n",
       "      <td>0.682031</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.688332</td>\n",
       "      <td>1.686048</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.670569</td>\n",
       "      <td>1.192412</td>\n",
       "      <td>0.521615</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.651405</td>\n",
       "      <td>1.889236</td>\n",
       "      <td>0.576563</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.627137</td>\n",
       "      <td>0.989955</td>\n",
       "      <td>0.660677</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.614699</td>\n",
       "      <td>3.161904</td>\n",
       "      <td>0.382292</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.598821</td>\n",
       "      <td>0.779477</td>\n",
       "      <td>0.771354</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.578244</td>\n",
       "      <td>1.034928</td>\n",
       "      <td>0.673177</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.555947</td>\n",
       "      <td>0.770345</td>\n",
       "      <td>0.689323</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.535649</td>\n",
       "      <td>0.904052</td>\n",
       "      <td>0.675521</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.526675</td>\n",
       "      <td>3.569214</td>\n",
       "      <td>0.338802</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.522720</td>\n",
       "      <td>0.805266</td>\n",
       "      <td>0.792969</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.513833</td>\n",
       "      <td>0.902295</td>\n",
       "      <td>0.696875</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.510774</td>\n",
       "      <td>1.340125</td>\n",
       "      <td>0.598437</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.500918</td>\n",
       "      <td>1.242007</td>\n",
       "      <td>0.547656</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.490992</td>\n",
       "      <td>1.898185</td>\n",
       "      <td>0.423177</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.482364</td>\n",
       "      <td>0.857775</td>\n",
       "      <td>0.711458</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.482218</td>\n",
       "      <td>3.759111</td>\n",
       "      <td>0.548698</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.489302</td>\n",
       "      <td>7.239906</td>\n",
       "      <td>0.273698</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.480128</td>\n",
       "      <td>0.856619</td>\n",
       "      <td>0.763542</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.466459</td>\n",
       "      <td>0.779515</td>\n",
       "      <td>0.759375</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.455140</td>\n",
       "      <td>0.673326</td>\n",
       "      <td>0.815365</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.445449</td>\n",
       "      <td>0.965414</td>\n",
       "      <td>0.631250</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.435539</td>\n",
       "      <td>0.756833</td>\n",
       "      <td>0.764063</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.423000</td>\n",
       "      <td>0.803139</td>\n",
       "      <td>0.737500</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.413156</td>\n",
       "      <td>0.619504</td>\n",
       "      <td>0.828385</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.403097</td>\n",
       "      <td>0.600853</td>\n",
       "      <td>0.836458</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.391631</td>\n",
       "      <td>0.586309</td>\n",
       "      <td>0.869792</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.381295</td>\n",
       "      <td>0.647244</td>\n",
       "      <td>0.848698</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.373545</td>\n",
       "      <td>0.815364</td>\n",
       "      <td>0.770312</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.368023</td>\n",
       "      <td>0.649498</td>\n",
       "      <td>0.838021</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.362836</td>\n",
       "      <td>0.572853</td>\n",
       "      <td>0.873958</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.355986</td>\n",
       "      <td>0.810131</td>\n",
       "      <td>0.760417</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.349860</td>\n",
       "      <td>0.609632</td>\n",
       "      <td>0.832031</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.344475</td>\n",
       "      <td>0.585053</td>\n",
       "      <td>0.856510</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.339286</td>\n",
       "      <td>0.546061</td>\n",
       "      <td>0.879948</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.334547</td>\n",
       "      <td>0.555818</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.330564</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.793750</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.327196</td>\n",
       "      <td>0.553459</td>\n",
       "      <td>0.879167</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.325948</td>\n",
       "      <td>0.571062</td>\n",
       "      <td>0.862240</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.323905</td>\n",
       "      <td>0.624044</td>\n",
       "      <td>0.848437</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.322843</td>\n",
       "      <td>0.563070</td>\n",
       "      <td>0.864844</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.321039</td>\n",
       "      <td>0.569150</td>\n",
       "      <td>0.858854</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.318754</td>\n",
       "      <td>0.599720</td>\n",
       "      <td>0.838542</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.316752</td>\n",
       "      <td>0.544781</td>\n",
       "      <td>0.879687</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.314902</td>\n",
       "      <td>0.667440</td>\n",
       "      <td>0.828646</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.312960</td>\n",
       "      <td>0.608686</td>\n",
       "      <td>0.852344</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.310989</td>\n",
       "      <td>0.554680</td>\n",
