{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "h5UHRu2eZIi8"
},
"source": [
"# Example of Computational Experiments"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "5LAIO9zHZIjC"
},
"source": [
"## For running on Colab"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 298
},
"colab_type": "code",
"id": "8Anf4MqUZIjE",
"outputId": "1e5f3880-ecd0-47a0-f91b-16717796eddf"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting git+https://github.com/PYFTS/pyFTS\n",
" Cloning https://github.com/PYFTS/pyFTS to /tmp/pip-req-build-lukq3tuz\n",
" Running command git clone -q https://github.com/PYFTS/pyFTS /tmp/pip-req-build-lukq3tuz\n",
"Building wheels for collected packages: pyFTS\n",
" Building wheel for pyFTS (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for pyFTS: filename=pyFTS-1.6-cp36-none-any.whl size=197025 sha256=448e215310ef7105063c398960ff3cbb516aea5106191a318d861fb67cfc5ae7\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-iklubsrn/wheels/e7/32/a9/230470113df5a73242a5a6d05671cb646db97abf14bbce2644\n",
"Successfully built pyFTS\n",
"Installing collected packages: pyFTS\n",
"Successfully installed pyFTS-1.6\n",
"Cloning into 'stac'...\n",
"remote: Enumerating objects: 2238, done.\u001b[K\n",
"remote: Total 2238 (delta 0), reused 0 (delta 0), pack-reused 2238\u001b[K\n",
"Receiving objects: 100% (2238/2238), 23.62 MiB | 21.66 MiB/s, done.\n",
"Resolving deltas: 100% (1147/1147), done.\n"
]
}
],
"source": [
"!pip3 install -U git+https://github.com/PYFTS/pyFTS\n",
"!git clone https://github.com/petroniocandido/stac"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "aIBZ6V8HZIjP"
},
"source": [
"## Common Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "vV4og7hyZIjQ",
"outputId": "0d893e55-cb7d-46c4-d492-92fb96cc08c4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pylab as plt\n",
"import seaborn as sns\n",
"\n",
"%pylab inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "9YxyCmFCZIjY"
},
"source": [
"## Common data transformations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "EFfUy0e4ZIjb"
},
"outputs": [],
"source": [
"from pyFTS.common import Transformations\n",
"\n",
"tdiff = Transformations.Differential(1)\n",
"\n",
"boxcox = Transformations.BoxCox(0) # Нормализация"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ovtP6DQvZIjf"
},
"source": [
"## Import Datasets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "n47EMCtUZIjh"
},
"outputs": [],
"source": [
"from pyFTS.data import TAIEX, NASDAQ, SP500\n",
"\n",
"dataset_names = [\"TAIEX\", \"SP500\",\"NASDAQ\"]\n",
"\n",
"def get_dataset(name):\n",
" if dataset_name == \"TAIEX\":\n",
" return TAIEX.get_data()\n",
" elif dataset_name == \"SP500\":\n",
" return SP500.get_data()[11500:16000]\n",
" elif dataset_name == \"NASDAQ\":\n",
" return NASDAQ.get_data()\n",
"\n",
"\n",
"train_split = 2000\n",
"test_length = 200"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "P6jeDcM7ZIjn",
"outputId": "0fadab98-9820-48d3-9591-7b2302c0fe28"
},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(nrows=3, ncols=3, figsize=[15,7])\n",
"\n",
"for count,dataset_name in enumerate(dataset_names):\n",
" dataset = get_dataset(dataset_name)\n",
" dataset_diff = tdiff.apply(dataset)\n",
" dataset_boxcox = boxcox.apply(dataset)\n",
"\n",
" ax[0][count].plot(dataset)\n",
" ax[1][count].plot(dataset_diff)\n",
" ax[2][count].plot(dataset_boxcox)\n",
" ax[0][count].set_title(dataset_name)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "XXnYaj6cZIjt"
},
"source": [
"## Пакетные вычислительные эксперименты с sliding_window_benchmarks\n",
"\n",
"**benchmarks.sliding_window_benchmarks** — это метод, который выполняет пакетные эксперименты со скользящим окном для нескольких моделей, секционаторов, количества секций, преобразований и т. д. и сохраняет результаты в базе данных Sqlite3 для последующего анализа.\n",
"\n",
"Имя файловой базы данных по умолчанию — «benchmarks.db», и оно содержит таблицу с именем «benchmarks» со следующей схемой:\n",
"\n",
" - ID: целочисленный инкрементный первичный ключ.\n",
" - Дата: дата/час выполнения теста.\n",
" - Набор данных: укажите, на каком наборе данных был выполнен набор данных.\n",
" - Тег: определяемое пользователем слово, обозначающее набор тестов. \n",
