TPNILM_UKDALE_run.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "TPNILM_UKDALE_run.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "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": "3.7.1"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1ICLFy-3kSz",
        "colab_type": "code",
        "outputId": "a6e1920a-002a-4de7-bca4-8895b9a4d9db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "!pip install torch torchvision pandas scikit-learn matplotlib tables pyarrow"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (1.3.1)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.6/dist-packages (0.4.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (0.25.3)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (0.22.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (3.1.2)\n",
            "Requirement already satisfied: tables in /usr/local/lib/python3.6/dist-packages (3.4.4)\n",
            "Requirement already satisfied: pyarrow in /usr/local/lib/python3.6/dist-packages (0.14.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch) (1.17.5)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision) (6.2.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.12.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas) (2.6.1)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn) (1.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn) (0.14.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.4.6)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.1.0)\n",
            "Requirement already satisfied: numexpr>=2.5.2 in /usr/local/lib/python3.6/dist-packages (from tables) (2.7.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib) (42.0.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "saPKpECRVlln",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import torch.utils.data as data\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "import math\n",
        "from collections import defaultdict, OrderedDict\n",
        "import copy\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.metrics import f1_score, precision_score, recall_score\n",
        "from sklearn.metrics import accuracy_score, mean_absolute_error, matthews_corrcoef\n",
        "\n",
        "import matplotlib\n",
        "matplotlib.use('Agg')\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "QPxd9q-PhAXB"
      },
      "source": [
        "# Build dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2t-SBZDmjvHJ",
        "outputId": "c5ec6875-b48c-47d3-dfc3-0b7beb6346cc",
        "colab": {}
      },
      "source": [
        "!curl https://data.ukedc.rl.ac.uk/browse/edc/efficiency/residential/EnergyConsumption/Domestic/UK-DALE-2015/UK-DALE-disaggregated/ukdale.h5.tgz -o ukdale.h5.tgz"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100 2835M  100 2835M    0     0  35.5M      0  0:01:19  0:01:19 --:--:-- 53.9M0:00:02 53.7M\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jR0T2s5QNkqv",
        "outputId": "7d50ab1c-fd17-49ee-982b-5199a97f7143",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 63
        }
      },
      "source": [
        "!tar xvfz ukdale.h5.tgz"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ukdale.h5\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "srIUiAAXPRrk",
        "colab": {}
      },
      "source": [
        "!rm ukdale.h5.tgz"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Lf-TVlKgPEVs",
        "outputId": "9536941e-9cb6-4e6d-91ae-2e01c4dce53b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        }
      },
      "source": [
        "!ls -l"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 3267076\r\n",
            "-rw-r--r-- 1 root root     370852 Oct 16 08:04 TPNet_UKDALE_run.ipynb\r\n",
            "-rw-r--r-- 1 root root        850 Oct 16 07:52 house_1_labels.dat\r\n",
            "-rw-r--r-- 1 root root        228 Oct 16 07:52 house_2_labels.dat\r\n",
            "-rw-r--r-- 1 root root         60 Oct 16 07:52 house_3_labels.dat\r\n",
            "-rw-r--r-- 1 root root        111 Oct 16 07:52 house_4_labels.dat\r\n",
            "-rw-r--r-- 1 root root        376 Oct 16 07:52 house_5_labels.dat\r\n",
            "-rw-r--r-- 1 root root          0 Oct 16 07:50 onstart.log\r\n",
            "-rw-r--r-- 1 root root         75 Oct 16 07:50 onstart.sh\r\n",
            "-rw-rw-r-- 1 1000 1000 3345086829 Jan  5  2015 ukdale.h5\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "H9g6B69jPJkz",
        "colab": {}
      },
      "source": [
        "store = pd.HDFStore('ukdale.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4y3vM6zQz5DC",
        "colab": {}
      },
      "source": [
        "def resample_meter(store=None, building=1, meter=1, period='1min', cutoff=1000.):\n",
        "    key = '/building{}/elec/meter{}'.format(building,meter)\n",
        "    m = store[key]\n",
        "    v = m.values.flatten()\n",
        "    t = m.index\n",
        "    s = pd.Series(v, index=t).clip(0.,cutoff)\n",
        "    s[s<10.] = 0.\n",
        "    return s.resample('1s').ffill(limit=300).fillna(0.).resample(period).mean().tz_convert('UTC')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_a-pqdClkRmO",
        "colab": {}
      },
      "source": [
        "def get_series(datastore, house, label, cutoff):\n",
        "    filename = './house_%1d_labels.dat' %house\n",
        "    print(filename)\n",
        "    labels = pd.read_csv(filename, delimiter=' ', header=None, index_col=0).to_dict()[1]\n",
        "    \n",
        "    for i in labels:\n",
        "        if labels[i] == label:\n",
        "            print(i, labels[i])\n",
        "            s = resample_meter(store, house, i, '1min', cutoff)\n",
        "            #s = resample_meter(store, house, i, '6s', cutoff)\n",
        "    \n",
        "    s.index.name = 'datetime'\n",
        "    \n",
        "    return s"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6lRVLSIzk6B5",
        "outputId": "bc980e76-1dca-4841-cddd-f10d6ee69d48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "house = 1\n",
        "m = get_series(store, house, 'aggregate', 10000.)\n",
        "m.name = 'aggregate'\n",
        "a1 = get_series(store, house, 'kettle', 3100.)\n",
        "a1.name = 'kettle'\n",
        "a2 = get_series(store, house, 'fridge', 300.)\n",
        "a2.name = 'fridge'\n",
        "a3 = get_series(store, house, 'washing_machine', 2500.)\n",
        "a3.name = 'washing_machine'\n",
        "a4 = get_series(store, house, 'microwave', 3000.)\n",
        "a4.name = 'microwave'\n",
        "a5 = get_series(store, house, 'dishwasher', 2500.)\n",
        "a5.name = 'dish_washer'\n",
        "ds_1 = pd.concat([m, a1, a2, a3, a4, a5], axis=1)\n",
        "ds_1.fillna(method='pad', inplace=True)\n",
        "\n",
        "ds_1_train = ds_1[pd.datetime(2013,4,12):pd.datetime(2014,12,15)]\n",
        "ds_1_valid = ds_1[pd.datetime(2014,12,15):]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "./house_1_labels.dat\n",
            "1 aggregate\n",
            "./house_1_labels.dat\n",
            "10 kettle\n",
            "./house_1_labels.dat\n",
            "12 fridge\n",
            "./house_1_labels.dat\n",
            "5 washing_machine\n",
            "./house_1_labels.dat\n",
            "13 microwave\n",
            "./house_1_labels.dat\n",
            "6 dishwasher\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5_PW33ZLpwsK",
        "outputId": "776d64c0-fcfa-4637-f983-cf7207bd6ea2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "house = 2\n",
        "m = get_series(store, house, 'aggregate', 10000.)\n",
        "m.name = 'aggregate'\n",
        "a1 = get_series(store, house, 'kettle', 3100.)\n",
        "a1.name = 'kettle'\n",
        "a2 = get_series(store, house, 'fridge', 300.)\n",
        "a2.name = 'fridge'\n",
        "a3 = get_series(store, house, 'washing_machine', 2500.)\n",
        "a3.name = 'washing_machine'\n",
        "a4 = get_series(store, house, 'microwave', 3000.)\n",
