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Introduction to supervised machine learning in Elastic webinar - Tuesday, February 25, 2020
Raw
README.md

Introduction to supervised machine learning in Elastic webinar

Tuesday, February 25, 2020

Data and Python Jupyter Notebooks

Customer churn demo

Energy price forecasting demo

Language Identification Demo

This demo requires analysis-kuromoji, analysis-nori and analysis-smartcn to be installed.

Console API calls are in lang_ident_console_cmds in this gist.

See blog for more information.

Kibana saved objects

  • saved_objects.ndjson

Installation

This is how these notebooks were developed and tested (conda 4.8.1).

Create a local environment (in this directory):

python3 -m venv elastic_webinar
source elastic_webinar/bin/activate

Install requirements

pip install --upgrade pip
pip install -r requirements.txt

Run notebook server

jupyter notebook

Elasticsearch connection configuration

To connect to an Elastic Cloud Elasticsearch cluster add connection details to cloud_id.csv and credentials.csv.

cloud_id.csv should contain the cloud_id value shown in the Elastic Cloud deployment page (without \n) credentials.csv should contain the password for the elastic user to connect to Elasticsearch

e.g.

$ cat cloud_id.csv
ml_webinar:ZXVyb3BlZXdlc3QzLmdjcC5jbG91ZC5lcy5pbyRlZDBmMTQ2M2I3MTM0NjIwOGRlMzAzZWJjYmIzNGY2MSQ5NWE2YzNhYTBiMzk0YzI0OWEwYzI2Y2JlOTQ2YWY2Ng4=
$ cat credentials.csv
234dsp8ae42KWdYDZkn9Sg29

To connect to a local Elasticsearch cluster change CLOUD = True to CLOUD = False

Open, explore and run notebooks

The notebooks summarise the demos performed in the webinar.