" - Тип: тип прогнозирования (точечный, интервальный, распределение).\n",
" - Модель: модель ФТС\n",
" - Преобразование: имя преобразования данных, если оно использовалось.\n",
" - Порядок: порядок метода FTS. \n",
" - Схема: схема разбиения UoD\n",
" - Разделы: количество разделов\n",
" - Размер: количество правил модели FTS. \n",
" - Шаги: горизонт прогнозирования, т.е. е., количество шагов вперед\n",
" - Мера: мера точности\n",
" - Значение: значение меры.\n",
"\n",
"Знайте параметры Sliding_window_benchmarks:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "aa7Hk0q-ZIjw",
"outputId": "7b4213bf-b937-49f4-8185-f08ae72fb344"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function sliding_window_benchmarks in module pyFTS.benchmarks.benchmarks:\n",
"\n",
"sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs)\n",
" Sliding window benchmarks for FTS forecasters.\n",
" \n",
" For each data window, a train and test datasets will be splitted. For each train split, number of\n",
" partitions and partitioning method will be created a partitioner model. And for each partitioner, order,\n",
" steps ahead and FTS method a foreasting model will be trained.\n",
" \n",
" Then all trained models are benchmarked on the test data and the metrics are stored on a sqlite3 database\n",
" (identified by the 'file' parameter) for posterior analysis.\n",
" \n",
" All these process can be distributed on a dispy cluster, setting the atributed 'distributed' to true and\n",
" informing the list of dispy nodes on 'nodes' parameter.\n",
" \n",
" The number of experiments is determined by 'windowsize' and 'inc' parameters.\n",
" \n",
" :param data: test data\n",
" :param windowsize: size of sliding window\n",
" :param train: percentual of sliding window data used to train the models\n",
" :param kwargs: dict, optional arguments\n",
" \n",
" :keyword\n",
" benchmark_methods: a list with Non FTS models to benchmark. The default is None.\n",
" benchmark_methods_parameters: a list with Non FTS models parameters. The default is None.\n",
" dataset: the dataset name to identify the current set of benchmarks results on database.\n",
" distributed: A boolean value indicating if the forecasting procedure will be distributed in a dispy cluster. . The default is False\n",
" file: file path to save the results. The default is benchmarks.db.\n",
" inc: a float on interval [0,1] indicating the percentage of the windowsize to move the window\n",
" methods: a list with FTS class names. The default depends on the forecasting type and contains the list of all FTS methods.\n",
" models: a list with prebuilt FTS objects. The default is None.\n",
" nodes: a list with the dispy cluster nodes addresses. The default is [127.0.0.1].\n",
" orders: a list with orders of the models (for high order models). The default is [1,2,3].\n",
" partitions: a list with the numbers of partitions on the Universe of Discourse. The default is [10].\n",
" partitioners_models: a list with prebuilt Universe of Discourse partitioners objects. The default is None.\n",
" partitioners_methods: a list with Universe of Discourse partitioners class names. The default is [partitioners.Grid.GridPartitioner].\n",
" progress: If true a progress bar will be displayed during the benchmarks. The default is False.\n",
" start: in the multi step forecasting, the index of the data where to start forecasting. The default is 0.\n",
" steps_ahead: a list with the forecasting horizons, i. e., the number of steps ahead to forecast. The default is 1.\n",
" tag: a name to identify the current set of benchmarks results on database.\n",
" type: the forecasting type, one of these values: point(default), interval or distribution. The default is point.\n",
" transformations: a list with data transformations do apply . The default is [None].\n",
"\n"
]
}
],
"source": [
"from pyFTS.benchmarks import benchmarks as bchmk\n",
"help(bchmk.sliding_window_benchmarks)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "NiIEkgwTZIj2"
},
"source": [
"## Partitioning optimization by dataset\n",
"\n",
"**CAUTION**: This task is computationally expensive and take several hours to be performed. We strongly recommend to use the distributed version with a dispy cluster."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Xo7iJG4hZIj4",