        "a4.name = 'microwave'\n",
        "a5 = get_series(store, house, 'dish_washer', 2500.)\n",
        "a5.name = 'dish_washer'\n",
        "ds_2 = pd.concat([m, a1, a2, a3, a4, a5], axis=1)\n",
        "ds_2.fillna(method='pad', inplace=True)\n",
        "\n",
        "ds_2_train = ds_2[pd.datetime(2013,5,22):pd.datetime(2013,10,3,6,16)]\n",
        "ds_2_valid = ds_2[pd.datetime(2013,10,3,6,16):]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "./house_2_labels.dat\n",
            "1 aggregate\n",
            "./house_2_labels.dat\n",
            "8 kettle\n",
            "./house_2_labels.dat\n",
            "14 fridge\n",
            "./house_2_labels.dat\n",
            "12 washing_machine\n",
            "./house_2_labels.dat\n",
            "15 microwave\n",
            "./house_2_labels.dat\n",
            "13 dish_washer\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WPRtFZqclByE",
        "outputId": "234de859-ec02-433f-8d75-86dbfe448a26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "house = 3\n",
        "m = get_series(store, house, 'aggregate', 10000.)\n",
        "m.name = 'aggregate'\n",
        "a1 = get_series(store, house, 'kettle', 3100.)\n",
        "a1.name = 'kettle'\n",
        "a2 = 0.*m\n",
        "a2.name = 'fridge'\n",
        "a3 = 0.*m\n",
        "a3.name = 'washing_machine'\n",
        "a4 = 0.*m\n",
        "a4.name = 'microwave'\n",
        "a5 = 0.*m\n",
        "a5.name = 'dish_washer'\n",
        "ds_3 = pd.concat([m, a1, a2, a3, a4, a5], axis=1)\n",
        "ds_3.fillna(method='pad', inplace=True)\n",
        "\n",
        "ds_3_train = ds_3[pd.datetime(2013,2,27):pd.datetime(2013,4,1,6,15)]\n",
        "ds_3_valid = ds_3[pd.datetime(2013,4,1,6,15):]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "./house_3_labels.dat\n",
            "1 aggregate\n",
            "./house_3_labels.dat\n",
            "2 kettle\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "q0xA9KgDqt1q",
        "outputId": "e5eb5263-7e1c-4fbf-c46c-3a2c3e55858a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "house = 4\n",
        "m = get_series(store, house, 'aggregate', 10000.)\n",
        "m.name = 'aggregate'\n",
        "a1 = get_series(store, house, 'kettle_radio', 3100.)\n",
        "a1.name = 'kettle'\n",
        "a2 = get_series(store, house, 'freezer', 300.)\n",
        "a2.name = 'fridge'\n",
        "a3 = 0.*m\n",
        "a3.name = 'washing_machine'\n",
        "a4 = 0.*m\n",
        "a4.name = 'microwave'\n",
        "a5 = 0.*m\n",
        "a5.name = 'dish_washer'\n",
        "ds_4 = pd.concat([m, a1, a2, a3, a4, a5], axis=1)\n",
        "ds_4.fillna(method='pad', inplace=True)\n",
        "\n",
        "ds_4_train = ds_4[pd.datetime(2013,3,9):pd.datetime(2013,9,24,6,15)]\n",
        "ds_4_valid = ds_4[pd.datetime(2013,9,24,6,15):]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "./house_4_labels.dat\n",
            "1 aggregate\n",
            "./house_4_labels.dat\n",
            "3 kettle_radio\n",
            "./house_4_labels.dat\n",
            "5 freezer\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-G2EBJcWrM5V",
        "outputId": "088066b8-e349-475e-fdf0-0bd14abcc57c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "house = 5\n",
        "m = get_series(store, house, 'aggregate', 10000.)\n",
        "m.name = 'aggregate'\n",
        "a1 = get_series(store, house, 'kettle', 3100.)\n",
        "a1.name = 'kettle'\n",
        "a2 = get_series(store, house, 'fridge_freezer', 300.)\n",
        "a2.name = 'fridge'\n",
        "a3 = get_series(store, house, 'washer_dryer', 2500.)\n",
        "a3.name = 'washing_machine'\n",
        "a4 = get_series(store, house, 'microwave', 3000.)\n",
        "a4.name = 'microwave'\n",
        "a5 = get_series(store, house, 'dishwasher', 2500.)\n",
        "a5.name = 'dish_washer'\n",
        "ds_5 = pd.concat([m, a1, a2, a3, a4, a5], axis=1)\n",
        "ds_5.fillna(method='pad', inplace=True)\n",
        "\n",
        "ds_5_train = ds_5[pd.datetime(2014,6,29):pd.datetime(2014,9,1)]\n",
        "ds_5_valid = ds_5[pd.datetime(2014,9,1):]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "./house_5_labels.dat\n",
            "1 aggregate\n",
            "./house_5_labels.dat\n",
            "18 kettle\n",
            "./house_5_labels.dat\n",
            "19 fridge_freezer\n",
            "./house_5_labels.dat\n",
            "24 washer_dryer\n",
            "./house_5_labels.dat\n",
            "23 microwave\n",
            "./house_5_labels.dat\n",
            "22 dishwasher\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "69C1JT0kdJ_W",
        "colab": {}
      },
      "source": [
        "ds_1_train.reset_index().to_feather('./UKDALE_1_train.feather')\n",
        "ds_2_train.reset_index().to_feather('./UKDALE_2_train.feather')\n",
        "ds_3_train.reset_index().to_feather('./UKDALE_3_train.feather')\n",
        "ds_4_train.reset_index().to_feather('./UKDALE_4_train.feather')\n",
        "ds_5_train.reset_index().to_feather('./UKDALE_5_train.feather')\n",
        "\n",
        "ds_1_valid.reset_index().to_feather('./UKDALE_1_valid.feather')\n",
        "ds_2_valid.reset_index().to_feather('./UKDALE_2_valid.feather')\n",
        "ds_3_valid.reset_index().to_feather('./UKDALE_3_valid.feather')\n",
        "ds_4_valid.reset_index().to_feather('./UKDALE_4_valid.feather')\n",
        "ds_5_valid.reset_index().to_feather('./UKDALE_5_valid.feather')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PvY0BjXDdKXR"
      },
      "source": [
        "# Read the feather dataframe resampled\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "I5vzLJT4LwVl",
        "colab": {}
      },
      "source": [
        "def get_status(app, threshold, min_off, min_on):\n",
        "    condition = app > threshold\n",
        "    # Find the indicies of changes in \"condition\"\n",
        "    d = np.diff(condition)\n",
        "    idx, = d.nonzero() \n",
        "\n",
        "    # We need to start things after the change in \"condition\". Therefore, \n",
        "    # we'll shift the index by 1 to the right.\n",
        "    idx += 1\n",
        "\n",
        "    if condition[0]:\n",
        "        # If the start of condition is True prepend a 0\n",
        "        idx = np.r_[0, idx]\n",
        "\n",
        "    if condition[-1]:\n",
        "        # If the end of condition is True, append the length of the array\n",
        "        idx = np.r_[idx, condition.size] # Edit\n",
        "\n",
        "    # Reshape the result into two columns\n",
        "    idx.shape = (-1,2)\n",
        "    on_events = idx[:,0].copy()\n",
        "    off_events = idx[:,1].copy()\n",
        "    assert len(on_events) == len(off_events)\n",
        "\n",
        "    if len(on_events) > 0:\n",
        "        off_duration = on_events[1:] - off_events[:-1]\n",
        "        off_duration = np.insert(off_duration, 0, 1000.)\n",
        "        on_events = on_events[off_duration > min_off]\n",
        "        off_events = off_events[np.roll(off_duration, -1) > min_off]\n",
        "        assert len(on_events) == len(off_events)\n",
        "\n",
        "        on_duration = off_events - on_events\n",
        "        on_events = on_events[on_duration > min_on]\n",
        "        off_events = off_events[on_duration > min_on]\n",
        "\n",
        "    s = app.copy()\n",
        "    #s.iloc[:] = 0.\n",
        "    s[:] = 0.\n",
        "\n",
        "    for on, off in zip(on_events, off_events):\n",
        "        #s.iloc[on:off] = 1.\n",
        "        s[on:off] = 1.\n",
        "    \n",
        "    return s"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5iR9d91y1ktT",
        "colab": {}
      },
      "source": [
        "class Power(data.Dataset):\n",
        "    def __init__(self, meter=None, appliance=None, status=None, \n",
        "                 length=256, border=680, max_power=1., train=False):\n",