Note running these notebooks may take ~1hr on a modest Elastic instance

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customer_churn.ipynb
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energy_price_forecasting.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Energy Price Forecasting\n",
"\n",
"Given a historical time series of hourly spot prices and weather, this analysis predicts future hourly spot prices multiple days into the future.\n",
"\n",
"> In many countries worldwide, electricity is now traded under market rules using spot and derivative contracts. However, electricity is a very special commodity. It is economically non-storable, and power system stability requires a constant balance between production and consumption ([Kaminski, 2013](https://scholar.google.com/scholar_lookup?title=Energy%20markets&publication_year=2013&author=V.%20Kaminski), [Shahidehpour et al., 2002](https://scholar.google.com/scholar_lookup?title=Market%20operations%20in%20electric%20power%20systems%3A%20forecasting%2C%20scheduling%2C%20and%20risk%20management&publication_year=2002&author=M.%20Shahidehpour&author=H.%20Yamin&author=Z.%20Li)). At the same time, electricity demand depends on weather (temperature, wind speed, precipitation, etc.) and the intensity of business and everyday activities (on-peak vs. off-peak hours, weekdays vs. weekends, holidays and near-holidays, etc.). On the one hand, these unique and specific characteristics lead to price dynamics not observed in any other market, exhibiting seasonality at the daily, weekly and annual levels, and abrupt, short-lived and generally unanticipated price spikes. On the other hand, they have encouraged researchers to intensify their efforts in the development of better forecasting techniques. [Weron, 2014](https://www.sciencedirect.com/science/article/pii/S0169207014001083)\n",
"\n",
"The code takes in data from the [EEM2016 energy price forecast competition](http://complatt.smartwatt.net/#/public/home), transforms and prepares the data (based on [here](https://github.com/GoogleCloudPlatform/professional-services/tree/master/examples/cloudml-energy-price-forecasting)), uses Elastic Machine Learning to train a regression model on historical spot prices, and then makes predictions of future spot prices using the model."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import eland as ed\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import elasticsearch\n",
"import numpy as np\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"CLOUD = True # are we connecting to an Elastic Cloud deployment?\n",
"DATA_INDEX = 'energy_price_forecasting' # source index for data\n",
"RESULT_INDEX = 'energy_price_forecasting_results' # destination index for results\n",
"JOB_NAME = 'energy_price_forecasting' # ML analysis job name"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Connect to Elasticsearch instance"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'name': 'instance-0000000000',\n",
" 'cluster_name': 'ed0f1463b71346208de303ebcbb34f61',\n",
" 'cluster_uuid': 'MvujRnv8R5eokONpWCTLKw',\n",
" 'version': {'number': '7.6.0',\n",
" 'build_flavor': 'default',\n",
" 'build_type': 'tar',\n",
" 'build_hash': '7f634e9f44834fbc12724506cc1da681b0c3b1e3',\n",
" 'build_date': '2020-02-06T00:09:00.449973Z',\n",
" 'build_snapshot': False,\n",
" 'lucene_version': '8.4.0',\n",
" 'minimum_wire_compatibility_version': '6.8.0',\n",
" 'minimum_index_compatibility_version': '6.0.0-beta1'},\n",
" 'tagline': 'You Know, for Search'}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"if CLOUD:\n",
" # See README.md for connection details \n",
" with open('cloud_id.csv','r') as f:\n",
" cloud_id = f.read()\n",
" user = 'elastic'\n",
" with open('credentials.csv','r') as f:\n",
" password = f.read()\n",
" \n",
" # Connect to Elastic Cloud elasticsearch cluster\n",
" es_client = elasticsearch.Elasticsearch(cloud_id=cloud_id, http_auth=(user, password))\n",
"else:\n",
" # Connect to localhost elasticsearch cluster (localhost:9200)\n",
" es_client = elasticsearch.Elasticsearch()\n",
"\n",
"es_client.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Read in transformed data using [eland.read_csv](https://eland.readthedocs.io/en/latest/reference/api/eland.read_csv.html#eland.read_csv)\n",
"\n",
"This reads the csv file and indexes the data into Elasticsearch index `energy_price_forecasting`.\n",
"\n",
"* price - FLOAT - Energy price.\n",
"* date_utc - TIMESTAMP - Date and hour for specified price.\n",
"* day - INTEGER - Day of week.\n",
"* hour - INTEGER - Hour of day.\n",
"* distribution0 to distribution4 - FLOAT - Distribution of hourly prices during the previous week (min, 25th, 50th, 75th, max).\n",
"* weather0 to weather179 - FLOAT - Weather features. Contains 10 distinct weather metrics (temperature, wind_speed_100m, wind_direction_100m, air_density, precipitation, wind_gust, radiation, wind_speed, wind_direction, pressure) from 18 distinct parts of the country (180 total features)."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"ed_df = ed.read_csv('energy_price_forecasting.csv', \n",