"outputId": "55d8d711-137e-446b-be81-44962207520a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-05-14 16:40:47 pycos - version 4.6.5 with epoll I/O notifier\n",
"2018-05-14 16:40:47 dispy - dispy client version: 4.8.5\n",
"2018-05-14 16:40:47 dispy - Storing fault recovery information in \"_dispy_20180514164047\"\n",
"2018-05-14 16:40:48 dispy - Started HTTP server at ('0.0.0.0', 8181)\n",
"2018-05-14 16:40:48 dispy - dispy client at 127.0.0.1:51347\n",
"2018-05-14 16:40:48 dispy - Discovered 192.168.0.110:51348 (petronio-notebook) with 3 cpus\n",
"2018-05-14 16:40:48 dispy - Running job 140142391887816 on 192.168.0.110\n",
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"2018-05-14 16:40:51 dispy - Received reply for job / 140142391887816 from 192.168.0.110\n",
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"2018-05-14 16:41:00 dispy - Running job 140142379463688 on 192.168.0.110\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-05-14 16:41:01 dispy - Received reply for job / 140142379463688 from 192.168.0.110\n",
"2018-05-14 16:41:01 dispy - Running job 140142379464168 on 192.168.0.110\n",
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"2018-05-14 16:41:04 dispy - Running job 140142379464768 on 192.168.0.110\n",
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"2018-05-14 16:41:10 dispy - Running job 140142379465728 on 192.168.0.110\n",
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"2018-05-14 16:41:14 dispy - Running job 140142379466448 on 192.168.0.110\n",
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"2018-05-14 16:41:14 dispy - Running job 140142379466568 on 192.168.0.110\n",
"2018-05-14 16:41:14 dispy - Running job / 140142379466568 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:15 dispy - Received reply for job / 140142379466328 from 192.168.0.110\n",
"2018-05-14 16:41:15 dispy - Running job 140142377492552 on 192.168.0.110\n",
"2018-05-14 16:41:15 dispy - Running job / 140142377492552 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:15 dispy - Received reply for job / 140142379466448 from 192.168.0.110\n",
"2018-05-14 16:41:15 dispy - Running job 140142377492672 on 192.168.0.110\n",
"2018-05-14 16:41:15 dispy - Running job / 140142377492672 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:16 dispy - Received reply for job / 140142379466568 from 192.168.0.110\n",
"2018-05-14 16:41:16 dispy - Running job 140142377492792 on 192.168.0.110\n",
"2018-05-14 16:41:16 dispy - Running job / 140142377492792 on 192.168.0.110 (busy: 3 / 3)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-05-14 16:41:16 dispy - Received reply for job / 140142377492552 from 192.168.0.110\n",
"2018-05-14 16:41:16 dispy - Running job 140142377492912 on 192.168.0.110\n",
"2018-05-14 16:41:16 dispy - Running job / 140142377492912 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:17 dispy - Received reply for job / 140142377492672 from 192.168.0.110\n",
"2018-05-14 16:41:17 dispy - Running job 140142377493032 on 192.168.0.110\n",
"2018-05-14 16:41:17 dispy - Running job / 140142377493032 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:17 dispy - Received reply for job / 140142377492792 from 192.168.0.110\n",
"2018-05-14 16:41:17 dispy - Running job 140142377493152 on 192.168.0.110\n",
"2018-05-14 16:41:17 dispy - Running job / 140142377493152 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:17 dispy - Received reply for job / 140142377492912 from 192.168.0.110\n",
"2018-05-14 16:41:17 dispy - Running job 140142377493272 on 192.168.0.110\n",
"2018-05-14 16:41:17 dispy - Running job / 140142377493272 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:18 dispy - Received reply for job / 140142377493032 from 192.168.0.110\n",
"2018-05-14 16:41:18 dispy - Running job 140142377493392 on 192.168.0.110\n",
"2018-05-14 16:41:18 dispy - Running job / 140142377493392 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:18 dispy - Received reply for job / 140142377493152 from 192.168.0.110\n",
"2018-05-14 16:41:18 dispy - Running job 140142377493512 on 192.168.0.110\n",
"2018-05-14 16:41:18 dispy - Running job / 140142377493512 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:19 dispy - Received reply for job / 140142377493272 from 192.168.0.110\n",