        "        self.length = length\n",
        "        self.border = border\n",
        "        self.max_power = max_power\n",
        "        self.train = train\n",
        "\n",
        "        self.meter = meter.copy()/self.max_power\n",
        "        self.appliance = appliance.copy()/self.max_power\n",
        "        self.status = status.copy()\n",
        "\n",
        "        self.epochs = (len(self.meter) - 2*self.border) // self.length\n",
        "        \n",
        "    def __getitem__(self, index):\n",
        "        i = index * self.length + self.border\n",
        "        if self.train:\n",
        "            i = np.random.randint(self.border, len(self.meter) - self.length - self.border)\n",
        "\n",
        "        x = self.meter.iloc[i-self.border:i+self.length+self.border].values.astype('float32')\n",
        "        y = self.appliance.iloc[i:i+self.length].values.astype('float32')\n",
        "        s = self.status.iloc[i:i+self.length].values.astype('float32')\n",
        "        x -= x.mean()\n",
        "        \n",
        "        return x, y, s\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.epochs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mirkSD9a1qTQ",
        "outputId": "df7353f4-93f8-4583-da76-3d999bb2d248",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "class Encoder(nn.Module):\n",
        "    def __init__(self, in_features=3, out_features=1, kernel_size=3, padding=1, stride=1):\n",
        "        super(Encoder, self).__init__()\n",
        "        self.conv = nn.Conv1d(in_features, out_features, kernel_size=kernel_size, padding=padding, stride=stride, bias=False)\n",
        "        self.bn = nn.BatchNorm1d(out_features)\n",
        "        self.drop = nn.Dropout(0.1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        #return self.bn(F.relu(self.conv(x)))\n",
        "        return self.drop(self.bn(F.relu(self.conv(x))))\n",
        "\n",
        "class TemporalPooling(nn.Module):\n",
        "    def __init__(self, in_features=3, out_features=1, kernel_size=2):\n",
        "        super(TemporalPooling, self).__init__()\n",
        "        self.kernel_size = kernel_size\n",
        "        self.pool = nn.AvgPool1d(kernel_size=self.kernel_size, stride=self.kernel_size)\n",
        "        self.conv = nn.Conv1d(in_features, out_features, kernel_size=1, padding=0)\n",
        "        #self.upsample = nn.Upsample( scale_factor=kernel_size, mode='linear', align_corners=True)\n",
        "        self.bn = nn.BatchNorm1d(out_features)\n",
        "        self.drop = nn.Dropout(0.1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(x)\n",
        "        x = self.conv(x)\n",
        "        x = self.bn(F.relu(x))\n",
        "        #return self.upsample(x)\n",
        "        #return self.drop(self.upsample(x))\n",
        "        return self.drop(F.interpolate(x, scale_factor=self.kernel_size, mode='linear', align_corners=True))\n",
        "\n",
        "class Decoder(nn.Module):\n",
        "    def __init__(self, in_features=3, out_features=1, kernel_size=2, stride=2):\n",
        "        super(Decoder, self).__init__()\n",
        "        self.conv = nn.ConvTranspose1d(in_features, out_features, kernel_size=kernel_size, stride=stride, bias=False)\n",
        "        self.bn = nn.BatchNorm1d(out_features)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return F.relu(self.conv(x))\n",
        "\n",
        "class PTPNet(nn.Module):\n",
        "\n",
        "    def __init__(self, in_channels=3, out_channels=1, init_features=32):\n",
        "        super(PTPNet, self).__init__()\n",
        "        p = 2\n",
        "        k = 1\n",
        "        features = init_features\n",
        "        self.encoder1 = Encoder(in_channels, features, kernel_size=3, padding=0)\n",
        "        self.pool1 = nn.MaxPool1d(kernel_size=p, stride=p)\n",
        "        self.encoder2 = Encoder(features * 1**k, features * 2**k, kernel_size=3, padding=0)\n",
        "        self.pool2 = nn.MaxPool1d(kernel_size=p, stride=p)\n",
        "        self.encoder3 = Encoder(features * 2**k, features * 4**k, kernel_size=3, padding=0)\n",
        "        self.pool3 = nn.MaxPool1d(kernel_size=p, stride=p)\n",
        "        self.encoder4 = Encoder(features * 4**k, features * 8**k, kernel_size=3, padding=0)\n",
        "        \n",
        "        self.tpool1 = TemporalPooling(features*8**k, features*2**k, kernel_size=5)\n",
        "        self.tpool2 = TemporalPooling(features*8**k, features*2**k, kernel_size=10)\n",
        "        self.tpool3 = TemporalPooling(features*8**k, features*2**k, kernel_size=20)\n",
        "        self.tpool4 = TemporalPooling(features*8**k, features*2**k, kernel_size=30)\n",
        "\n",
        "        self.decoder = Decoder(2*features * 8**k, features * 1**k, kernel_size=p**3, stride=p**3)\n",
        "\n",
        "        self.activation = nn.Conv1d(features * 1**k, out_channels, kernel_size=1, padding=0)\n",
        "\n",
        "        \n",
        "    def forward(self, x):\n",
        "        enc1 = self.encoder1(x)\n",
        "        enc2 = self.encoder2(self.pool1(enc1))\n",
        "        enc3 = self.encoder3(self.pool2(enc2))\n",
        "        enc4 = self.encoder4(self.pool3(enc3))\n",
        "\n",
        "        tp1 = self.tpool1(enc4)\n",
        "        tp2 = self.tpool2(enc4)\n",
        "        tp3 = self.tpool3(enc4)\n",
        "        tp4 = self.tpool4(enc4)\n",
        "\n",
        "        dec = self.decoder(torch.cat([enc4, tp1, tp2, tp3, tp4], dim=1))\n",
        "\n",
        "        act = self.activation(dec)\n",
        "        return act\n",
        "\n",
        "x = torch.randn(32,1,60*8+2*16)\n",
        "model = PTPNet(1,3,32)\n",
        "print(model(x).shape)\n",
        "print(sum(p.numel() for p in model.parameters() if p.requires_grad))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([32, 3, 480])\n",
            "327619\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fMsGSOln1wS3",
        "colab": {}
      },
      "source": [
        "def train_model(model, batch_size, n_epochs, filename):\n",
        "    \n",
        "    # to track the training loss as the model trains\n",
        "    train_losses = []\n",
        "    # to track the validation loss as the model trains\n",
        "    valid_losses = []\n",
        "    # to track the test loss as the model trains\n",
        "    test_losses = []\n",
        "    # to track the average training loss per epoch as the model trains\n",
        "    avg_train_losses = []\n",
        "    # to track the average validation loss per epoch as the model trains\n",
        "    avg_valid_losses = [] \n",
        "    # to track the average test loss per epoch as the model trains\n",
        "    avg_test_losses = [] \n",
        "    \n",
        "    min_loss = np.inf\n",
        "    \n",
        "    # initialize the early_stopping object\n",
        "    #patience = 10\n",
        "    #early_stopping = EarlyStopping(patience=patience, verbose=True)\n",
        "    \n",
        "    for epoch in range(1, n_epochs + 1):\n",
        "\n",
        "        ###################\n",
        "        # train the model #\n",
        "        ###################\n",
        "        model.train() # prep model for training\n",
        "        for batch, (data, target_power, target_status) in enumerate(train_loader, 1):\n",
        "            data = data.unsqueeze(1).cuda()\n",
        "            target_power = target_power.cuda()\n",
        "            target_status = target_status.cuda()\n",
        "            \n",
        "            # clear the gradients of all optimized variables\n",
        "            optimizer.zero_grad()\n",
        "            # forward pass: compute predicted outputs by passing inputs to the model\n",