" es_client=es_client, \n",
" es_dest_index=DATA_INDEX,\n",
" es_if_exists='replace',\n",
" es_dropna=True,\n",
" parse_dates=['date_utc'], \n",
" index_col=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Summarise indexed data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'eland.dataframe.DataFrame'>\n",
"Index: 6500 entries, 2348 to 951\n",
"Columns: 190 entries, date_utc to weather99\n",
"dtypes: datetime64[ns](1), float64(187), int64(2)\n",
"memory usage: 96.0 bytes\n"
]
}
],
"source": [
"ed_df.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"date_utc datetime64[ns]\n",
"day int64\n",
"distribution0 float64\n",
"distribution1 float64\n",
"distribution2 float64\n",
" ... \n",
"weather95 float64\n",
"weather96 float64\n",
"weather97 float64\n",
"weather98 float64\n",
"weather99 float64\n",
"Length: 190, dtype: object"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ed_df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>price</th>\n",
" <th>test_price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>6164.000000</td>\n",
" <td>336.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.502183</td>\n",
" <td>0.506475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.125891</td>\n",
" <td>0.087033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.040000</td>\n",
" <td>0.253600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.430210</td>\n",
" <td>0.445217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.509858</td>\n",
" <td>0.508100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.600284</td>\n",
" <td>0.572500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.850500</td>\n",
" <td>0.684800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" price test_price\n",
"count 6164.000000 336.000000\n",
"mean 0.502183 0.506475\n",
"std 0.125891 0.087033\n",
"min 0.040000 0.253600\n",
"25% 0.430210 0.445217\n",
"50% 0.509858 0.508100\n",
"75% 0.600284 0.572500\n",
"max 0.850500 0.684800"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ed_df[['price', 'test_price']].describe()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date_utc</th>\n",
" <th>day</th>\n",
" <th>distribution0</th>\n",
" <th>distribution1</th>\n",
" <th>distribution2</th>\n",
" <th>distribution3</th>\n",
" <th>distribution4</th>\n",
" <th>hour</th>\n",
" <th>price</th>\n",
" <th>test_price</th>\n",
" <th>...</th>\n",
" <th>weather90</th>\n",
" <th>weather91</th>\n",
" <th>weather92</th>\n",
" <th>weather93</th>\n",
" <th>weather94</th>\n",
" <th>weather95</th>\n",
" <th>weather96</th>\n",
" <th>weather97</th>\n",
" <th>weather98</th>\n",
" <th>weather99</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2348</th>\n",
" <td>2015-04-16 20:00:00</td>\n",
" <td>4</td>\n",
" <td>0.252</td>\n",
" <td>0.3705</td>\n",
" <td>0.4430</td>\n",
" <td>0.5369</td>\n",
" <td>0.6410</td>\n",
" <td>20</td>\n",
" <td>0.5248</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>7.2</td>\n",
" <td>4.7</td>\n",
" <td>273.4</td>\n",
" <td>1.09</td>\n",
" <td>0.1</td>\n",
" <td>3.8</td>\n",
" <td>88.0</td>\n",
" <td>2.6</td>\n",
" <td>267.3</td>\n",
" <td>878.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2349</th>\n",
" <td>2015-04-16 21:00:00</td>\n",
" <td>4</td>\n",
" <td>0.252</td>\n",
" <td>0.3705</td>\n",
" <td>0.4430</td>\n",
" <td>0.5369</td>\n",
" <td>0.6410</td>\n",
" <td>21</td>\n",
" <td>0.4834</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>6.0</td>\n",
" <td>4.6</td>\n",
" <td>269.3</td>\n",
" <td>1.10</td>\n",
" <td>0.1</td>\n",
" <td>2.9</td>\n",
" <td>10.0</td>\n",
" <td>2.2</td>\n",
" <td>260.4</td>\n",
" <td>878.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>235</th>\n",
" <td>2015-01-14 19:00:00</td>\n",
" <td>3</td>\n",
" <td>0.023</td>\n",
" <td>0.3706</td>\n",
" <td>0.4601</td>\n",
" <td>0.6150</td>\n",
" <td>0.8505</td>\n",
" <td>19</td>\n",
" <td>0.6875</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>0.4</td>\n",
" <td>4.5</td>\n",
" <td>188.2</td>\n",
" <td>1.13</td>\n",
" <td>0.1</td>\n",
" <td>4.3</td>\n",
" <td>131.1</td>\n",
" <td>3.2</td>\n",
" <td>182.5</td>\n",
" <td>886.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2350</th>\n",
" <td>2015-04-16 22:00:00</td>\n",
" <td>4</td>\n",
" <td>0.252</td>\n",
" <td>0.3705</td>\n",
" <td>0.4430</td>\n",
" <td>0.5369</td>\n",
" <td>0.6410</td>\n",
" <td>22</td>\n",
" <td>0.5498</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>6.2</td>\n",
" <td>4.5</td>\n",
" <td>269.9</td>\n",
" <td>1.10</td>\n",
" <td>0.1</td>\n",
" <td>3.2</td>\n",
" <td>0.0</td>\n",
" <td>2.2</td>\n",
" <td>261.5</td>\n",
" <td>878.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2351</th>\n",
" <td>2015-04-16 23:00:00</td>\n",
" <td>4</td>\n",
" <td>0.252</td>\n",
" <td>0.3705</td>\n",
" <td>0.4430</td>\n",
" <td>0.5369</td>\n",
" <td>0.6410</td>\n",
" <td>23</td>\n",
" <td>0.4525</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>6.4</td>\n",
" <td>4.4</td>\n",
" <td>270.6</td>\n",