"2018-05-14 16:41:19 dispy - Running job 140142377493632 on 192.168.0.110\n",
"2018-05-14 16:41:19 dispy - Running job / 140142377493632 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:19 dispy - Received reply for job / 140142377493392 from 192.168.0.110\n",
"2018-05-14 16:41:19 dispy - Running job 140142377493752 on 192.168.0.110\n",
"2018-05-14 16:41:19 dispy - Running job / 140142377493752 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:20 dispy - Received reply for job / 140142377493512 from 192.168.0.110\n",
"2018-05-14 16:41:20 dispy - Running job 140142377493872 on 192.168.0.110\n",
"2018-05-14 16:41:20 dispy - Running job / 140142377493872 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:20 dispy - Received reply for job / 140142377493632 from 192.168.0.110\n",
"2018-05-14 16:41:20 dispy - Running job 140142377493992 on 192.168.0.110\n",
"2018-05-14 16:41:20 dispy - Running job / 140142377493992 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:21 dispy - Received reply for job / 140142377493752 from 192.168.0.110\n",
"2018-05-14 16:41:21 dispy - Running job 140142377494112 on 192.168.0.110\n",
"2018-05-14 16:41:21 dispy - Running job / 140142377494112 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:21 dispy - Received reply for job / 140142377493872 from 192.168.0.110\n",
"2018-05-14 16:41:21 dispy - Running job 140142377494232 on 192.168.0.110\n",
"2018-05-14 16:41:21 dispy - Running job / 140142377494232 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:22 dispy - Received reply for job / 140142377493992 from 192.168.0.110\n",
"2018-05-14 16:41:22 dispy - Running job 140142377494352 on 192.168.0.110\n",
"2018-05-14 16:41:22 dispy - Running job / 140142377494352 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:22 dispy - Received reply for job / 140142377494112 from 192.168.0.110\n",
"2018-05-14 16:41:22 dispy - Running job 140142377494472 on 192.168.0.110\n",
"2018-05-14 16:41:22 dispy - Running job / 140142377494472 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:22 dispy - Received reply for job / 140142377494232 from 192.168.0.110\n",
"2018-05-14 16:41:22 dispy - Running job 140142377494592 on 192.168.0.110\n",
"2018-05-14 16:41:22 dispy - Running job / 140142377494592 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:24 dispy - Received reply for job / 140142377494472 from 192.168.0.110\n",
"2018-05-14 16:41:24 dispy - Running job 140142377494712 on 192.168.0.110\n",
"2018-05-14 16:41:24 dispy - Running job / 140142377494712 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:24 dispy - Received reply for job / 140142377494352 from 192.168.0.110\n",
"2018-05-14 16:41:24 dispy - Running job 140142377494832 on 192.168.0.110\n",
"2018-05-14 16:41:24 dispy - Running job / 140142377494832 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:24 dispy - Received reply for job / 140142377494592 from 192.168.0.110\n",
"2018-05-14 16:41:24 dispy - Running job 140142377494952 on 192.168.0.110\n",
"2018-05-14 16:41:24 dispy - Running job / 140142377494952 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:25 dispy - Received reply for job / 140142377494832 from 192.168.0.110\n",
"2018-05-14 16:41:25 dispy - Running job 140142377495072 on 192.168.0.110\n",
"2018-05-14 16:41:25 dispy - Running job / 140142377495072 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:25 dispy - Received reply for job / 140142377494712 from 192.168.0.110\n",
"2018-05-14 16:41:25 dispy - Running job 140142377495192 on 192.168.0.110\n",
"2018-05-14 16:41:25 dispy - Running job / 140142377495192 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:26 dispy - Received reply for job / 140142377494952 from 192.168.0.110\n",
"2018-05-14 16:41:26 dispy - Running job 140142377495312 on 192.168.0.110\n",
"2018-05-14 16:41:26 dispy - Running job / 140142377495312 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:27 dispy - Received reply for job / 140142377495072 from 192.168.0.110\n",
"2018-05-14 16:41:27 dispy - Running job 140142377495432 on 192.168.0.110\n",
"2018-05-14 16:41:27 dispy - Running job / 140142377495432 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:27 dispy - Received reply for job / 140142377495192 from 192.168.0.110\n",
"2018-05-14 16:41:27 dispy - Running job 140142377495552 on 192.168.0.110\n",