        "            output_status = model(data).permute(0,2,1)\n",
        "            # calculate the loss\n",
        "            loss = criterion(output_status, target_status)\n",
        "            # backward pass: compute gradient of the loss with respect to model parameters\n",
        "            loss.backward()\n",
        "            # perform a single optimization step (parameter update)\n",
        "            optimizer.step()\n",
        "            # record training loss\n",
        "            train_losses.append(loss.item())\n",
        "\n",
        "        ######################    \n",
        "        # validate the model #\n",
        "        ######################\n",
        "        model.eval() # prep model for evaluation\n",
        "        for data, target_power, target_status in valid_loader:\n",
        "            data = data.unsqueeze(1).cuda()\n",
        "            target_power = target_power.cuda()\n",
        "            target_status = target_status.cuda()\n",
        "            \n",
        "            # forward pass: compute predicted outputs by passing inputs to the model\n",
        "            output_status = model(data).permute(0,2,1)\n",
        "            # calculate the loss\n",
        "            loss = criterion(output_status, target_status)\n",
        "            # record validation loss\n",
        "            valid_losses.append(loss.item())\n",
        "\n",
        "        ##################    \n",
        "        # test the model #\n",
        "        ##################\n",
        "        model.eval() # prep model for evaluation\n",
        "        for data, target_power, target_status in test_loader:\n",
        "            data = data.unsqueeze(1).cuda()\n",
        "            target_power = target_power.cuda()\n",
        "            target_status = target_status.cuda()\n",
        "            \n",
        "            # forward pass: compute predicted outputs by passing inputs to the model\n",
        "            output_status = model(data).permute(0,2,1)\n",
        "            # calculate the loss\n",
        "            loss = criterion(output_status, target_status)\n",
        "            # record validation loss\n",
        "            test_losses.append(loss.item())\n",
        "\n",
        "        # print training/validation statistics \n",
        "        # calculate average loss over an epoch\n",
        "        train_loss = np.average(train_losses)\n",
        "        valid_loss = np.average(valid_losses)\n",
        "        test_loss = np.average(test_losses)\n",
        "        avg_train_losses.append(train_loss)\n",
        "        avg_valid_losses.append(valid_loss)\n",
        "        avg_test_losses.append(test_loss)\n",
        "        \n",
        "        epoch_len = len(str(n_epochs))\n",
        "        \n",
        "        print_msg = (f'[{epoch:>{epoch_len}}/{n_epochs:>{epoch_len}}] ' +\n",
        "                     f'train_loss: {train_loss:.5f} ' +\n",
        "                     f'valid_loss: {valid_loss:.5f} ' +\n",
        "                     f'test_loss: {test_loss:.5f} ')\n",
        "        \n",
        "        print(print_msg)\n",
        "        \n",
        "        # clear lists to track next epoch\n",
        "        train_losses = []\n",
        "        valid_losses = []\n",
        "        test_losses = []\n",
        "        \n",
        "        # early_stopping needs the validation loss to check if it has decresed, \n",
        "        # and if it has, it will make a checkpoint of the current model\n",
        "        #early_stopping(valid_loss, model)\n",
        "        #if (early_stopping.early_stop and (epoch > 80)):\n",
        "        #    break\n",
        "        \n",
        "        if valid_loss < min_loss:\n",
        "            print(f'Validation loss decreased ({min_loss:.6f} --> {valid_loss:.6f}).  Saving model ...')\n",
        "            torch.save(model.state_dict(), filename)\n",
        "            min_loss = valid_loss\n",
        "        \n",
        "    # load the last checkpoint with the best model\n",
        "    model.load_state_dict(torch.load(filename))\n",
        "    \n",
        "    return  model, avg_train_losses, avg_valid_losses, avg_test_losses"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "pP9W8d_K12mm",
        "colab": {}
      },
      "source": [
        "def evaluate_activation(model, loader, a):\n",
        "    x_true = []\n",
        "    s_true = []\n",
        "    p_true = []\n",
        "    s_hat = []\n",
        "    \n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        for x, p, s in loader:\n",
        "            x = x.unsqueeze(1).cuda()\n",
        "            p = p.permute(0,2,1)[:,a,:]\n",
        "            s = s.permute(0,2,1)[:,a,:]\n",
        "            \n",
        "            sh = model(x)\n",
        "            sh = torch.sigmoid(sh[:,a,:])\n",
        "            \n",
        "            s_hat.append(sh.contiguous().view(-1).detach().cpu().numpy())\n",
        "            \n",
        "            x_true.append(x[:,:,BORDER:-BORDER].contiguous().view(-1).detach().cpu().numpy())\n",
        "            s_true.append(s.contiguous().view(-1).detach().cpu().numpy())\n",
        "            p_true.append(p.contiguous().view(-1).detach().cpu().numpy())\n",
        "    x_true = np.hstack(x_true)\n",
        "    s_true = np.hstack(s_true)\n",
        "    p_true = np.hstack(p_true)\n",
        "    s_hat = np.hstack(s_hat)\n",
        "\n",
        "    return x_true, p_true, s_true, s_hat"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "OeLT8jHC1-mA",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PSypXakc14qb",
        "colab": {}
      },
      "source": [
        "APPLIANCE = ['fridge', 'dish_washer', 'washing_machine']\n",
        "THRESHOLD = [50., 10., 20.]\n",
        "MIN_ON = [1., 30., 30.]\n",
        "MIN_OFF = [1., 30., 3.]\n",
        "\n",
        "METER = 'aggregate'\n",
        "SEQ_LEN = 60*8\n",
        "BORDER = 16\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "MAX_POWER = 2000."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mO2wSprQfV6x",
        "colab": {}
      },
      "source": [
        "ds_meter = []\n",
        "ds_appliance = []\n",
        "ds_status = []\n",
        "for i in range(5):\n",
        "    ds = pd.read_feather('./UKDALE_%d_train.feather' %(i+1))\n",
        "    ds.set_index('datetime', inplace=True)\n",
        "    \n",
        "    meter = ds[METER]\n",
        "    appliances = ds[APPLIANCE]\n",
        "    \n",
        "    status = pd.DataFrame()\n",
        "    for a in range(len(APPLIANCE)):\n",
        "        status = pd.concat([status, get_status(ds[APPLIANCE[a]], THRESHOLD[a], MIN_OFF[a], MIN_ON[a])], axis=1)\n",
        "    \n",
        "    ds_meter.append(meter)\n",
        "    ds_appliance.append(appliances)\n",
        "    ds_status.append(status)\n",
        "\n",
        "ds_len = [len(ds_meter[i]) for i in range(5)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sGfWAsgWbVtt",
        "colab_type": "code",
        "outputId": "2c141854-5a34-469f-e034-c5f535f83c52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "(ds_status[1].diff()==1).sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fridge             3525\n",
              "dish_washer          98\n",
              "washing_machine      54\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tq5YJh7lzqvS",
        "colab_type": "code",
        "outputId": "494fd4da-e7ad-485e-9da1-4ffee3d198a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "ds_status[1].describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>fridge</th>\n",
              "      <th>dish_washer</th>\n",
              "      <th>washing_machine</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>193337.00000</td>\n",
              "      <td>193337.000000</td>\n",