" <td>1.10</td>\n",
" <td>0.1</td>\n",
" <td>3.4</td>\n",
" <td>0.0</td>\n",
" <td>2.3</td>\n",
" <td>262.6</td>\n",
" <td>878.8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
"<p>5 rows × 190 columns</p>"
],
"text/plain": [
" date_utc day distribution0 distribution1 distribution2 \\\n",
"2348 2015-04-16 20:00:00 4 0.252 0.3705 0.4430 \n",
"2349 2015-04-16 21:00:00 4 0.252 0.3705 0.4430 \n",
"235 2015-01-14 19:00:00 3 0.023 0.3706 0.4601 \n",
"2350 2015-04-16 22:00:00 4 0.252 0.3705 0.4430 \n",
"2351 2015-04-16 23:00:00 4 0.252 0.3705 0.4430 \n",
"\n",
" distribution3 distribution4 hour price test_price ... weather90 \\\n",
"2348 0.5369 0.6410 20 0.5248 NaN ... 7.2 \n",
"2349 0.5369 0.6410 21 0.4834 NaN ... 6.0 \n",
"235 0.6150 0.8505 19 0.6875 NaN ... 0.4 \n",
"2350 0.5369 0.6410 22 0.5498 NaN ... 6.2 \n",
"2351 0.5369 0.6410 23 0.4525 NaN ... 6.4 \n",
"\n",
" weather91 weather92 weather93 weather94 weather95 weather96 \\\n",
"2348 4.7 273.4 1.09 0.1 3.8 88.0 \n",
"2349 4.6 269.3 1.10 0.1 2.9 10.0 \n",
"235 4.5 188.2 1.13 0.1 4.3 131.1 \n",
"2350 4.5 269.9 1.10 0.1 3.2 0.0 \n",
"2351 4.4 270.6 1.10 0.1 3.4 0.0 \n",
"\n",
" weather97 weather98 weather99 \n",
"2348 2.6 267.3 878.1 \n",
"2349 2.2 260.4 878.6 \n",
"235 3.2 182.5 886.2 \n",
"2350 2.2 261.5 878.7 \n",
"2351 2.3 262.6 878.8 \n",
"\n",
"[5 rows x 190 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ed_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read weather data (used in Kibana visualizations) - this could take several minutes"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>air_density</th>\n",
" <th>date</th>\n",
" <th>latitude</th>\n",
" <th>location</th>\n",
" <th>longitude</th>\n",
" <th>point</th>\n",
" <th>precipitation</th>\n",
" <th>pressure</th>\n",
" <th>radiation</th>\n",
" <th>temperature</th>\n",
" <th>wind_direction</th>\n",
" <th>wind_direction_100m</th>\n",
" <th>wind_gust</th>\n",
" <th>wind_speed</th>\n",
" <th>wind_speed_100m</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.17</td>\n",
" <td>2014-01-01 12:00:00</td>\n",
" <td>43</td>\n",
" <td>43,-8</td>\n",
" <td>-8</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>952.2</td>\n",
" <td>10.0</td>\n",
" <td>9.8</td>\n",
" <td>203.7</td>\n",
" <td>210.3</td>\n",
" <td>19.4</td>\n",
" <td>7.0</td>\n",
" <td>13.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.17</td>\n",
" <td>2014-01-01 13:00:00</td>\n",
" <td>43</td>\n",
" <td>43,-8</td>\n",
" <td>-8</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>951.6</td>\n",
" <td>16.8</td>\n",
" <td>9.8</td>\n",
" <td>204.0</td>\n",
" <td>210.0</td>\n",
" <td>19.6</td>\n",
" <td>7.0</td>\n",
" <td>13.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.17</td>\n",
" <td>2014-01-01 14:00:00</td>\n",
" <td>43</td>\n",
" <td>43,-8</td>\n",
" <td>-8</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>951.1</td>\n",
" <td>19.9</td>\n",
" <td>9.8</td>\n",
" <td>204.3</td>\n",
" <td>209.8</td>\n",
" <td>19.9</td>\n",
" <td>7.0</td>\n",
" <td>13.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.17</td>\n",
" <td>2014-01-01 15:00:00</td>\n",
" <td>43</td>\n",
" <td>43,-8</td>\n",
" <td>-8</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>950.5</td>\n",
" <td>20.0</td>\n",
" <td>9.8</td>\n",
" <td>204.6</td>\n",
" <td>209.6</td>\n",
" <td>20.1</td>\n",
" <td>7.1</td>\n",
" <td>13.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.17</td>\n",
" <td>2014-01-01 16:00:00</td>\n",
" <td>43</td>\n",
" <td>43,-8</td>\n",
" <td>-8</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>950.7</td>\n",
" <td>17.9</td>\n",
" <td>9.9</td>\n",
" <td>212.6</td>\n",
" <td>218.2</td>\n",
" <td>19.0</td>\n",
" <td>7.1</td>\n",
" <td>13.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>272587</th>\n",
" <td>1.08</td>\n",
" <td>2015-10-19 07:00:00</td>\n",
" <td>41</td>\n",
" <td>41,0</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>0.0</td>\n",
" <td>877.5</td>\n",
" <td>6.8</td>\n",
" <td>9.7</td>\n",
" <td>157.3</td>\n",
" <td>155.5</td>\n",
" <td>2.0</td>\n",
" <td>1.3</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>272588</th>\n",
" <td>1.08</td>\n",
" <td>2015-10-19 08:00:00</td>\n",
" <td>41</td>\n",
" <td>41,0</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>0.0</td>\n",
" <td>877.7</td>\n",
" <td>17.1</td>\n",
" <td>10.7</td>\n",
" <td>155.8</td>\n",
" <td>150.4</td>\n",
" <td>1.8</td>\n",
" <td>1.2</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>272589</th>\n",
" <td>1.07</td>\n",
" <td>2015-10-19 09:00:00</td>\n",
" <td>41</td>\n",
" <td>41,0</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>0.0</td>\n",
" <td>878.0</td>\n",
" <td>30.0</td>\n",
" <td>11.7</td>\n",
" <td>154.4</td>\n",
" <td>145.3</td>\n",
" <td>1.6</td>\n",
" <td>1.1</td>\n",
" <td>1.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>272590</th>\n",
" <td>1.07</td>\n",
" <td>2015-10-19 10:00:00</td>\n",
" <td>41</td>\n",
" <td>41,0</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>0.0</td>\n",
" <td>878.1</td>\n",
" <td>44.9</td>\n",
