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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

Display the source blob
Display the rendered blob
Raw
customer_churn.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"# Customer Churn Analysis\n",
"\n",
"The goal of this analysis is to understand which customers of a telco are likely to cancel their subscriptions (churn) and why?\n",
"\n",
"In churn analysis, it is important to\n",
"\n",
"* identify customers who are likely to stop using a service\n",
"* identify factors which trigger the decision\n",
"\n",
"This analysis:\n",
"\n",
"1. transforms the raw data into an entity-centric view into customer behaviour\n",
"2. trains a binary classification (churn, no churn) ML model\n",
"3. uses the ML model to predict which customers are likely to churn and why\n",
"\n",
"Data is derived from a dataset referenced in various sources: [openml](https://www.openml.org/d/40701), [kaggle](https://www.kaggle.com/becksddf/churn-in-telecoms-dataset) [Larose 2014](http://dataminingconsultant.com/DKD2e_data_sets.zip). This dataset was disaggregated into line items, and random features where added. Phone numbers and other data are fabricated and any resemblable to real data is coincidental."
]
},
{
"cell_type": "code",
"execution_count": 57,
"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\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\", category=DeprecationWarning)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ObjectApiResponse({'name': 'instance-0000000000', 'cluster_name': '66de2e13118f461a9c47a9777d19c9d6', 'cluster_uuid': 'uEt7rAZJR6e_9sq8p24kHA', 'version': {'number': '8.0.0-rc2', 'build_flavor': 'default', 'build_type': 'docker', 'build_hash': '1b6a7ece17463df5ff54a3e1302d825889aa1161', 'build_date': '2022-01-31T21:03:07.336372824Z', 'build_snapshot': False, 'lucene_version': '9.0.0', 'minimum_wire_compatibility_version': '7.17.0', 'minimum_index_compatibility_version': '7.0.0'}, 'tagline': 'You Know, for Search'})"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CLOUD = True # are we connecting to an Elastic Cloud deployment?\n",
"\n",
"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[:-1], 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 raw data using eland.read_csv\n",
"This reads csv files and indexes the data into Elasticsearch indices."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Call records\n",
"\n",
"* call_charges: the charge in dollars for the call\n",
"* call_duration: the duration of the call in minutes\n",
"* dialled_number: the recipient of the call\n",
"* phone_number: the customer's phone number - essentially a surrogate for customer id\n",
"* timestamp: the start time of the call"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Note there are 1,002,092 call records, so indexing this into a single Elastic Cloud instance may\n",
"# take ~5 mins. Restoring from a snapshot would be significantly quicker.\n",
"\n",
"df_calls = ed.csv_to_eland('calls.csv', \n",
" es_client=es_client, \n",
" es_dest_index='calls',\n",
" es_if_exists='replace',\n",
" es_dropna=True,\n",
" parse_dates=['timestamp'], \n",
" index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'eland.dataframe.DataFrame'>\n",
"Index: 1002092 entries, 200000 to 930182\n",
"Data columns (total 5 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 call_charges 1002092 non-null float64 \n",
" 1 call_duration 1002092 non-null float64 \n",
" 2 dialled_number 1002092 non-null object \n",
" 3 phone_number 1002092 non-null object \n",
" 4 timestamp 1002092 non-null datetime64[ns]\n",
"dtypes: datetime64[ns](1), float64(2), object(2)\n",
"memory usage: 64.000 bytes\n",
"Elasticsearch storage usage: 130.745 MB\n"
]
}
],
"source": [
"df_calls.info()"
]
},
{
"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>call_charges</th>\n",
" <th>call_duration</th>\n",
" <th>dialled_number</th>\n",
" <th>phone_number</th>\n",
" <th>timestamp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>200000</th>\n",
" <td>0.258150</td>\n",
" <td>2.290440</td>\n",
" <td>415-329-8638</td>\n",