              "      <td>193337.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.37792</td>\n",
              "      <td>0.028918</td>\n",
              "      <td>0.011317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.48503</td>\n",
              "      <td>0.167543</td>\n",
              "      <td>0.105773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             fridge    dish_washer  washing_machine\n",
              "count  193337.00000  193337.000000    193337.000000\n",
              "mean        0.37792       0.028918         0.011317\n",
              "std         0.48503       0.167543         0.105773\n",
              "min         0.00000       0.000000         0.000000\n",
              "25%         0.00000       0.000000         0.000000\n",
              "50%         0.00000       0.000000         0.000000\n",
              "75%         1.00000       0.000000         0.000000\n",
              "max         1.00000       1.000000         1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iLZ7PmzG7jHS",
        "colab": {}
      },
      "source": [
        "ds_house_train = [Power(ds_meter[i][:int(0.8*ds_len[i])], \n",
        "                        ds_appliance[i][:int(0.8*ds_len[i])], \n",
        "                        ds_status[i][:int(0.8*ds_len[i])], \n",
        "                        SEQ_LEN, BORDER, MAX_POWER, True) for i in range(5+0)]\n",
        "\n",
        "ds_house_valid = [Power(ds_meter[i][int(0.8*ds_len[i]):int(0.9*ds_len[i])], \n",
        "                        ds_appliance[i][int(0.8*ds_len[i]):int(0.9*ds_len[i])],\n",
        "                        ds_status[i][int(0.8*ds_len[i]):int(0.9*ds_len[i])], \n",
        "                        SEQ_LEN, BORDER, MAX_POWER, False) for i in range(5+0)]\n",
        "\n",
        "ds_house_test  = [Power(ds_meter[i][int(0.9*ds_len[i]):], \n",
        "                        ds_appliance[i][int(0.9*ds_len[i]):],\n",
        "                        ds_status[i][int(0.9*ds_len[i]):], \n",
        "                        SEQ_LEN, BORDER, MAX_POWER, False) for i in range(5+0)]\n",
        "\n",
        "ds_house_total  = [Power(ds_meter[i], ds_appliance[i], ds_status[i], \n",
        "                         SEQ_LEN, BORDER, MAX_POWER, False) for i in range(5+0)]\n",
        "\n",
        "ds_train_seen = torch.utils.data.ConcatDataset([ds_house_train[0], \n",
        "                                                ds_house_train[1], \n",
        "                                                #ds_house_train[2], \n",
        "                                                #ds_house_train[3],\n",
        "                                                ds_house_train[4]\n",
        "                                                ])\n",
        "ds_valid_seen = torch.utils.data.ConcatDataset([ds_house_valid[0], \n",
        "                                                #ds_house_valid[1], \n",
        "                                                #ds_house_valid[2], \n",
        "                                                #ds_house_valid[3], \n",
        "                                                #ds_house_valid[4]\n",
        "                                                ])\n",
        "\n",
        "dl_train_seen = DataLoader(dataset = ds_train_seen, batch_size = BATCH_SIZE, shuffle=True)\n",
        "dl_valid_seen = DataLoader(dataset = ds_valid_seen, batch_size = BATCH_SIZE, shuffle=False)\n",
        "dl_test_seen = DataLoader(dataset = ds_house_test[0], batch_size = BATCH_SIZE, shuffle=False)\n",
        "\n",
        "ds_train_unseen = torch.utils.data.ConcatDataset([ds_house_train[0], \n",
        "                                                  #ds_house_train[1], \n",
        "                                                  #ds_house_train[2], \n",
        "                                                  #ds_house_train[3], \n",
        "                                                  ds_house_train[4]\n",
        "                                                  ])\n",
        "ds_valid_unseen = torch.utils.data.ConcatDataset([ds_house_valid[0], \n",
        "                                                  #ds_house_valid[1], \n",
        "                                                  #ds_house_valid[2], \n",
        "                                                  #ds_house_valid[3], \n",
        "                                                  ds_house_valid[4]\n",
        "                                                  ])\n",
        "dl_train_unseen = DataLoader(dataset = ds_train_unseen, batch_size = BATCH_SIZE, shuffle=True)\n",
        "dl_valid_unseen = DataLoader(dataset = ds_valid_unseen, batch_size = BATCH_SIZE, shuffle=False)\n",
        "dl_test_unseen = DataLoader(dataset = ds_house_total[1], batch_size = BATCH_SIZE, shuffle=False)\n",
        "\n",
        "dl_house_test = [DataLoader(dataset = ds_house_test[i], batch_size = 1, shuffle=False) for i in range(5)]\n",
        "dl_house_valid = [DataLoader(dataset = ds_house_valid[i], batch_size = 1, shuffle=False) for i in range(5)]\n",
        "dl_house_total = [DataLoader(dataset = ds_house_total[i], batch_size = 1, shuffle=False) for i in range(5)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QC-IwqzoL2D5",
        "colab": {}
      },
      "source": [
        "dataiter = iter(dl_house_test[1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "rB6NlwrYYE3D",
        "outputId": "1a9d8d38-b2b9-45cd-f1fd-0b0604f464a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        }
      },
      "source": [
        "plt.figure(figsize=(15,8))\n",
        "#x, y, s = dataiter.next()\n",
        "a = 1\n",
        "for i in range(100):\n",
        "    x, y, s = dataiter.next()\n",
        "    if y[0,:,a].sum() > 0:\n",
        "        break\n",
        "    if s[0,:,a].sum() > 0:\n",
        "        break\n",
        "plt.plot(np.arange(-BORDER, SEQ_LEN + BORDER), x[0,:].detach().numpy(), 'k-')\n",
        "plt.plot(y[0,:,a].detach().numpy())\n",
        "plt.plot(s[0,:,a].detach().numpy())\n",
        "plt.ylim([-0.5,1.5])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.5, 1.5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vZk1m1iTp+PxtwA6l3JAXyWQmJbYS8CTp+eef14EHHlisCG6NmemjH/2ohg/PVfumTp2q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\nY8aM0ZgxY/SBD3xAK1eu1Ne//nX98Y9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RbRwezc3NWrBggVasWKFQKKSCggLl5OQoEoko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\nIQ8AAAAAAoSQBwAAAAABQsgDAAAAgAAh5AEAAABAgGQU8sx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            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zaxR7GIAnffI",
        "outputId": "5951ba49-d959-4cc3-d8af-edf6aea88a55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "batch_size = BATCH_SIZE\n",
        "n_epochs = 300\n",
        "\n",
        "train_loader = dl_train_seen\n",
        "valid_loader = dl_valid_seen\n",
        "test_loader = dl_test_seen\n",
        "\n",
        "#i = 0\n",
        "for i in range(20):\n",
        "    print('TRAINING MODEL %d' %i)\n",
        "    # Instantiate the model\n",
        "    model = PTPNet(1,3,32).cuda()\n",
        "    optimizer = optim.Adam(model.parameters(), lr=1.E-4)\n",
        "    criterion = nn.BCEWithLogitsLoss()\n",
        "    fn = 'UKDALE_seen_%d.pth' %i\n",
        "    model, train_loss, valid_loss, test_loss = train_model(model, batch_size, n_epochs, fn)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TRAINING MODEL 0\n",
            "[  1/300] train_loss: 0.58662 valid_loss: 0.50422 test_loss: 0.50825 \n",
            "Validation loss decreased (inf --> 0.504216).  Saving model ...\n",
            "[  2/300] train_loss: 0.41906 valid_loss: 0.38410 test_loss: 0.39097 \n",
            "Validation loss decreased (0.504216 --> 0.384097).  Saving model ...\n",