" <td>12.3</td>\n",
" <td>217.3</td>\n",
" <td>210.7</td>\n",
" <td>1.2</td>\n",
" <td>0.9</td>\n",
" <td>1.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>272591</th>\n",
" <td>1.07</td>\n",
" <td>2015-10-19 11:00:00</td>\n",
" <td>41</td>\n",
" <td>41,0</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>0.0</td>\n",
" <td>878.1</td>\n",
" <td>61.4</td>\n",
" <td>12.9</td>\n",
" <td>280.3</td>\n",
" <td>276.1</td>\n",
" <td>0.7</td>\n",
" <td>0.8</td>\n",
" <td>0.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
"<p>272592 rows × 15 columns</p>"
],
"text/plain": [
" air_density date latitude location longitude point \\\n",
"0 1.17 2014-01-01 12:00:00 43 43,-8 -8 1 \n",
"1 1.17 2014-01-01 13:00:00 43 43,-8 -8 1 \n",
"2 1.17 2014-01-01 14:00:00 43 43,-8 -8 1 \n",
"3 1.17 2014-01-01 15:00:00 43 43,-8 -8 1 \n",
"4 1.17 2014-01-01 16:00:00 43 43,-8 -8 1 \n",
"... ... ... ... ... ... ... \n",
"272587 1.08 2015-10-19 07:00:00 41 41,0 0 18 \n",
"272588 1.08 2015-10-19 08:00:00 41 41,0 0 18 \n",
"272589 1.07 2015-10-19 09:00:00 41 41,0 0 18 \n",
"272590 1.07 2015-10-19 10:00:00 41 41,0 0 18 \n",
"272591 1.07 2015-10-19 11:00:00 41 41,0 0 18 \n",
"\n",
" precipitation pressure radiation temperature wind_direction \\\n",
"0 0.0 952.2 10.0 9.8 203.7 \n",
"1 0.0 951.6 16.8 9.8 204.0 \n",
"2 0.0 951.1 19.9 9.8 204.3 \n",
"3 0.0 950.5 20.0 9.8 204.6 \n",
"4 0.0 950.7 17.9 9.9 212.6 \n",
"... ... ... ... ... ... \n",
"272587 0.0 877.5 6.8 9.7 157.3 \n",
"272588 0.0 877.7 17.1 10.7 155.8 \n",
"272589 0.0 878.0 30.0 11.7 154.4 \n",
"272590 0.0 878.1 44.9 12.3 217.3 \n",
"272591 0.0 878.1 61.4 12.9 280.3 \n",
"\n",
" wind_direction_100m wind_gust wind_speed wind_speed_100m \n",
"0 210.3 19.4 7.0 13.6 \n",
"1 210.0 19.6 7.0 13.6 \n",
"2 209.8 19.9 7.0 13.6 \n",
"3 209.6 20.1 7.1 13.6 \n",
"4 218.2 19.0 7.1 13.8 \n",
"... ... ... ... ... \n",
"272587 155.5 2.0 1.3 1.7 \n",
"272588 150.4 1.8 1.2 1.7 \n",
"272589 145.3 1.6 1.1 1.6 \n",
"272590 210.7 1.2 0.9 1.3 \n",
"272591 276.1 0.7 0.8 0.9 \n",
"\n",
"[272592 rows x 15 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ed.read_csv(\"energy_price_weather.csv\",\n",
" es_client=es_client, \n",
" es_dest_index='energy_price_weather',\n",
" es_if_exists='replace',\n",
" es_dropna=True,\n",
" es_geo_points=['location'],\n",
" parse_dates=['date'],\n",
" index_col=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read power data (used in Kibana visualizations) - this could take several minutes"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>value</th>\n",
" <th>variable</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>31500</th>\n",
" <td>2015-11-27 04:00:00</td>\n",
" <td>1340.8</td>\n",
" <td>coal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31501</th>\n",
" <td>2015-11-27 04:15:00</td>\n",
" <td>1479.6</td>\n",
" <td>coal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31502</th>\n",
" <td>2015-11-27 04:30:00</td>\n",
" <td>1502.8</td>\n",
" <td>coal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31503</th>\n",
" <td>2015-11-27 04:45:00</td>\n",
" <td>1572.0</td>\n",
" <td>coal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31504</th>\n",
" <td>2015-11-27 05:00:00</td>\n",
" <td>1710.0</td>\n",
" <td>coal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>452967</th>\n",
" <td>2015-12-31 22:45:00</td>\n",
" <td>0.0</td>\n",
" <td>pumping</td>\n",
" </tr>\n",
" <tr>\n",
" <th>452968</th>\n",
" <td>2015-12-31 23:00:00</td>\n",
" <td>0.0</td>\n",
" <td>pumping</td>\n",
" </tr>\n",
" <tr>\n",
" <th>452969</th>\n",
" <td>2015-12-31 23:15:00</td>\n",
" <td>0.0</td>\n",
" <td>pumping</td>\n",
" </tr>\n",
" <tr>\n",
" <th>452970</th>\n",
" <td>2015-12-31 23:30:00</td>\n",
" <td>0.0</td>\n",
" <td>pumping</td>\n",
" </tr>\n",
" <tr>\n",
" <th>452971</th>\n",
" <td>2015-12-31 23:45:00</td>\n",
" <td>0.0</td>\n",
" <td>pumping</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
"<p>452972 rows × 3 columns</p>"
],
"text/plain": [
" date value variable\n",
"31500 2015-11-27 04:00:00 1340.8 coal\n",
"31501 2015-11-27 04:15:00 1479.6 coal\n",
"31502 2015-11-27 04:30:00 1502.8 coal\n",
"31503 2015-11-27 04:45:00 1572.0 coal\n",
"31504 2015-11-27 05:00:00 1710.0 coal\n",
"... ... ... ...\n",
"452967 2015-12-31 22:45:00 0.0 pumping\n",
"452968 2015-12-31 23:00:00 0.0 pumping\n",
"452969 2015-12-31 23:15:00 0.0 pumping\n",
"452970 2015-12-31 23:30:00 0.0 pumping\n",
"452971 2015-12-31 23:45:00 0.0 pumping\n",
"\n",
"[452972 rows x 3 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ed.read_csv(\"energy_price_power.csv\",\n",
" es_client=es_client, \n",
" es_dest_index='energy_price_power',\n",
" es_if_exists='replace',\n",
" es_dropna=True,\n",
" parse_dates=['date'],\n",
" index_col=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a regression model to predict price"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cleanup old analysis/jobs"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"ret = es_client.indices.delete(index=RESULT_INDEX, ignore=[400, 404])\n",