" <td>415-368-8283</td>\n",
" <td>2020-07-08 23:09:16.147329</td>\n",
" </tr>\n",
" <tr>\n",
" <th>200001</th>\n",
" <td>0.113430</td>\n",
" <td>1.006410</td>\n",
" <td>415-373-4483</td>\n",
" <td>415-368-8283</td>\n",
" <td>2020-05-07 02:27:00.181845</td>\n",
" </tr>\n",
" <tr>\n",
" <th>200002</th>\n",
" <td>0.241665</td>\n",
" <td>2.144175</td>\n",
" <td>415-396-6775</td>\n",
" <td>415-368-8283</td>\n",
" <td>2020-07-26 02:22:51.608788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>200003</th>\n",
" <td>0.167663</td>\n",
" <td>1.487592</td>\n",
" <td>408-343-6643</td>\n",
" <td>415-368-8283</td>\n",
" <td>2020-10-14 06:06:53.294717</td>\n",
" </tr>\n",
" <tr>\n",
" <th>200004</th>\n",
" <td>0.052975</td>\n",
" <td>0.470021</td>\n",
" <td>510-393-9677</td>\n",
" <td>415-368-8283</td>\n",
" <td>2020-12-22 11:27:09.976609</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
"<p>5 rows × 5 columns</p>"
],
"text/plain": [
" call_charges call_duration dialled_number phone_number \\\n",
"200000 0.258150 2.290440 415-329-8638 415-368-8283 \n",
"200001 0.113430 1.006410 415-373-4483 415-368-8283 \n",
"200002 0.241665 2.144175 415-396-6775 415-368-8283 \n",
"200003 0.167663 1.487592 408-343-6643 415-368-8283 \n",
"200004 0.052975 0.470021 510-393-9677 415-368-8283 \n",
"\n",
" timestamp \n",
"200000 2020-07-08 23:09:16.147329 \n",
"200001 2020-05-07 02:27:00.181845 \n",
"200002 2020-07-26 02:22:51.608788 \n",
"200003 2020-10-14 06:06:53.294717 \n",
"200004 2020-12-22 11:27:09.976609 \n",
"\n",
"[5 rows x 5 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_calls.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Customer data\n",
"\n",
"Data related to the attributes and behaviour of a customer:\n",
"\n",
"* account_length: integer-valued, how long account has been active in days\n",
"* churn: indicator of whether the customer has left the company (True - 1 or False - 0)\n",
"* customer_service_calls: number of calls to customer service\n",
"* international_plan: dichotomous categorical, yes or no\n",
"* number_vmail_messages: number of voice mail messages\n",
"* phone_number: the customer's phone number - essentially a surrogate for customer id\n",
"* state: categorical, for the 50 states and the District of Columbia\n",
"* voice_mail_plan: Dichotomous categorical, yes or no"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"df_customers = ed.csv_to_eland('customers.csv', \n",
" es_client=es_client, \n",
" es_dest_index='customers',\n",
" es_if_exists='replace',\n",
" es_dropna=True,\n",
" index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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>account_length</th>\n",
" <th>churn</th>\n",
" <th>customer_service_calls</th>\n",
" <th>international_plan</th>\n",
" <th>number_vmail_messages</th>\n",
" <th>phone_number</th>\n",
" <th>state</th>\n",
" <th>voice_mail_plan</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2500</th>\n",
" <td>121</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>41</td>\n",
" <td>415-337-2348</td>\n",
" <td>UT</td>\n",
" <td>yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2501</th>\n",
" <td>101</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" <td>415-342-8702</td>\n",
" <td>IL</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2502</th>\n",
" <td>115</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" <td>408-393-5802</td>\n",
" <td>DC</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2503</th>\n",
" <td>168</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" <td>415-384-2219</td>\n",
" <td>IN</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2504</th>\n",
" <td>90</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" <td>415-347-6164</td>\n",
" <td>NM</td>\n",
" <td>no</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
"<p>5 rows × 8 columns</p>"
],
"text/plain": [
" account_length churn customer_service_calls international_plan \\\n",
"2500 121 0 1 no \n",
"2501 101 0 0 no \n",
"2502 115 0 2 no \n",
"2503 168 0 2 no \n",
"2504 90 0 1 no \n",
"\n",
" number_vmail_messages phone_number state voice_mail_plan \n",
