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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

Wednesday, May 27, 2020 - updated customer_churn.ipynb for version 7.7.0

Monday, November 16, 2020 - updated for version 7.10 and eland 7.10b

Monday, September 20, 2021 - updated customer_churn.ipynb for version 7.14

Monday, February 8, 2022 - updated customer_churn.ipynb for version 8.0

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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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": 17,
"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": 18,
"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": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'name': 'instance-0000000000',\n",
" 'cluster_name': 'b737ad7db9cc4833b9d9510b36ada0a7',\n",
" 'cluster_uuid': 'Gsjz1pYISLObiCCHO721Ew',\n",
" 'version': {'number': '7.10.0',\n",
" 'build_flavor': 'default',\n",
" 'build_type': 'docker',\n",
" 'build_hash': '51e9d6f22758d0374a0f3f5c6e8f3a7997850f96',\n",
" 'build_date': '2020-11-09T21:30:33.964949Z',\n",
" 'build_snapshot': False,\n",
" 'lucene_version': '8.7.0',\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": 19,
"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[:-1]))\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": 20,
"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": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'eland.dataframe.DataFrame'>\n",
"Index: 6552 entries, 0 to 999\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": 22,
"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": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ed_df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 23,
"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>6216.000000</td>\n",
" <td>336.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.500997</td>\n",
" <td>0.506475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.126586</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>0.429393</td>\n",
" <td>0.445217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.508730</td>\n",
" <td>0.508100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.600035</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 6216.000000 336.000000\n",
"mean 0.500997 0.506475\n",
"std 0.126586 0.087033\n",
"min 0.040000 0.253600\n",
"25% 0.429393 0.445217\n",
"50% 0.508730 0.508100\n",
"75% 0.600035 0.572500\n",
"max 0.850500 0.684800"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ed_df[['price', 'test_price']].describe()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" 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>0</th>\n",
" <td>2015-01-05 00:00:00</td>\n",
" <td>1</td>\n",
" <td>0.023</td>\n",
" <td>0.2110</td>\n",
" <td>0.3610</td>\n",
" <td>0.4514</td>\n",
" <td>0.6501</td>\n",
" <td>0</td>\n",
" <td>0.4807</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>-3.5</td>\n",
" <td>1.9</td>\n",
" <td>135.9</td>\n",
" <td>1.15</td>\n",
" <td>0.0</td>\n",
" <td>1.9</td>\n",
" <td>0.0</td>\n",
" <td>1.3</td>\n",
" <td>127.2</td>\n",
" <td>893.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2015-01-05 01:00:00</td>\n",
" <td>1</td>\n",
" <td>0.023</td>\n",
" <td>0.2110</td>\n",
" <td>0.3610</td>\n",
" <td>0.4514</td>\n",
" <td>0.6501</td>\n",
" <td>1</td>\n",
" <td>0.4393</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>-3.4</td>\n",
" <td>2.0</td>\n",
" <td>144.5</td>\n",
" <td>1.15</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>1.4</td>\n",
" <td>136.1</td>\n",
" <td>893.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2015-01-05 10:00:00</td>\n",
" <td>1</td>\n",
" <td>0.023</td>\n",
" <td>0.2110</td>\n",
" <td>0.3610</td>\n",
" <td>0.4514</td>\n",
" <td>0.6501</td>\n",
" <td>10</td>\n",
" <td>0.6597</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>5.7</td>\n",
" <td>3.5</td>\n",
" <td>184.6</td>\n",
" <td>1.11</td>\n",
" <td>0.0</td>\n",
" <td>3.3</td>\n",
" <td>48.6</td>\n",
" <td>2.7</td>\n",
" <td>179.9</td>\n",
" <td>891.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>2015-01-09 04:00:00</td>\n",
" <td>5</td>\n",
" <td>0.023</td>\n",
" <td>0.2110</td>\n",
" <td>0.3610</td>\n",
" <td>0.4514</td>\n",
" <td>0.6501</td>\n",
" <td>4</td>\n",
" <td>0.5060</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>-4.1</td>\n",
" <td>2.6</td>\n",
" <td>46.3</td>\n",
" <td>1.17</td>\n",
" <td>0.3</td>\n",
" <td>2.3</td>\n",
" <td>0.0</td>\n",
" <td>1.6</td>\n",
" <td>46.7</td>\n",
" <td>901.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>2015-02-15 16:00:00</td>\n",
" <td>0</td>\n",
" <td>0.170</td>\n",
" <td>0.3667</td>\n",
" <td>0.4521</td>\n",
" <td>0.5381</td>\n",
" <td>0.7003</td>\n",
" <td>16</td>\n",
" <td>0.2965</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>5.1</td>\n",
" <td>6.7</td>\n",
" <td>276.6</td>\n",
" <td>1.10</td>\n",
" <td>0.0</td>\n",
" <td>7.5</td>\n",
" <td>129.2</td>\n",
" <td>4.7</td>\n",
" <td>276.1</td>\n",
" <td>875.5</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",
"0 2015-01-05 00:00:00 1 0.023 0.2110 0.3610 \n",
"1 2015-01-05 01:00:00 1 0.023 0.2110 0.3610 \n",
"10 2015-01-05 10:00:00 1 0.023 0.2110 0.3610 \n",
"100 2015-01-09 04:00:00 5 0.023 0.2110 0.3610 \n",
"1000 2015-02-15 16:00:00 0 0.170 0.3667 0.4521 \n",
"\n",
" distribution3 distribution4 hour price test_price ... weather90 \\\n",