"ret = es_client.ml.stop_data_frame_analytics(JOB_NAME, ignore=[400, 404])\n",
"ret = es_client.ml.delete_data_frame_analytics(JOB_NAME, ignore=[400, 404])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create ML regression analysis job"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'id': 'energy_price_forecasting',\n",
" 'source': {'index': ['energy_price_forecasting'], 'query': {'match_all': {}}},\n",
" 'dest': {'index': 'energy_price_forecasting_results', 'results_field': 'ml'},\n",
" 'analysis': {'regression': {'dependent_variable': 'price',\n",
" 'num_top_feature_importance_values': 5,\n",
" 'prediction_field_name': 'price_prediction',\n",
" 'training_percent': 100.0,\n",
" 'randomize_seed': 1}},\n",
" 'analyzed_fields': {'includes': [], 'excludes': []},\n",
" 'model_memory_limit': '2gb',\n",
" 'create_time': 1582629990071,\n",
" 'version': '7.6.0',\n",
" 'allow_lazy_start': False}"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_config = {\n",
" \"source\": {\n",
" \"index\": [\n",
" DATA_INDEX\n",
" ]\n",
" },\n",
" \"dest\": {\n",
" \"index\": RESULT_INDEX\n",
" },\n",
" \"analysis\": {\n",
" \"regression\": {\n",
" \"dependent_variable\": \"price\",\n",
" \"num_top_feature_importance_values\": 5,\n",
" \"randomize_seed\": 1\n",
" }\n",
" },\n",
" \"analyzed_fields\": {\n",
" \"includes\": [],\n",
" \"excludes\": []\n",
" },\n",
" \"model_memory_limit\": \"2gb\"\n",
"}\n",
"\n",
"es_client.ml.put_data_frame_analytics(JOB_NAME, analysis_config)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Start ML job and monitor progress (note the job may take ~45mins on a standard GCP.ML instance on Elastic Cloud)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2020-02-25 11:26:31.283973 reindexing reindexing:1 loading_data:0 analyzing:0 writing_results:0 \n",
"2020-02-25 11:27:31.330880 analyzing reindexing:100 loading_data:100 analyzing:0 writing_results:0 \n",
"2020-02-25 11:28:31.373940 analyzing reindexing:100 loading_data:100 analyzing:0 writing_results:0 \n",
"2020-02-25 11:29:31.413009 analyzing reindexing:100 loading_data:100 analyzing:0 writing_results:0 \n",
"2020-02-25 11:30:31.456674 analyzing reindexing:100 loading_data:100 analyzing:0 writing_results:0 \n",
"2020-02-25 11:31:31.594324 analyzing reindexing:100 loading_data:100 analyzing:0 writing_results:0 \n",
"2020-02-25 11:32:31.640381 analyzing reindexing:100 loading_data:100 analyzing:0 writing_results:0 \n",
"2020-02-25 11:33:31.702093 analyzing reindexing:100 loading_data:100 analyzing:0 writing_results:0 \n",
"2020-02-25 11:34:31.755368 analyzing reindexing:100 loading_data:100 analyzing:0 writing_results:0 \n",
"2020-02-25 11:35:31.797870 analyzing reindexing:100 loading_data:100 analyzing:0 writing_results:0 \n",
"2020-02-25 11:36:31.841622 analyzing reindexing:100 loading_data:100 analyzing:0 writing_results:0 \n",
"2020-02-25 11:37:31.902029 analyzing reindexing:100 loading_data:100 analyzing:0 writing_results:0 \n",
"2020-02-25 11:38:31.943558 analyzing reindexing:100 loading_data:100 analyzing:0 writing_results:0 \n",
"2020-02-25 11:39:31.994137 analyzing reindexing:100 loading_data:100 analyzing:0 writing_results:0 \n",
"2020-02-25 11:40:32.041441 analyzing reindexing:100 loading_data:100 analyzing:0 writing_results:0 \n",
"2020-02-25 11:41:32.088199 analyzing reindexing:100 loading_data:100 analyzing:0 writing_results:0 \n",
"2020-02-25 11:42:32.648751 analyzing reindexing:100 loading_data:100 analyzing:0 writing_results:0 \n",
"2020-02-25 11:43:32.688329 analyzing reindexing:100 loading_data:100 analyzing:0 writing_results:0 \n",
"2020-02-25 11:44:32.742369 analyzing reindexing:100 loading_data:100 analyzing:3 writing_results:0 \n",
"2020-02-25 11:45:32.790900 analyzing reindexing:100 loading_data:100 analyzing:8 writing_results:0 \n",
"2020-02-25 11:46:32.841700 analyzing reindexing:100 loading_data:100 analyzing:9 writing_results:0 \n",
"2020-02-25 11:47:32.880705 analyzing reindexing:100 loading_data:100 analyzing:12 writing_results:0 \n",
"2020-02-25 11:48:32.923245 analyzing reindexing:100 loading_data:100 analyzing:13 writing_results:0 \n",
"2020-02-25 11:49:32.968409 analyzing reindexing:100 loading_data:100 analyzing:17 writing_results:0 \n",
"2020-02-25 11:50:33.010560 analyzing reindexing:100 loading_data:100 analyzing:20 writing_results:0 \n",
"2020-02-25 11:51:33.061337 analyzing reindexing:100 loading_data:100 analyzing:23 writing_results:0 \n",
"2020-02-25 11:52:33.107291 analyzing reindexing:100 loading_data:100 analyzing:25 writing_results:0 \n",
"2020-02-25 11:53:33.169414 analyzing reindexing:100 loading_data:100 analyzing:26 writing_results:0 \n",
"2020-02-25 11:54:33.219357 analyzing reindexing:100 loading_data:100 analyzing:29 writing_results:0 \n",
"2020-02-25 11:55:33.266814 analyzing reindexing:100 loading_data:100 analyzing:34 writing_results:0 \n",
"2020-02-25 11:56:33.335751 analyzing reindexing:100 loading_data:100 analyzing:40 writing_results:0 \n",
"2020-02-25 11:57:33.396142 analyzing reindexing:100 loading_data:100 analyzing:47 writing_results:0 \n",