            "[  3/300] train_loss: 0.33519 valid_loss: 0.32905 test_loss: 0.33933 \n",
            "Validation loss decreased (0.384097 --> 0.329049).  Saving model ...\n",
            "[  4/300] train_loss: 0.28827 valid_loss: 0.29103 test_loss: 0.30620 \n",
            "Validation loss decreased (0.329049 --> 0.291033).  Saving model ...\n",
            "[  5/300] train_loss: 0.25685 valid_loss: 0.25705 test_loss: 0.27648 \n",
            "Validation loss decreased (0.291033 --> 0.257051).  Saving model ...\n",
            "[  6/300] train_loss: 0.23341 valid_loss: 0.23138 test_loss: 0.24990 \n",
            "Validation loss decreased (0.257051 --> 0.231382).  Saving model ...\n",
            "[  7/300] train_loss: 0.20863 valid_loss: 0.21102 test_loss: 0.22715 \n",
            "Validation loss decreased (0.231382 --> 0.211021).  Saving model ...\n",
            "[  8/300] train_loss: 0.19058 valid_loss: 0.19807 test_loss: 0.21330 \n",
            "Validation loss decreased (0.211021 --> 0.198066).  Saving model ...\n",
            "[  9/300] train_loss: 0.18391 valid_loss: 0.18420 test_loss: 0.20043 \n",
            "Validation loss decreased (0.198066 --> 0.184200).  Saving model ...\n",
            "[ 10/300] train_loss: 0.17234 valid_loss: 0.17930 test_loss: 0.19197 \n",
            "Validation loss decreased (0.184200 --> 0.179297).  Saving model ...\n",
            "[ 11/300] train_loss: 0.16542 valid_loss: 0.17029 test_loss: 0.18321 \n",
            "Validation loss decreased (0.179297 --> 0.170289).  Saving model ...\n",
            "[ 12/300] train_loss: 0.15733 valid_loss: 0.16452 test_loss: 0.17885 \n",
            "Validation loss decreased (0.170289 --> 0.164522).  Saving model ...\n",
            "[ 13/300] train_loss: 0.15105 valid_loss: 0.15863 test_loss: 0.17275 \n",
            "Validation loss decreased (0.164522 --> 0.158635).  Saving model ...\n",
            "[ 14/300] train_loss: 0.14810 valid_loss: 0.15509 test_loss: 0.16830 \n",
            "Validation loss decreased (0.158635 --> 0.155093).  Saving model ...\n",
            "[ 15/300] train_loss: 0.14683 valid_loss: 0.14944 test_loss: 0.16396 \n",
            "Validation loss decreased (0.155093 --> 0.149435).  Saving model ...\n",
            "[ 16/300] train_loss: 0.14086 valid_loss: 0.14902 test_loss: 0.16289 \n",
            "Validation loss decreased (0.149435 --> 0.149016).  Saving model ...\n",
            "[ 17/300] train_loss: 0.13785 valid_loss: 0.14539 test_loss: 0.16029 \n",
            "Validation loss decreased (0.149016 --> 0.145385).  Saving model ...\n",
            "[ 18/300] train_loss: 0.13558 valid_loss: 0.14430 test_loss: 0.15787 \n",
            "Validation loss decreased (0.145385 --> 0.144301).  Saving model ...\n",
            "[ 19/300] train_loss: 0.13611 valid_loss: 0.14092 test_loss: 0.15535 \n",
            "Validation loss decreased (0.144301 --> 0.140922).  Saving model ...\n",
            "[ 20/300] train_loss: 0.13583 valid_loss: 0.13740 test_loss: 0.15306 \n",
            "Validation loss decreased (0.140922 --> 0.137395).  Saving model ...\n",
            "[ 21/300] train_loss: 0.13086 valid_loss: 0.13756 test_loss: 0.15222 \n",
            "[ 22/300] train_loss: 0.12549 valid_loss: 0.13634 test_loss: 0.15202 \n",
            "Validation loss decreased (0.137395 --> 0.136339).  Saving model ...\n",
            "[ 23/300] train_loss: 0.12839 valid_loss: 0.13646 test_loss: 0.15032 \n",
            "[ 24/300] train_loss: 0.12490 valid_loss: 0.13644 test_loss: 0.15115 \n",
            "[ 25/300] train_loss: 0.12432 valid_loss: 0.13315 test_loss: 0.14808 \n",
            "Validation loss decreased (0.136339 --> 0.133151).  Saving model ...\n",
            "[ 26/300] train_loss: 0.12384 valid_loss: 0.13099 test_loss: 0.14589 \n",
            "Validation loss decreased (0.133151 --> 0.130989).  Saving model ...\n",
            "[ 27/300] train_loss: 0.11972 valid_loss: 0.12924 test_loss: 0.14673 \n",
            "Validation loss decreased (0.130989 --> 0.129241).  Saving model ...\n",
            "[ 28/300] train_loss: 0.11986 valid_loss: 0.12983 test_loss: 0.14466 \n",
            "[ 29/300] train_loss: 0.12058 valid_loss: 0.12981 test_loss: 0.14366 \n",
            "[ 30/300] train_loss: 0.12081 valid_loss: 0.12737 test_loss: 0.14338 \n",
            "Validation loss decreased (0.129241 --> 0.127365).  Saving model ...\n",
            "[ 31/300] train_loss: 0.11942 valid_loss: 0.12455 test_loss: 0.14273 \n",
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            "TRAINING MODEL 2\n",
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            "TRAINING MODEL 4\n",
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            "TRAINING MODEL 9\n",
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            "TRAINING MODEL 10\n",
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            "TRAINING MODEL 11\n",
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            "TRAINING MODEL 12\n",
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            "Validation loss decreased (0.088603 --> 0.088360).  Saving model ...\n",
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            "Validation loss decreased (0.088360 --> 0.087344).  Saving model ...\n",
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            "Validation loss decreased (0.086207 --> 0.085813).  Saving model ...\n",
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            "Validation loss decreased (0.083973 --> 0.083257).  Saving model ...\n",
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            "Validation loss decreased (0.083257 --> 0.082323).  Saving model ...\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[236/300] train_loss: 0.07167 valid_loss: 0.08437 test_loss: 0.09478 \n",
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            "Validation loss decreased (0.082323 --> 0.082070).  Saving model ...\n",
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            "Validation loss decreased (0.082070 --> 0.081656).  Saving model ...\n",
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            "Validation loss decreased (0.081656 --> 0.080927).  Saving model ...\n",
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            "[300/300] train_loss: 0.06860 valid_loss: 0.08018 test_loss: 0.09199 \n",
            "TRAINING MODEL 13\n",
            "[  1/300] train_loss: 0.62252 valid_loss: 0.52169 test_loss: 0.51562 \n",
            "Validation loss decreased (inf --> 0.521686).  Saving model ...\n",
            "[  2/300] train_loss: 0.43676 valid_loss: 0.39454 test_loss: 0.39140 \n",
            "Validation loss decreased (0.521686 --> 0.394539).  Saving model ...\n",
            "[  3/300] train_loss: 0.34527 valid_loss: 0.34208 test_loss: 0.34299 \n",
            "Validation loss decreased (0.394539 --> 0.342082).  Saving model ...\n",
            "[  4/300] train_loss: 0.30178 valid_loss: 0.30410 test_loss: 0.31224 \n",
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            "[  5/300] train_loss: 0.26793 valid_loss: 0.27145 test_loss: 0.28097 \n",
            "Validation loss decreased (0.304104 --> 0.271452).  Saving model ...\n",
            "[  6/300] train_loss: 0.23892 valid_loss: 0.23868 test_loss: 0.25235 \n",
            "Validation loss decreased (0.271452 --> 0.238680).  Saving model ...\n",
            "[  7/300] train_loss: 0.21859 valid_loss: 0.21645 test_loss: 0.22929 \n",
            "Validation loss decreased (0.238680 --> 0.216447).  Saving model ...\n",
            "[  8/300] train_loss: 0.19748 valid_loss: 0.20219 test_loss: 0.21496 \n",
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            "[  9/300] train_loss: 0.18533 valid_loss: 0.19074 test_loss: 0.20094 \n",
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            "[ 10/300] train_loss: 0.17516 valid_loss: 0.18288 test_loss: 0.19255 \n",
            "Validation loss decreased (0.190744 --> 0.182876).  Saving model ...\n",