"0 0.4514 0.6501 0 0.4807 NaN ... -3.5 \n",
"1 0.4514 0.6501 1 0.4393 NaN ... -3.4 \n",
"10 0.4514 0.6501 10 0.6597 NaN ... 5.7 \n",
"100 0.4514 0.6501 4 0.5060 NaN ... -4.1 \n",
"1000 0.5381 0.7003 16 0.2965 NaN ... 5.1 \n",
"\n",
" weather91 weather92 weather93 weather94 weather95 weather96 \\\n",
"0 1.9 135.9 1.15 0.0 1.9 0.0 \n",
"1 2.0 144.5 1.15 0.0 2.0 0.0 \n",
"10 3.5 184.6 1.11 0.0 3.3 48.6 \n",
"100 2.6 46.3 1.17 0.3 2.3 0.0 \n",
"1000 6.7 276.6 1.10 0.0 7.5 129.2 \n",
"\n",
" weather97 weather98 weather99 \n",
"0 1.3 127.2 893.6 \n",
"1 1.4 136.1 893.0 \n",
"10 2.7 179.9 891.4 \n",
"100 1.6 46.7 901.7 \n",
"1000 4.7 276.1 875.5 \n",
"\n",
"[5 rows x 190 columns]"
]
},
"execution_count": 24,
"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": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <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>23969</th>\n",
" <td>1.18</td>\n",
" <td>2015-01-15 05:00:00</td>\n",
" <td>41</td>\n",
" <td>41,-8</td>\n",
" <td>-8</td>\n",
" <td>2</td>\n",
" <td>0.1</td>\n",
" <td>943.6</td>\n",
" <td>0.0</td>\n",
" <td>5.5</td>\n",
" <td>197.8</td>\n",
" <td>205.0</td>\n",
" <td>10.0</td>\n",
" <td>2.7</td>\n",
" <td>6.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23970</th>\n",
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" <td>0.1</td>\n",
" <td>942.8</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>201.4</td>\n",
" <td>208.1</td>\n",
" <td>12.0</td>\n",
" <td>3.0</td>\n",
" <td>6.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23971</th>\n",
" <td>1.18</td>\n",
" <td>2015-01-15 07:00:00</td>\n",
" <td>41</td>\n",
" <td>41,-8</td>\n",
" <td>-8</td>\n",
" <td>2</td>\n",
" <td>0.1</td>\n",
" <td>942.3</td>\n",
" <td>0.2</td>\n",
" <td>6.2</td>\n",
" <td>200.4</td>\n",
" <td>207.1</td>\n",
" <td>12.0</td>\n",
" <td>3.1</td>\n",
" <td>7.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23972</th>\n",
" <td>1.17</td>\n",
" <td>2015-01-15 08:00:00</td>\n",
" <td>41</td>\n",
" <td>41,-8</td>\n",
" <td>-8</td>\n",
" <td>2</td>\n",
" <td>0.1</td>\n",
" <td>941.8</td>\n",
" <td>0.0</td>\n",
" <td>6.4</td>\n",
" <td>199.3</td>\n",
" <td>206.1</td>\n",
" <td>12.1</td>\n",
" <td>3.2</td>\n",
" <td>7.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23973</th>\n",
" <td>1.17</td>\n",
" <td>2015-01-15 09:00:00</td>\n",
" <td>41</td>\n",
" <td>41,-8</td>\n",
" <td>-8</td>\n",
" <td>2</td>\n",
" <td>0.1</td>\n",
" <td>941.4</td>\n",
" <td>0.0</td>\n",
" <td>6.7</td>\n",
" <td>198.3</td>\n",
" <td>205.2</td>\n",
" <td>12.1</td>\n",
" <td>3.4</td>\n",
" <td>7.6</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",
"23969 1.18 2015-01-15 05:00:00 41 41,-8 -8 2 \n",
"23970 1.18 2015-01-15 06:00:00 41 41,-8 -8 2 \n",
"23971 1.18 2015-01-15 07:00:00 41 41,-8 -8 2 \n",
"23972 1.17 2015-01-15 08:00:00 41 41,-8 -8 2 \n",
"23973 1.17 2015-01-15 09:00:00 41 41,-8 -8 2 \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",
"23969 0.1 943.6 0.0 5.5 197.8 \n",
"23970 0.1 942.8 0.0 6.0 201.4 \n",
"23971 0.1 942.3 0.2 6.2 200.4 \n",
"23972 0.1 941.8 0.0 6.4 199.3 \n",
"23973 0.1 941.4 0.0 6.7 198.3 \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",
"23969 205.0 10.0 2.7 6.2 \n",
"23970 208.1 12.0 3.0 6.9 \n",
"23971 207.1 12.0 3.1 7.2 \n",
"23972 206.1 12.1 3.2 7.4 \n",
"23973 205.2 12.1 3.4 7.6 \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": 27,
"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_type_overrides={'location': 'geo_point'},\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": 28,
"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>0</th>\n",
" <td>2015-01-01 00:00:00</td>\n",
" <td>1757.2</td>\n",
" <td>coal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2015-01-01 00:15:00</td>\n",
" <td>1757.6</td>\n",
" <td>coal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2015-01-01 00:30:00</td>\n",
" <td>1757.2</td>\n",
" <td>coal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2015-01-01 00:45:00</td>\n",
" <td>1756.4</td>\n",
" <td>coal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015-01-01 01:00:00</td>\n",
" <td>1756.0</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",
"0 2015-01-01 00:00:00 1757.2 coal\n",
"1 2015-01-01 00:15:00 1757.6 coal\n",
"2 2015-01-01 00:30:00 1757.2 coal\n",
"3 2015-01-01 00:45:00 1756.4 coal\n",
"4 2015-01-01 01:00:00 1756.0 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": 28,
"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": 29,
"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": 31,
"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",
" 'loss_function': 'mse'}},\n",
" 'analyzed_fields': {'includes': [], 'excludes': []},\n",
" 'model_memory_limit': '2gb',\n",
" 'create_time': 1605523132569,\n",
" 'version': '7.10.0',\n",
" 'allow_lazy_start': False,\n",
" 'max_num_threads': 1}"
]
},
"execution_count": 31,