"2020-02-25 11:58:33.438398 analyzing reindexing:100 loading_data:100 analyzing:49 writing_results:0 \n",
"2020-02-25 11:59:33.474964 analyzing reindexing:100 loading_data:100 analyzing:59 writing_results:0 \n",
"2020-02-25 12:00:33.532294 analyzing reindexing:100 loading_data:100 analyzing:64 writing_results:0 \n",
"2020-02-25 12:01:33.636352 analyzing reindexing:100 loading_data:100 analyzing:72 writing_results:0 \n",
"2020-02-25 12:02:33.682427 analyzing reindexing:100 loading_data:100 analyzing:78 writing_results:0 \n",
"2020-02-25 12:03:33.731757 analyzing reindexing:100 loading_data:100 analyzing:81 writing_results:0 \n",
"2020-02-25 12:04:33.782803 analyzing reindexing:100 loading_data:100 analyzing:83 writing_results:0 \n",
"2020-02-25 12:05:33.831690 analyzing reindexing:100 loading_data:100 analyzing:86 writing_results:0 \n",
"2020-02-25 12:06:33.887955 analyzing reindexing:100 loading_data:100 analyzing:88 writing_results:0 \n",
"2020-02-25 12:07:33.928964 analyzing reindexing:100 loading_data:100 analyzing:89 writing_results:0 \n",
"2020-02-25 12:08:33.978913 analyzing reindexing:100 loading_data:100 analyzing:91 writing_results:0 \n",
"2020-02-25 12:09:34.026803 analyzing reindexing:100 loading_data:100 analyzing:95 writing_results:0 \n",
"2020-02-25 12:10:34.070850 analyzing reindexing:100 loading_data:100 analyzing:99 writing_results:0 \n",
"2020-02-25 12:11:34.120042 stopped reindexing:100 loading_data:100 analyzing:100 writing_results:100 \n"
]
}
],
"source": [
"es_client.ml.start_data_frame_analytics(JOB_NAME)\n",
"\n",
"while True:\n",
" results = es_client.ml.get_data_frame_analytics_stats(JOB_NAME)\n",
" state = results['data_frame_analytics'][0]['state']\n",
" progress = results['data_frame_analytics'][0]['progress']\n",
" status = state + \" \"\n",
" for p in progress:\n",
" status = status + p['phase'] + \":\" + str(p['progress_percent']) + \" \"\n",
" print(str(pd.Timestamp.now()), status)\n",
" if state == 'stopped':\n",
" break\n",
" time.sleep(60)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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imported_ml_models.ipynb
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lang_ident_console_cmds
DELETE standard_analyzer
PUT /standard_analyzer
{
"mappings": {
"properties": {
"body": {
"type": "text"
}
}
}
}
PUT /standard_analyzer/_doc/1
{
"body" : "세계인의 축제, 제23회 동계올림픽대회는 대한민국 강원도 평창에서 2018년 2월 9일부터 25일까지 17일간 개최됩니다. 대한민국 평창은 세 번의 도전 끝에 지난 2011년 7월 6일 열린 제123차 IOC 총회에서 과반 표를 획득하며 2018년 동계올림픽 개최지로 선정되었습니다. 이로써 대한민국에서는 1988년 서울 올림픽 이후 30년 만에, 평창에서 개∙폐회식과 대부분의 설상 경기가 개최되며, 강릉에서는 빙상 종목 전 경기가, 그리고 정선에서는 알파인 스키 활강 경기가 개최될 예정입니다."
}
PUT /standard_analyzer/_doc/2
{
"body" : "The XXIII Olympic Winter Games will be held for 17 days from 9 to 25 February 2018 in PyeongChang, Gangwon Province, the Republic of Korea. PyeongChang was selected as the host city of the 2018 Olympic Winter Games after receiving a majority vote at the 123rd IOC Session held on 6 July 2011 after three consecutive bids. The Olympic Winter Games will be held in Korea for the first time in 30 years after the Seoul Olympic Games in 1988. PyeongChang will be the stage for the Opening and Closing Ceremonies and most snow sports. Alpine speed events will take place in Jeongseon, and all ice sports will be competed in the coastal city of Gangneung."
}
PUT /standard_analyzer/_doc/3
{
"body": "第23届冬季奥运会将于2018年2月9日-25日在韩国江原道平昌展开。韩国平昌在第三次申奥之后，于2011年7月6日召开的第123届国际奥委会全会上被选定为2018年冬季奥运会的主办地。由此，韩国自1988年举办首尔夏季奥运会以后，时隔30年，将首次举办冬季奥运会。该届冬奥会的开·闭幕式以及大部分的雪上运动将在平昌进行，而所有冰上运动将在江陵、高山滑雪滑降比赛则将在旌善进行。"
}
PUT /standard_analyzer/_doc/4
{
"body": "世界の人々の祝祭、第23回冬季オリンピック大会は大韓民国江原道平昌で2018年2月9日から25日までの17日間、開催されます。大韓民国・平昌は三度の挑戦の末、2011年7月7日に開かれた第123回IOC総会で過半数票を獲得し、2018年冬季オリンピック及びパラリンピックの開催地に選ばれました。これにより1988年ソウルオリンピック開催後30年の時を経てついに、大韓民国で最初の冬季パラリンピックの舞台が繰り広げられます。平昌で開・閉会式とほぼ全ての雪上競技が開催され、江陵では氷上種目全競技が、そして旌善ではアルペンスキー滑降競技が開催される予定です。"
}
GET /standard_analyzer/_search
{
"query": {
"multi_match": {
"query": "올림픽대회",
"fields": [
"body"
]
}
}
}
DELETE /language_analyzers
PUT /language_analyzers
{
"mappings": {
"properties": {
"english_field": {
"type": "text"
},
"korean_field": {
"analyzer": "nori",
"type": "text"
},
"japanese_field": {
"analyzer": "kuromoji",
"type": "text"
},
"chinese_field": {
"analyzer": "smartcn",
"type": "text"
}
}
}
}
POST /_ingest/pipeline/_simulate
{
"pipeline": {
"processors": [
{
"inference": {
"model_id": "lang_ident_model_1",
"inference_config": {
"classification": {
"num_top_classes": 1
}
},
"field_mappings": {
"body": "text"
}
}
}
]
},
"docs": [
{
"_source": {
"body": "세계인의 축제, 제23회 동계올림픽대회는 대한민국 강원도 평창에서 2018년 2월 9일부터 25일까지 17일간 개최됩니다. 대한민국 평창은 세 번의 도전 끝에 지난 2011년 7월 6일 열린 제123차 IOC 총회에서 과반 표를 획득하며 2018년 동계올림픽 개최지로 선정되었습니다. 이로써 대한민국에서는 1988년 서울 올림픽 이후 30년 만에, 평창에서 개∙폐회식과 대부분의 설상 경기가 개최되며, 강릉에서는 빙상 종목 전 경기가, 그리고 정선에서는 알파인 스키 활강 경기가 개최될 예정입니다."
}
},
{
"_source": {