"2500 41 415-337-2348 UT yes \n",
"2501 0 415-342-8702 IL no \n",
"2502 0 408-393-5802 DC no \n",
"2503 0 415-384-2219 IN no \n",
"2504 0 415-347-6164 NM no \n",
"\n",
"[5 rows x 8 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_customers.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Transform raw data into entity-centric index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"'Join' calls and customers to create an 'entity-centric' index summarising the behaviour of each customer.\n",
"\n",
"First create an index template to map strings created by the transform and ML analysis to keywords. "
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_30459/1719681357.py:22: ElasticsearchWarning: Legacy index templates are deprecated in favor of composable templates.\n",
" es_client.indices.put_template(name='churn_template', body=template_config)\n"
]
},
{
"data": {
"text/plain": [
"ObjectApiResponse({'acknowledged': True})"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"template_config = {\n",
" \"index_patterns\": [\n",
" \"churn*\"\n",
" ],\n",
" \"settings\": {\n",
" \"number_of_shards\": 1\n",
" },\n",
" \"mappings\": {\n",
" \"dynamic_templates\": [\n",
" {\n",
" \"strings_as_keyword\": {\n",
" \"match_mapping_type\": \"string\",\n",
" \"mapping\": {\n",
" \"type\": \"keyword\"\n",
" }\n",
" }\n",
" }\n",
" ]\n",
" }\n",
"}\n",
"\n",
"es_client.indices.put_template(name='churn_template', body=template_config)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now clean up old transforms and indices"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"ret = es_client.indices.delete(index='churn', ignore=[400, 404])\n",
"ret = es_client.transform.stop_transform(transform_id='churn', ignore=[400, 404])\n",
"ret = es_client.transform.delete_transform(transform_id='churn', ignore=[400, 404])"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"transform_config = {\n",
" \"source\": {\n",
" \"index\": [\n",
" \"calls\",\n",
" \"customers\"\n",
" ]\n",
" },\n",
" \"dest\": {\n",
" \"index\": \"churn\"\n",
" },\n",
" \"pivot\": {\n",
" \"group_by\": {\n",
" \"phone_number\": {\n",
" \"terms\": {\n",
" \"field\": \"phone_number\"\n",
" }\n",
" }\n",
" },\n",
" \"aggregations\": {\n",
" \"call_charges\": {\n",
" \"sum\": {\n",
" \"field\": \"call_charges\"\n",
" }\n",
" },\n",
" \"call_duration\": {\n",
" \"sum\": {\n",
" \"field\": \"call_duration\"\n",
" }\n",
" },\n",
" \"call_count\": {\n",
" \"value_count\": {\n",
" \"field\": \"dialled_number\"\n",
" }\n",
" },\n",
" \"customer_service_calls\": {\n",
" \"sum\": {\n",
" \"field\": \"customer_service_calls\"\n",
" }\n",
" },\n",
" \"number_vmail_messages\": {\n",
" \"sum\": {\n",
" \"field\": \"number_vmail_messages\"\n",
" }\n",
" },\n",
" \"churn\": {\n",
" \"scripted_metric\": {\n",
" \"init_script\": \"state.churn = null\",\n",
" \"map_script\": \"state.churn = params._source.churn\",\n",
" \"combine_script\": \"return state.churn\",\n",
" \"reduce_script\": \"for (d in states) if (d != null) return d\"\n",
" }\n",
" },\n",
" \"account_length\": {\n",
" \"scripted_metric\": {\n",
" \"init_script\": \"state.account_length = null\",\n",
" \"map_script\": \"state.account_length = params._source.account_length\",\n",
" \"combine_script\": \"return state.account_length\",\n",
" \"reduce_script\": \"for (d in states) if (d != null) return d\"\n",
" }\n",
" },\n",
" \"international_plan\": {\n",
" \"scripted_metric\": {\n",
" \"init_script\": \"state.international_plan = null\",\n",
" \"map_script\": \"state.international_plan = params._source.international_plan\",\n",
" \"combine_script\": \"return state.international_plan\",\n",
" \"reduce_script\": \"for (d in states) if (d != null) return d\"\n",
" }\n",
" },\n",
" \"voice_mail_plan\": {\n",
" \"scripted_metric\": {\n",
" \"init_script\": \"state.voice_mail_plan = null\",\n",
" \"map_script\": \"state.voice_mail_plan = params._source.voice_mail_plan\",\n",
" \"combine_script\": \"return state.voice_mail_plan\",\n",
" \"reduce_script\": \"for (d in states) if (d != null) return d\"\n",
" }\n",
" },\n",