            "[ 11/300] train_loss: 0.16541 valid_loss: 0.17210 test_loss: 0.18219 \n",
            "Validation loss decreased (0.182876 --> 0.172100).  Saving model ...\n",
            "[ 12/300] train_loss: 0.16270 valid_loss: 0.16601 test_loss: 0.17618 \n",
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            "[ 13/300] train_loss: 0.15505 valid_loss: 0.16220 test_loss: 0.17257 \n",
            "Validation loss decreased (0.166013 --> 0.162203).  Saving model ...\n",
            "[ 14/300] train_loss: 0.15210 valid_loss: 0.15517 test_loss: 0.16751 \n",
            "Validation loss decreased (0.162203 --> 0.155168).  Saving model ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Sh-_NRsnPkdC",
        "colab": {}
      },
      "source": [
        "plt.plot(train_loss)\n",
        "plt.plot(valid_loss)\n",
        "plt.plot(test_loss)\n",
        "\n",
        "plt.yscale('log')\n",
        "plt.grid(True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LzBMEXOuEDrj",
        "colab": {}
      },
      "source": [
        "batch_size = BATCH_SIZE\n",
        "n_epochs = 100\n",
        "\n",
        "train_loader = dl_train_unseen\n",
        "valid_loader = dl_valid_unseen\n",
        "test_loader = dl_test_unseen\n",
        "\n",
        "#i = 0\n",
        "for i in range(20):\n",
        "    print('TRAINING MODEL %d' %i)\n",
        "    # Instantiate the model\n",
        "    model = PTPNet(1,3,32).cuda()\n",
        "    optimizer = optim.Adam(model.parameters(), lr=1.E-4)\n",
        "    criterion = nn.BCEWithLogitsLoss()\n",
        "    fn = 'UKDALE_unseen_%d.pth' %i\n",
        "    model, train_loss, valid_loss, test_loss = train_model(model, batch_size, n_epochs, fn)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qYrzbsFJEThv",
        "colab": {}
      },
      "source": [
        "plt.plot(train_loss)\n",
        "plt.plot(valid_loss)\n",
        "plt.plot(test_loss)\n",
        "\n",
        "plt.yscale('log')\n",
        "plt.grid(True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "MD1z2vUwyZIH",
        "outputId": "42c5af30-c246-4805-8ce7-2a7fbe79176c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        }
      },
      "source": [
        "model = PTPNet(1,3,32).cuda()\n",
        "model.eval()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PTPNet(\n",
              "  (encoder1): Encoder(\n",
              "    (conv): Conv1d(1, 32, kernel_size=(3,), stride=(1,), bias=False)\n",
              "    (bn): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (pool1): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (encoder2): Encoder(\n",
              "    (conv): Conv1d(32, 64, kernel_size=(3,), stride=(1,), bias=False)\n",
              "    (bn): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (pool2): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (encoder3): Encoder(\n",
              "    (conv): Conv1d(64, 128, kernel_size=(3,), stride=(1,), bias=False)\n",
              "    (bn): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (pool3): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (encoder4): Encoder(\n",
              "    (conv): Conv1d(128, 256, kernel_size=(3,), stride=(1,), bias=False)\n",
              "    (bn): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (tpool1): TemporalPooling(\n",
              "    (pool): AvgPool1d(kernel_size=(5,), stride=(5,), padding=(0,))\n",
              "    (conv): Conv1d(256, 64, kernel_size=(1,), stride=(1,))\n",
              "    (bn): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (tpool2): TemporalPooling(\n",
              "    (pool): AvgPool1d(kernel_size=(10,), stride=(10,), padding=(0,))\n",
              "    (conv): Conv1d(256, 64, kernel_size=(1,), stride=(1,))\n",
              "    (bn): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (tpool3): TemporalPooling(\n",
              "    (pool): AvgPool1d(kernel_size=(20,), stride=(20,), padding=(0,))\n",
              "    (conv): Conv1d(256, 64, kernel_size=(1,), stride=(1,))\n",
              "    (bn): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (tpool4): TemporalPooling(\n",
              "    (pool): AvgPool1d(kernel_size=(30,), stride=(30,), padding=(0,))\n",
              "    (conv): Conv1d(256, 64, kernel_size=(1,), stride=(1,))\n",
              "    (bn): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (decoder): Decoder(\n",
              "    (conv): ConvTranspose1d(512, 32, kernel_size=(8,), stride=(8,), bias=False)\n",
              "    (bn): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (activation): Conv1d(32, 3, kernel_size=(1,), stride=(1,))\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "HuXotkW1yd-b",
        "outputId": "49ef336e-dab6-470f-ab7c-7ed83a4c4162",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 816
        }
      },
      "source": [
        "scores = {}\n",
        "for a in range(3):\n",
        "    scores[a] = {}\n",
        "    scores[a]['F1'] = []\n",
        "    scores[a]['Precision'] = []\n",
        "    scores[a]['Recall'] = []\n",
        "    scores[a]['Accuracy'] = []\n",
        "    scores[a]['MCC'] = []\n",
        "    scores[a]['MAE'] = []\n",
        "    scores[a]['SAE'] = []\n",
        "\n",
        "thr = 0.5\n",
        "for i in range(20):\n",
        "    #filename = '/content/gdrive/My Drive/NILM/UKDALE_seen_%d.pth' %i\n",
        "    filename = './UKDALE_seen_%d.pth' %i\n",
        "    print(filename)\n",
        "    model.load_state_dict(torch.load(filename))\n",
        "    for a in range(3):\n",
        "        #x_true, p_true, s_true, s_hat = evaluate_activation(model, dl_house_total[0], a)\n",
        "        #pm = p_true.sum() / s_true.sum()\n",
        "        #pm = (ds_appliance[0][APPLIANCE[a]] * \n",
        "        #      ds_status[0][APPLIANCE[a]]).sum() / ds_status[0][APPLIANCE[a]].sum() / MAX_POWER\n",
        "        pm = ds_appliance[0][APPLIANCE[a]].sum() / ds_status[0][APPLIANCE[a]].sum() / MAX_POWER\n",
        "        x_true, p_true, s_true, s_hat = evaluate_activation(model, dl_house_test[0], a)\n",
        "        s_hat = get_status(s_hat, thr, MIN_OFF[a], MIN_ON[a])\n",
        "        p_hat = pm * s_hat\n",
        "        scores[a]['F1'].append(f1_score(s_true, s_hat))\n",
        "        scores[a]['Precision'].append(precision_score(s_true, s_hat))\n",
        "        scores[a]['Recall'].append(recall_score(s_true, s_hat))\n",
        "        scores[a]['Accuracy'].append(accuracy_score(s_true, s_hat))\n",
        "        scores[a]['MCC'].append(matthews_corrcoef(s_true, s_hat))\n",
        "        scores[a]['MAE'].append(mean_absolute_error(p_true, p_hat)*MAX_POWER)\n",
        "        scores[a]['SAE'].append((p_hat.sum() - p_true.sum()) / p_true.sum())\n",
        "\n",
        "for i,a in enumerate(APPLIANCE):\n",
        "    print()\n",
        "    print(a)\n",
        "    print('F1 score  : %.3f (%.3f, %.3f)' %(np.mean(scores[i]['F1']), sorted(scores[i]['F1'])[1], sorted(scores[i]['F1'])[18]))\n",
        "    print('Precision : %.3f (%.3f, %.3f)' %(np.mean(scores[i]['Precision']), sorted(scores[i]['Precision'])[1], sorted(scores[i]['Precision'])[18]))\n",
        "    print('Recall    : %.3f (%.3f, %.3f)' %(np.mean(scores[i]['Recall']), sorted(scores[i]['Recall'])[1], sorted(scores[i]['Recall'])[18]))\n",
        "    print('Accuracy  : %.3f (%.3f, %.3f)' %(np.mean(scores[i]['Accuracy']), sorted(scores[i]['Accuracy'])[1], sorted(scores[i]['Accuracy'])[18]))\n",
        "    print('MCC       : %.3f (%.3f, %.3f)' %(np.mean(scores[i]['MCC']), sorted(scores[i]['MCC'])[1], sorted(scores[i]['MCC'])[18]))\n",
        "    print('MAE       : %.2f (%.2f, %.2f)' %(np.mean(scores[i]['MAE']), sorted(scores[i]['MAE'])[1], sorted(scores[i]['MAE'])[18]))\n",