"body": "The XXIII Olympic Winter Games will be held for 17 days from 9 to 25 February 2018 in PyeongChang, Gangwon Province, the Republic of Korea. PyeongChang was selected as the host city of the 2018 Olympic Winter Games after receiving a majority vote at the 123rd IOC Session held on 6 July 2011 after three consecutive bids. The Olympic Winter Games will be held in Korea for the first time in 30 years after the Seoul Olympic Games in 1988. PyeongChang will be the stage for the Opening and Closing Ceremonies and most snow sports. Alpine speed events will take place in Jeongseon, and all ice sports will be competed in the coastal city of Gangneung."
}
},
{
"_source": {
"body": "第23届冬季奥运会将于2018年2月9日-25日在韩国江原道平昌展开。韩国平昌在第三次申奥之后，于2011年7月6日召开的第123届国际奥委会全会上被选定为2018年冬季奥运会的主办地。由此，韩国自1988年举办首尔夏季奥运会以后，时隔30年，将首次举办冬季奥运会。该届冬奥会的开·闭幕式以及大部分的雪上运动将在平昌进行，而所有冰上运动将在江陵、高山滑雪滑降比赛则将在旌善进行。"
}
},
{
"_source": {
"body": "世界の人々の祝祭、第23回冬季オリンピック大会は大韓民国江原道平昌で2018年2月9日から25日までの17日間、開催されます。大韓民国・平昌は三度の挑戦の末、2011年7月7日に開かれた第123回IOC総会で過半数票を獲得し、2018年冬季オリンピック及びパラリンピックの開催地に選ばれました。これにより1988年ソウルオリンピック開催後30年の時を経てついに、大韓民国で最初の冬季パラリンピックの舞台が繰り広げられます。平昌で開・閉会式とほぼ全ての雪上競技が開催され、江陵では氷上種目全競技が、そして旌善ではアルペンスキー滑降競技が開催される予定です。"
}
}
]
}
PUT /_ingest/pipeline/langdetect-pipeline
{
"processors": [
{
"inference": {
"model_id": "lang_ident_model_1",
"inference_config": {
"classification": {}
},
"field_mappings": {
"body": "text"
}
}
},
{
"script": {
"lang": "painless",
"source": """
if (ctx.ml.inference.predicted_value == "ko") ctx.korean_field = ctx.body;
if (ctx.ml.inference.predicted_value == "ja") ctx.japanese_field = ctx.body;
if (ctx.ml.inference.predicted_value == "zh") ctx.chinese_field = ctx.body;
if (ctx.ml.inference.predicted_value == "en") ctx.english_field = ctx.body;"""
}
},
{
"remove": {
"field": "body"
}
}
]
}
PUT /language_analyzers/_doc/1?pipeline=langdetect-pipeline
{
"body" : "세계인의 축제, 제23회 동계올림픽대회는 대한민국 강원도 평창에서 2018년 2월 9일부터 25일까지 17일간 개최됩니다. 대한민국 평창은 세 번의 도전 끝에 지난 2011년 7월 6일 열린 제123차 IOC 총회에서 과반 표를 획득하며 2018년 동계올림픽 개최지로 선정되었습니다. 이로써 대한민국에서는 1988년 서울 올림픽 이후 30년 만에, 평창에서 개∙폐회식과 대부분의 설상 경기가 개최되며, 강릉에서는 빙상 종목 전 경기가, 그리고 정선에서는 알파인 스키 활강 경기가 개최될 예정입니다."
}
PUT /language_analyzers/_doc/2?pipeline=langdetect-pipeline
{
"body" : "The XXIII Olympic Winter Games will be held for 17 days from 9 to 25 February 2018 in PyeongChang, Gangwon Province, the Republic of Korea. PyeongChang was selected as the host city of the 2018 Olympic Winter Games after receiving a majority vote at the 123rd IOC Session held on 6 July 2011 after three consecutive bids. The Olympic Winter Games will be held in Korea for the first time in 30 years after the Seoul Olympic Games in 1988. PyeongChang will be the stage for the Opening and Closing Ceremonies and most snow sports. Alpine speed events will take place in Jeongseon, and all ice sports will be competed in the coastal city of Gangneung."
}
PUT /language_analyzers/_doc/3?pipeline=langdetect-pipeline
{
"body" : "第23届冬季奥运会将于2018年2月9日-25日在韩国江原道平昌展开。韩国平昌在第三次申奥之后，于2011年7月6日召开的第123届国际奥委会全会上被选定为2018年冬季奥运会的主办地。由此，韩国自1988年举办首尔夏季奥运会以后，时隔30年，将首次举办冬季奥运会。该届冬奥会的开·闭幕式以及大部分的雪上运动将在平昌进行，而所有冰上运动将在江陵、高山滑雪滑降比赛则将在旌善进行。"
}
PUT /language_analyzers/_doc/4?pipeline=langdetect-pipeline
{
"body" : "世界の人々の祝祭、第23回冬季オリンピック大会は大韓民国江原道平昌で2018年2月9日から25日までの17日間、開催されます。大韓民国・平昌は三度の挑戦の末、2011年7月7日に開かれた第123回IOC総会で過半数票を獲得し、2018年冬季オリンピック及びパラリンピックの開催地に選ばれました。これにより1988年ソウルオリンピック開催後30年の時を経てついに、大韓民国で最初の冬季パラリンピックの舞台が繰り広げられます。平昌で開・閉会式とほぼ全ての雪上競技が開催され、江陵では氷上種目全競技が、そして旌善ではアルペンスキー滑降競技が開催される予定です。"
}
GET /language_analyzers/_search
Raw
requirements.txt
eland>=7.6.0a3
xgboost==0.90
scikit-learn
jupyter
requests
Raw
saved_objects.ndjson
{"attributes":{"buildNum":29000,"defaultIndex":"840427d0-574b-11ea-a299-cd501a6b9d56"},"id":"7.6.0","references":[],"type":"config","updated_at":"2020-02-24T21:20:44.219Z","version":"WzI1LDFd"}
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{"attributes":{"description":"","kibanaSavedObjectMeta":{"searchSourceJSON":"{}"},"title":"Energy Wind Speed","uiStateJSON":"{}","version":1,"visState":"{\"title\":\"Energy Wind Speed\",\"type\":\"metrics\",\"params\":{\"axis_formatter\":\"number\",\"axis_position\":\"left\",\"axis_scale\":\"normal\",\"default_index_pattern\":\"energy_price_forecasting_results\",\"default_timefield\":\"date_utc\",\"id\":\"61ca57f0-469d-11e7-af02-69e470af7417\",\"index_pattern\":\"energy_price_weather\",\"interval\":\"\",\"isModelInvalid\":false,\"series\":[{\"axis_position\":\"right\",\"chart_type\":\"line\",\"color\":\"#68BC00\",\"fill\":\"-1.7\",\"formatter\":\"number\",\"id\":\"61ca57f1-469d-11e7-af02-69e470af7417\",\"line_width\":1,\"metrics\":[{\"id\":\"61ca57f2-469d-11e7-af02-69e470af7417\",\"type\":\"avg\",\"field\":\"wind_speed\"}],\"point_size\":1,\"separate_axis\":0,\"split_mode\":\"terms\",\"stacked\":\"none\",\"label\":\"wind_speed\",\"type\":\"timeseries\",\"terms_field\":\"point\"}],\"show_grid\":1,\"show_legend\":1,\"time_field\":\"date\",\"type\":\"timeseries\"},\"aggs\":[]}"},"id":"dd33c090-57cd-11ea-a299-cd501a6b9d56","migrationVersion":{"visualization":"7.4.2"},"references":[],"type":"visualization","updated_at":"2020-02-25T12:53:47.929Z","version":"WzExNCwxXQ=="}
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