" \"state\": {\n",
" \"scripted_metric\": {\n",
" \"init_script\": \"state.state = null\",\n",
" \"map_script\": \"state.state = params._source.state\",\n",
" \"combine_script\": \"return state.state\",\n",
" \"reduce_script\": \"for (d in states) if (d != null) return d\"\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Preview the transform"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"preview = es_client.transform.preview_transform(body=transform_config)\n",
"\n",
"preview_df = pd.DataFrame.from_records(preview['preview'])"
]
},
{
"cell_type": "code",
"execution_count": 63,
"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>voice_mail_plan</th>\n",
" <th>call_count</th>\n",
" <th>number_vmail_messages</th>\n",
" <th>churn</th>\n",
" <th>account_length</th>\n",
" <th>phone_number</th>\n",
" <th>international_plan</th>\n",
" <th>state</th>\n",
" <th>call_charges</th>\n",
" <th>customer_service_calls</th>\n",
" <th>call_duration</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>no</td>\n",
" <td>308</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>105</td>\n",
" <td>408-327-6764</td>\n",
" <td>no</td>\n",
" <td>NE</td>\n",
" <td>55.31</td>\n",
" <td>4.0</td>\n",
" <td>589.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>yes</td>\n",
" <td>262</td>\n",
" <td>31.0</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>408-327-9289</td>\n",
" <td>no</td>\n",
" <td>OH</td>\n",
" <td>51.97</td>\n",
" <td>1.0</td>\n",
" <td>588.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>no</td>\n",
" <td>268</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>80</td>\n",
" <td>408-327-9957</td>\n",
" <td>no</td>\n",
" <td>DC</td>\n",
" <td>30.32</td>\n",
" <td>3.0</td>\n",
" <td>384.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>no</td>\n",
" <td>224</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>118</td>\n",
" <td>408-328-1222</td>\n",
" <td>no</td>\n",
" <td>CO</td>\n",
" <td>60.48</td>\n",
" <td>4.0</td>\n",
" <td>626.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>no</td>\n",
" <td>283</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>137</td>\n",
" <td>408-328-2110</td>\n",
" <td>no</td>\n",
" <td>OH</td>\n",
" <td>45.68</td>\n",
" <td>0.0</td>\n",
" <td>453.7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" voice_mail_plan call_count number_vmail_messages churn account_length \\\n",
"0 no 308 0.0 0 105 \n",
"1 yes 262 31.0 0 52 \n",
"2 no 268 0.0 0 80 \n",
"3 no 224 0.0 0 118 \n",
"4 no 283 0.0 0 137 \n",
"\n",
" phone_number international_plan state call_charges \\\n",
"0 408-327-6764 no NE 55.31 \n",
"1 408-327-9289 no OH 51.97 \n",
"2 408-327-9957 no DC 30.32 \n",
"3 408-328-1222 no CO 60.48 \n",
"4 408-328-2110 no OH 45.68 \n",
"\n",
" customer_service_calls call_duration \n",
"0 4.0 589.5 \n",
"1 1.0 588.5 \n",
"2 3.0 384.9 \n",
"3 4.0 626.3 \n",
"4 0.0 453.7 "
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preview_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check a record is correct"
]
},
{
"cell_type": "code",
"execution_count": 64,
"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>voice_mail_plan</th>\n",
" <th>call_count</th>\n",
" <th>number_vmail_messages</th>\n",
" <th>churn</th>\n",
" <th>account_length</th>\n",
" <th>phone_number</th>\n",
" <th>international_plan</th>\n",
" <th>state</th>\n",
" <th>call_charges</th>\n",
" <th>customer_service_calls</th>\n",
" <th>call_duration</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>no</td>\n",
" <td>308</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>105</td>\n",
" <td>408-327-6764</td>\n",
" <td>no</td>\n",
" <td>NE</td>\n",
" <td>55.31</td>\n",
" <td>4.0</td>\n",
" <td>589.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" voice_mail_plan call_count number_vmail_messages churn account_length \\\n",
"0 no 308 0.0 0 105 \n",
"\n",
" phone_number international_plan state call_charges \\\n",
"0 408-327-6764 no NE 55.31 \n",
"\n",
" customer_service_calls call_duration \n",
"0 4.0 589.5 "
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preview_df[preview_df.phone_number=='408-327-6764']"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"call_charges 55.31\n",