"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": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2020-11-16 10:38:56.659990 started reindexing:1 loading_data:0 feature_selection:0 coarse_parameter_search:0 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:39:56.691676 analyzing reindexing:100 loading_data:100 feature_selection:3 coarse_parameter_search:0 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:40:56.739869 analyzing reindexing:100 loading_data:100 feature_selection:29 coarse_parameter_search:0 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:41:56.770450 analyzing reindexing:100 loading_data:100 feature_selection:84 coarse_parameter_search:0 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:42:56.792760 analyzing reindexing:100 loading_data:100 feature_selection:99 coarse_parameter_search:0 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:43:56.818403 analyzing reindexing:100 loading_data:100 feature_selection:99 coarse_parameter_search:0 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:44:56.852165 analyzing reindexing:100 loading_data:100 feature_selection:99 coarse_parameter_search:0 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:45:56.879490 analyzing reindexing:100 loading_data:100 feature_selection:99 coarse_parameter_search:0 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:46:56.911853 analyzing reindexing:100 loading_data:100 feature_selection:99 coarse_parameter_search:0 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:47:56.939228 analyzing reindexing:100 loading_data:100 feature_selection:99 coarse_parameter_search:0 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:48:56.964264 analyzing reindexing:100 loading_data:100 feature_selection:99 coarse_parameter_search:0 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:49:57.001504 analyzing reindexing:100 loading_data:100 feature_selection:99 coarse_parameter_search:0 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:50:57.030970 analyzing reindexing:100 loading_data:100 feature_selection:99 coarse_parameter_search:0 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:51:57.059052 analyzing reindexing:100 loading_data:100 feature_selection:99 coarse_parameter_search:0 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:52:57.083831 analyzing reindexing:100 loading_data:100 feature_selection:99 coarse_parameter_search:0 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:53:57.131809 analyzing reindexing:100 loading_data:100 feature_selection:99 coarse_parameter_search:0 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:54:57.158366 analyzing reindexing:100 loading_data:100 feature_selection:99 coarse_parameter_search:0 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:55:57.197574 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:4 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:56:57.222547 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:15 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:57:57.248612 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:20 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:58:57.372440 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:34 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 10:59:57.400636 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:45 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:00:57.428018 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:59 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:01:57.459872 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:64 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:02:57.488169 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:71 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:03:57.527748 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:80 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:04:57.552591 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:05:57.577522 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:1 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:06:57.602384 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:2 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:07:57.626735 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:4 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:08:57.659737 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:5 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:09:57.686385 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:7 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:10:57.719862 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:9 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:11:57.752140 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:11 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:12:57.849861 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:13 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:13:57.882604 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:15 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:14:57.912771 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:16 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:15:57.942965 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:17 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:16:57.964462 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:17 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:17:57.999053 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:18 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:18:58.123825 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:20 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:19:58.149326 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:20 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:20:58.176112 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:21 final_training:0 