        "    print('SAE       : %.3f (%.3f, %.3f)' %(np.mean(scores[i]['SAE']), sorted(scores[i]['SAE'])[1], sorted(scores[i]['SAE'])[18]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/NILM/UKDALE_seen_0.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_1.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_2.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_3.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_4.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_5.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_6.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_7.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_8.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_9.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_10.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_11.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_12.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_13.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_14.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_15.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_16.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_17.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_18.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_seen_19.pth\n",
            "\n",
            "fridge\n",
            "F1 score  : 0.867 (0.864, 0.870)\n",
            "Precision : 0.875 (0.867, 0.885)\n",
            "Recall    : 0.859 (0.846, 0.871)\n",
            "Accuracy  : 0.880 (0.878, 0.882)\n",
            "MCC       : 0.759 (0.755, 0.762)\n",
            "MAE       : 15.25 (15.08, 15.47)\n",
            "SAE       : -0.020 (-0.046, 0.002)\n",
            "\n",
            "dish_washer\n",
            "F1 score  : 0.930 (0.905, 0.946)\n",
            "Precision : 0.942 (0.904, 0.966)\n",
            "Recall    : 0.919 (0.890, 0.942)\n",
            "Accuracy  : 0.997 (0.995, 0.997)\n",
            "MCC       : 0.928 (0.903, 0.945)\n",
            "MAE       : 20.41 (19.99, 21.00)\n",
            "SAE       : -0.042 (-0.082, -0.005)\n",
            "\n",
            "washing_machine\n",
            "F1 score  : 0.978 (0.976, 0.980)\n",
            "Precision : 0.975 (0.968, 0.979)\n",
            "Recall    : 0.982 (0.977, 0.987)\n",
            "Accuracy  : 0.997 (0.996, 0.997)\n",
            "MCC       : 0.977 (0.974, 0.979)\n",
            "MAE       : 41.97 (41.80, 42.21)\n",
            "SAE       : -0.077 (-0.085, -0.066)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oYjnWxnQyURS",
        "outputId": "1f4f491b-4271-426e-ee56-c739248a2a9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 816
        }
      },
      "source": [
        "scores = {}\n",
        "for a in range(3):\n",
        "    scores[a] = {}\n",
        "    scores[a]['F1'] = []\n",
        "    scores[a]['Precision'] = []\n",
        "    scores[a]['Recall'] = []\n",
        "    scores[a]['Accuracy'] = []\n",
        "    scores[a]['MCC'] = []\n",
        "    scores[a]['MAE'] = []\n",
        "    scores[a]['SAE'] = []\n",
        "\n",
        "thr = 0.5\n",
        "\n",
        "for i in range(20):\n",
        "    #filename = '/content/gdrive/My Drive/NILM/UKDALE_unseen_%d.pth' %i\n",
        "    filename = './UKDALE_unseen_%d.pth' %i\n",
        "    print(filename)\n",
        "    model.load_state_dict(torch.load(filename))\n",
        "    for a in range(3):\n",
        "        #x_true, p_true, s_true, s_hat = evaluate_activation(model, dl_house_total[1], a)\n",
        "        #pm = p_true.sum() / s_true.sum()\n",
        "        pm = ds_appliance[1][APPLIANCE[a]].sum() / ds_status[1][APPLIANCE[a]].sum() / MAX_POWER\n",
        "        x_true, p_true, s_true, s_hat = evaluate_activation(model, dl_house_total[1], a)\n",
        "        s_hat = get_status(s_hat, thr, MIN_OFF[a], MIN_ON[a])\n",
        "        p_hat = pm * s_hat\n",
        "        scores[a]['F1'].append(f1_score(s_true, s_hat))\n",
        "        scores[a]['Precision'].append(precision_score(s_true, s_hat))\n",
        "        scores[a]['Recall'].append(recall_score(s_true, s_hat))\n",
        "        scores[a]['Accuracy'].append(accuracy_score(s_true, s_hat))\n",
        "        scores[a]['MCC'].append(matthews_corrcoef(s_true, s_hat))\n",
        "        scores[a]['MAE'].append(mean_absolute_error(p_true, p_hat)*MAX_POWER)\n",
        "        scores[a]['SAE'].append((p_hat.sum() - p_true.sum()) / p_true.sum())\n",
        "\n",
        "for i,a in enumerate(APPLIANCE):\n",
        "    print()\n",
        "    print(a)\n",
        "    print('F1 score  : %.3f (%.3f, %.3f)' %(np.mean(scores[i]['F1']), sorted(scores[i]['F1'])[1], sorted(scores[i]['F1'])[18]))\n",
        "    print('Precision : %.3f (%.3f, %.3f)' %(np.mean(scores[i]['Precision']), sorted(scores[i]['Precision'])[1], sorted(scores[i]['Precision'])[18]))\n",
        "    print('Recall    : %.3f (%.3f, %.3f)' %(np.mean(scores[i]['Recall']), sorted(scores[i]['Recall'])[1], sorted(scores[i]['Recall'])[18]))\n",
        "    print('Accuracy  : %.3f (%.3f, %.3f)' %(np.mean(scores[i]['Accuracy']), sorted(scores[i]['Accuracy'])[1], sorted(scores[i]['Accuracy'])[18]))\n",
        "    print('MCC       : %.3f (%.3f, %.3f)' %(np.mean(scores[i]['MCC']), sorted(scores[i]['MCC'])[1], sorted(scores[i]['MCC'])[18]))\n",
        "    print('MAE       : %.2f (%.2f, %.2f)' %(np.mean(scores[i]['MAE']), sorted(scores[i]['MAE'])[1], sorted(scores[i]['MAE'])[18]))\n",
        "    print('SAE       : %.3f (%.3f, %.3f)' %(np.mean(scores[i]['SAE']), sorted(scores[i]['SAE'])[1], sorted(scores[i]['SAE'])[18]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_0.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_1.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_2.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_3.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_4.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_5.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_6.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_7.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_8.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_9.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_10.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_11.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_12.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_13.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_14.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_15.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_16.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_17.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_18.pth\n",
            "/content/gdrive/My Drive/NILM/UKDALE_unseen_19.pth\n",
            "\n",
            "fridge\n",
            "F1 score  : 0.871 (0.863, 0.876)\n",
            "Precision : 0.892 (0.883, 0.898)\n",
            "Recall    : 0.851 (0.841, 0.861)\n",
            "Accuracy  : 0.905 (0.900, 0.908)\n",
            "MCC       : 0.796 (0.786, 0.803)\n",
            "MAE       : 17.03 (16.82, 17.24)\n",
            "SAE       : -0.046 (-0.066, -0.025)\n",
            "\n",
            "dish_washer\n",
            "F1 score  : 0.809 (0.790, 0.822)\n",
            "Precision : 0.788 (0.738, 0.826)\n",
            "Recall    : 0.835 (0.768, 0.897)\n",
            "Accuracy  : 0.989 (0.987, 0.990)\n",
            "MCC       : 0.805 (0.784, 0.817)\n",
            "MAE       : 33.07 (31.19, 35.68)\n",
            "SAE       : 0.063 (-0.054, 0.219)\n",
            "\n",
            "washing_machine\n",
            "F1 score  : 0.863 (0.835, 0.900)\n",
            "Precision : 0.858 (0.811, 0.893)\n",
            "Recall    : 0.869 (0.827, 0.918)\n",
            "Accuracy  : 0.997 (0.996, 0.998)\n",
            "MCC       : 0.862 (0.834, 0.899)\n",
            "MAE       : 8.31 (7.88, 8.70)\n",
            "SAE       : 0.014 (-0.059, 0.115)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqLXkeMG3kVJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}