"2018-05-14 16:41:27 dispy - Running job / 140142377495552 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:27 dispy - Received reply for job / 140142377495312 from 192.168.0.110\n",
"2018-05-14 16:41:27 dispy - Running job 140142377495672 on 192.168.0.110\n",
"2018-05-14 16:41:27 dispy - Running job / 140142377495672 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:29 dispy - Received reply for job / 140142377495552 from 192.168.0.110\n",
"2018-05-14 16:41:29 dispy - Running job 140142377495792 on 192.168.0.110\n",
"2018-05-14 16:41:29 dispy - Running job / 140142377495792 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:29 dispy - Received reply for job / 140142377495432 from 192.168.0.110\n",
"2018-05-14 16:41:29 dispy - Running job 140142377495912 on 192.168.0.110\n",
"2018-05-14 16:41:29 dispy - Running job / 140142377495912 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:29 dispy - Received reply for job / 140142377495672 from 192.168.0.110\n",
"2018-05-14 16:41:29 dispy - Running job 140142377496032 on 192.168.0.110\n",
"2018-05-14 16:41:29 dispy - Running job / 140142377496032 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:31 dispy - Received reply for job / 140142377495792 from 192.168.0.110\n",
"2018-05-14 16:41:31 dispy - Running job 140142377496152 on 192.168.0.110\n",
"2018-05-14 16:41:31 dispy - Running job / 140142377496152 on 192.168.0.110 (busy: 3 / 3)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-05-14 16:41:31 dispy - Received reply for job / 140142377495912 from 192.168.0.110\n",
"2018-05-14 16:41:31 dispy - Running job 140142377496272 on 192.168.0.110\n",
"2018-05-14 16:41:31 dispy - Running job / 140142377496272 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:31 dispy - Received reply for job / 140142377496032 from 192.168.0.110\n",
"2018-05-14 16:41:31 dispy - Running job 140142377496392 on 192.168.0.110\n",
"2018-05-14 16:41:31 dispy - Running job / 140142377496392 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:32 dispy - Received reply for job / 140142377496152 from 192.168.0.110\n",
"2018-05-14 16:41:32 dispy - Running job 140142376026184 on 192.168.0.110\n",
"2018-05-14 16:41:32 dispy - Running job / 140142376026184 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:33 dispy - Received reply for job / 140142377496272 from 192.168.0.110\n",
"2018-05-14 16:41:33 dispy - Running job 140142376026304 on 192.168.0.110\n",
"2018-05-14 16:41:33 dispy - Running job / 140142376026304 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:34 dispy - Received reply for job / 140142377496392 from 192.168.0.110\n",
"2018-05-14 16:41:34 dispy - Running job 140142376026424 on 192.168.0.110\n",
"2018-05-14 16:41:34 dispy - Running job / 140142376026424 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:34 dispy - Received reply for job / 140142376026184 from 192.168.0.110\n",
"2018-05-14 16:41:34 dispy - Running job 140142376026544 on 192.168.0.110\n",
"2018-05-14 16:41:34 dispy - Running job / 140142376026544 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:35 dispy - Received reply for job / 140142376026304 from 192.168.0.110\n",
"2018-05-14 16:41:35 dispy - Running job 140142376026664 on 192.168.0.110\n",
"2018-05-14 16:41:35 dispy - Running job / 140142376026664 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:35 dispy - Received reply for job / 140142376026424 from 192.168.0.110\n",
"2018-05-14 16:41:35 dispy - Running job 140142376026784 on 192.168.0.110\n",
"2018-05-14 16:41:35 dispy - Running job / 140142376026784 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:35 dispy - Received reply for job / 140142376026544 from 192.168.0.110\n",
"2018-05-14 16:41:35 dispy - Running job 140142376026904 on 192.168.0.110\n",
"2018-05-14 16:41:36 dispy - Running job / 140142376026904 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:36 dispy - Received reply for job / 140142376026664 from 192.168.0.110\n",
"2018-05-14 16:41:36 dispy - Running job 140142376027024 on 192.168.0.110\n",
"2018-05-14 16:41:36 dispy - Running job / 140142376027024 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:36 dispy - Received reply for job / 140142376026784 from 192.168.0.110\n",
"2018-05-14 16:41:36 dispy - Running job 140142376027144 on 192.168.0.110\n",