       "      <td>0.872917</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.309441</td>\n",
       "      <td>0.576424</td>\n",
       "      <td>0.867969</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.308173</td>\n",
       "      <td>0.536844</td>\n",
       "      <td>0.886979</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.306985</td>\n",
       "      <td>0.603294</td>\n",
       "      <td>0.840104</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.305795</td>\n",
       "      <td>0.522197</td>\n",
       "      <td>0.890104</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.304489</td>\n",
       "      <td>0.558832</td>\n",
       "      <td>0.875521</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.303626</td>\n",
       "      <td>0.531456</td>\n",
       "      <td>0.880208</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.302658</td>\n",
       "      <td>0.520873</td>\n",
       "      <td>0.888802</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.302252</td>\n",
       "      <td>0.544483</td>\n",
       "      <td>0.875781</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.302131</td>\n",
       "      <td>0.535302</td>\n",
       "      <td>0.880990</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.301939</td>\n",
       "      <td>0.533701</td>\n",
       "      <td>0.885156</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.301331</td>\n",
       "      <td>0.552646</td>\n",
       "      <td>0.876823</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.300886</td>\n",
       "      <td>0.546013</td>\n",
       "      <td>0.878906</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.300437</td>\n",
       "      <td>0.528197</td>\n",
       "      <td>0.882552</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.300061</td>\n",
       "      <td>0.526811</td>\n",
       "      <td>0.883333</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.299241</td>\n",
       "      <td>0.530381</td>\n",
       "      <td>0.883333</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.298963</td>\n",
       "      <td>0.516044</td>\n",
       "      <td>0.891927</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.298455</td>\n",
       "      <td>0.514931</td>\n",
       "      <td>0.891667</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.298414</td>\n",
       "      <td>0.515347</td>\n",
       "      <td>0.890104</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.297749</td>\n",
       "      <td>0.517213</td>\n",
       "      <td>0.887500</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.297164</td>\n",
       "      <td>0.519581</td>\n",
       "      <td>0.883594</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.296880</td>\n",
       "      <td>0.522687</td>\n",
       "      <td>0.882812</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.296422</td>\n",
       "      <td>0.522712</td>\n",
       "      <td>0.883073</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.296069</td>\n",
       "      <td>0.521608</td>\n",
       "      <td>0.882031</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.295833</td>\n",
       "      <td>0.520996</td>\n",
       "      <td>0.882552</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.295439</td>\n",
       "      <td>0.518523</td>\n",
       "      <td>0.885156</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.295410</td>\n",
       "      <td>0.520304</td>\n",
       "      <td>0.884115</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.295622</td>\n",
       "      <td>0.520641</td>\n",
       "      <td>0.883854</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.295427</td>\n",
       "      <td>0.521142</td>\n",
       "      <td>0.883854</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.295301</td>\n",
       "      <td>0.519712</td>\n",
       "      <td>0.883594</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "learn.metrics = metrics\n",
    "learn.load('stage_0')\n",
    "learn.fit_one_cycle(epochs, max_lr=max_lr, pct_start=pct_start, moms=(.95, .85) if warmup else (.95, .95),\n",
    "                    div_factor=25.0 if warmup else 1., wd=wd)\n",
    "learn.save('stage_1')\n",
    "learn.recorder.plot_lr()\n",
    "learn.recorder.plot_losses()\n",
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T19:01:33.678116Z",
     "start_time": "2019-09-30T19:01:33.623756Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arch</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>accuracy train loss</th>\n",
       "      <th>max_accuracy</th>\n",
       "      <th>n_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>InceptionTime</td>\n",
       "      <td>0.88359</td>\n",
       "      <td>0.88385</td>\n",
       "      <td>0.89193</td>\n",
       "      <td>406307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            arch accuracy accuracy train loss max_accuracy n_params\n",
       "0  InceptionTime  0.88359             0.88385      0.89193   406307"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "archs_names, acc_, acces_, acc5_, n_params_,  = [], [], [], [], []\n",
    "archs_names.append(arch.__name__)\n",
    "early_stop = math.ceil(np.argmin(learn.recorder.losses) / len(learn.data.train_dl))\n",
    "acc_.append('{:.5}'.format(learn.recorder.metrics[-1][0].item()))\n",
    "acces_.append('{:.5}'.format(learn.recorder.metrics[early_stop - 1][0].item()))\n",
    "acc5_.append('{:.5}'.format(np.mean(np.max(learn.recorder.metrics))))\n",
    "n_params_.append(count_params(learn))\n",
    "clear_output()\n",
    "df = (pd.DataFrame(np.stack((archs_names, acc_, acces_, acc5_, n_params_)).T,\n",
    "                   columns=['arch', 'accuracy', 'accuracy train loss', 'max_accuracy','n_params'])\n",
    "      .sort_values('accuracy train loss', ascending=False).reset_index(drop=True))\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T19:02:15.237723Z",
     "start_time": "2019-09-30T19:02:14.228684Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interp = ClassificationInterpretation.from_learner(learn)\n",
    "interp.plot_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}