writing_results:0 inference:0 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2020-11-16 11:21:58.209114 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:22 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:22:58.238908 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:23 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:23:58.273281 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:24 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:24:58.311296 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:25 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:25:58.342587 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:26 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:26:58.367687 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:33 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:27:58.400035 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:38 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:28:58.437701 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:40 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:29:58.478005 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:45 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:30:58.510712 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:46 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:31:58.540910 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:50 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:32:58.577255 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:51 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:33:58.607607 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:56 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:34:58.639054 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:61 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:35:58.677033 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:62 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:36:58.706660 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:63 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:37:58.737007 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:64 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:38:58.866394 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:67 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:39:58.891615 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:68 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:40:58.919519 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:72 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:41:58.956212 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:77 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:42:58.981613 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:79 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:43:59.010507 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:80 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:44:59.035788 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:81 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:45:59.122493 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:84 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:46:59.152170 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:85 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:47:59.184831 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:86 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:48:59.214497 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:88 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:49:59.243110 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:89 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:50:59.274015 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:91 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:51:59.300674 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:92 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:52:59.331144 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:93 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:53:59.354806 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:94 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:54:59.400040 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:95 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:55:59.433139 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:96 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:56:59.465405 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:98 final_training:0 writing_results:0 inference:0 \n",
"2020-11-16 11:57:59.499310 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:100 final_training:69 writing_results:0 inference:0 \n",
"2020-11-16 11:58:59.632615 analyzing reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:100 final_training:100 writing_results:58 inference:0 \n",
"2020-11-16 11:59:59.669153 stopped reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:100 final_training:100 writing_results:100 inference: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
}
Display the source blob
Display the rendered blob
Raw
imported_ml_models.ipynb
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Raw
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>=7.10.0b1
xgboost==0.90
scikit-learn
jupyter
requests
Raw
saved_objects.ndjson
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