"2018-05-14 16:41:36 dispy - Running job / 140142376027144 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:37 dispy - Received reply for job / 140142376026904 from 192.168.0.110\n",
"2018-05-14 16:41:37 dispy - Running job 140142376027264 on 192.168.0.110\n",
"2018-05-14 16:41:37 dispy - Running job / 140142376027264 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:37 dispy - Received reply for job / 140142376027144 from 192.168.0.110\n",
"2018-05-14 16:41:37 dispy - Running job 140142376027384 on 192.168.0.110\n",
"2018-05-14 16:41:37 dispy - Running job / 140142376027384 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:37 dispy - Received reply for job / 140142376027024 from 192.168.0.110\n",
"2018-05-14 16:41:37 dispy - Running job 140142376027504 on 192.168.0.110\n",
"2018-05-14 16:41:37 dispy - Running job / 140142376027504 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:38 dispy - Received reply for job / 140142376027264 from 192.168.0.110\n",
"2018-05-14 16:41:38 dispy - Running job 140142376027624 on 192.168.0.110\n",
"2018-05-14 16:41:38 dispy - Running job / 140142376027624 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:38 dispy - Received reply for job / 140142376027384 from 192.168.0.110\n",
"2018-05-14 16:41:38 dispy - Running job 140142376027744 on 192.168.0.110\n",
"2018-05-14 16:41:38 dispy - Running job / 140142376027744 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:39 dispy - Received reply for job / 140142376027504 from 192.168.0.110\n",
"2018-05-14 16:41:39 dispy - Running job 140142376027864 on 192.168.0.110\n",
"2018-05-14 16:41:39 dispy - Running job / 140142376027864 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:39 dispy - Received reply for job / 140142376027624 from 192.168.0.110\n",
"2018-05-14 16:41:39 dispy - Running job 140142376027984 on 192.168.0.110\n",
"2018-05-14 16:41:39 dispy - Running job / 140142376027984 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:39 dispy - Received reply for job / 140142376027744 from 192.168.0.110\n",
"2018-05-14 16:41:39 dispy - Running job 140142376028104 on 192.168.0.110\n",
"2018-05-14 16:41:39 dispy - Running job / 140142376028104 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:40 dispy - Received reply for job / 140142376027864 from 192.168.0.110\n",
"2018-05-14 16:41:40 dispy - Running job 140142376028224 on 192.168.0.110\n",
"2018-05-14 16:41:40 dispy - Running job / 140142376028224 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:41 dispy - Received reply for job / 140142376028104 from 192.168.0.110\n",
"2018-05-14 16:41:41 dispy - Running job 140142376028344 on 192.168.0.110\n",
"2018-05-14 16:41:41 dispy - Running job / 140142376028344 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:41 dispy - Received reply for job / 140142376027984 from 192.168.0.110\n",
"2018-05-14 16:41:41 dispy - Running job 140142376028464 on 192.168.0.110\n",
"2018-05-14 16:41:41 dispy - Running job / 140142376028464 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:41 dispy - Received reply for job / 140142376028224 from 192.168.0.110\n",
"2018-05-14 16:41:41 dispy - Running job 140142376028584 on 192.168.0.110\n",
"2018-05-14 16:41:41 dispy - Running job / 140142376028584 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:42 dispy - Received reply for job / 140142376028464 from 192.168.0.110\n",
"2018-05-14 16:41:42 dispy - Running job 140142376028704 on 192.168.0.110\n",
"2018-05-14 16:41:42 dispy - Running job / 140142376028704 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:42 dispy - Received reply for job / 140142376028344 from 192.168.0.110\n",
"2018-05-14 16:41:42 dispy - Running job 140142376028824 on 192.168.0.110\n",
"2018-05-14 16:41:42 dispy - Running job / 140142376028824 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:42 dispy - Received reply for job / 140142376028584 from 192.168.0.110\n",
"2018-05-14 16:41:42 dispy - Running job 140142376028944 on 192.168.0.110\n",
"2018-05-14 16:41:42 dispy - Running job / 140142376028944 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:43 dispy - Received reply for job / 140142376028704 from 192.168.0.110\n",
"2018-05-14 16:41:43 dispy - Running job 140142376029064 on 192.168.0.110\n",
"2018-05-14 16:41:43 dispy - Running job / 140142376029064 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:43 dispy - Received reply for job / 140142376028824 from 192.168.0.110\n",
"2018-05-14 16:41:43 dispy - Running job 140142376029184 on 192.168.0.110\n",
"2018-05-14 16:41:43 dispy - Running job / 140142376029184 on 192.168.0.110 (busy: 3 / 3)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-05-14 16:41:44 dispy - Received reply for job / 140142376028944 from 192.168.0.110\n",
"2018-05-14 16:41:44 dispy - Running job 140142376029304 on 192.168.0.110\n",
"2018-05-14 16:41:44 dispy - Running job / 140142376029304 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:45 dispy - Received reply for job / 140142376029064 from 192.168.0.110\n",
"2018-05-14 16:41:45 dispy - Running job 140142376029424 on 192.168.0.110\n",
"2018-05-14 16:41:45 dispy - Running job / 140142376029424 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:45 dispy - Received reply for job / 140142376029184 from 192.168.0.110\n",
"2018-05-14 16:41:45 dispy - Running job 140142376029544 on 192.168.0.110\n",
"2018-05-14 16:41:45 dispy - Running job / 140142376029544 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:45 dispy - Received reply for job / 140142376029304 from 192.168.0.110\n",
"2018-05-14 16:41:45 dispy - Running job 140142376029664 on 192.168.0.110\n",
"2018-05-14 16:41:45 dispy - Running job / 140142376029664 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:46 dispy - Received reply for job / 140142376029544 from 192.168.0.110\n",
"2018-05-14 16:41:46 dispy - Running job 140142376029784 on 192.168.0.110\n",
"2018-05-14 16:41:46 dispy - Running job / 140142376029784 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:47 dispy - Received reply for job / 140142376029424 from 192.168.0.110\n",
"2018-05-14 16:41:47 dispy - Running job 140142376029904 on 192.168.0.110\n",
"2018-05-14 16:41:47 dispy - Running job / 140142376029904 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:47 dispy - Received reply for job / 140142376029664 from 192.168.0.110\n",
"2018-05-14 16:41:47 dispy - Running job 140142376030024 on 192.168.0.110\n",
"2018-05-14 16:41:47 dispy - Running job / 140142376030024 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:48 dispy - Received reply for job / 140142376029904 from 192.168.0.110\n",
"2018-05-14 16:41:48 dispy - Running job 140142374686792 on 192.168.0.110\n",
"2018-05-14 16:41:48 dispy - Running job / 140142374686792 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:48 dispy - Received reply for job / 140142376029784 from 192.168.0.110\n",
"2018-05-14 16:41:48 dispy - Running job 140142374686912 on 192.168.0.110\n",
"2018-05-14 16:41:48 dispy - Running job / 140142374686912 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:49 dispy - Received reply for job / 140142376030024 from 192.168.0.110\n",
"2018-05-14 16:41:49 dispy - Running job 140142374687032 on 192.168.0.110\n",
"2018-05-14 16:41:49 dispy - Running job / 140142374687032 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:49 dispy - Received reply for job / 140142374686792 from 192.168.0.110\n",
"2018-05-14 16:41:49 dispy - Running job 140142374687152 on 192.168.0.110\n",
"2018-05-14 16:41:49 dispy - Running job / 140142374687152 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:50 dispy - Received reply for job / 140142374686912 from 192.168.0.110\n",
"2018-05-14 16:41:50 dispy - Running job 140142374687272 on 192.168.0.110\n",
"2018-05-14 16:41:50 dispy - Running job / 140142374687272 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:50 dispy - Received reply for job / 140142374687032 from 192.168.0.110\n",
"2018-05-14 16:41:50 dispy - Running job 140142374687392 on 192.168.0.110\n",
"2018-05-14 16:41:50 dispy - Running job / 140142374687392 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:51 dispy - Received reply for job / 140142374687152 from 192.168.0.110\n",
"2018-05-14 16:41:51 dispy - Running job 140142374687512 on 192.168.0.110\n",
"2018-05-14 16:41:51 dispy - Running job / 140142374687512 on 192.168.0.110 (busy: 3 / 3)\n",
"2018-05-14 16:41:52 dispy - Received reply for job / 140142374687272 from 192.168.0.110\n",
"2018-05-14 16:41:52 dispy - Running job 140142374687632 on 192.168.0.110\n",
"2018-05-14 16:41:52 dispy - Running job