"call_duration 589.50\n",
"dtype: float64"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_calls[df_calls.phone_number=='408-327-6764'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"call_charges 308\n",
"call_duration 308\n",
"dialled_number 308\n",
"phone_number 308\n",
"timestamp 308\n",
"dtype: int64"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_calls[df_calls.phone_number=='408-327-6764'].count()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"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>account_length</th>\n",
" <th>churn</th>\n",
" <th>customer_service_calls</th>\n",
" <th>international_plan</th>\n",
" <th>number_vmail_messages</th>\n",
" <th>phone_number</th>\n",
" <th>state</th>\n",
" <th>voice_mail_plan</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2087</th>\n",
" <td>105</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" <td>408-327-6764</td>\n",
" <td>NE</td>\n",
" <td>no</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
"<p>1 rows × 8 columns</p>"
],
"text/plain": [
" account_length churn customer_service_calls international_plan \\\n",
"2087 105 0 4 no \n",
"\n",
" number_vmail_messages phone_number state voice_mail_plan \n",
"2087 0 408-327-6764 NE no \n",
"\n",
"[1 rows x 8 columns]"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_customers[df_customers.phone_number=='408-327-6764']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Transform is consistent with raw data aggregates, now create transform"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ObjectApiResponse({'acknowledged': True})"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"es_client.transform.put_transform(transform_id='churn', body=transform_config)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2022-02-07 19:04:05.109023 state: indexing documents_processed: 0 documents_indexed: 0\n",
"2022-02-07 19:04:06.207164 state: indexing documents_processed: 149715 documents_indexed: 500\n",
"2022-02-07 19:04:07.304768 state: indexing documents_processed: 301456 documents_indexed: 1000\n",
"2022-02-07 19:04:08.402923 state: indexing documents_processed: 452445 documents_indexed: 1500\n",
"2022-02-07 19:04:09.499938 state: indexing documents_processed: 603147 documents_indexed: 2000\n",
"2022-02-07 19:04:10.598559 state: indexing documents_processed: 754388 documents_indexed: 2500\n",
"2022-02-07 19:04:11.695727 state: indexing documents_processed: 904806 documents_indexed: 3000\n",
"2022-02-07 19:04:12.795907 state: stopped documents_processed: 1005425 documents_indexed: 3333\n"
]
}
],
"source": [
"es_client.transform.start_transform(transform_id='churn')\n",
"\n",
"while True:\n",
" results = es_client.transform.get_transform_stats(transform_id='churn')\n",
" state = results['transforms'][0]['state']\n",
" documents_processed = str(results['transforms'][0]['stats']['documents_processed'])\n",
" documents_indexed = str(results['transforms'][0]['stats']['documents_indexed'])\n",
" status = \"state: \" + state + \" documents_processed: \" + documents_processed + \" documents_indexed: \" + documents_indexed\n",
" print(str(pd.Timestamp.now()), status)\n",
" if state == 'stopped':\n",
" break\n",
" time.sleep(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show transformed data"
]
},
{
"cell_type": "code",
"execution_count": 70,
"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>account_length</th>\n",
" <th>call_charges</th>\n",
" <th>call_count</th>\n",
" <th>call_duration</th>\n",
" <th>churn</th>\n",
" <th>customer_service_calls</th>\n",
" <th>international_plan</th>\n",
" <th>number_vmail_messages</th>\n",
" <th>phone_number</th>\n",
" <th>state</th>\n",
" <th>voice_mail_plan</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>NCZgNKO6O7FR7IL7QvUWYwwAAAAAAAAA</th>\n",
" <td>105</td>\n",
" <td>55.31</td>\n",
" <td>308</td>\n",
" <td>589.5</td>\n",
" <td>0</td>\n",
" <td>4.0</td>\n",
" <td>no</td>\n",
" <td>0.0</td>\n",
" <td>408-327-6764</td>\n",
" <td>NE</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NJB4UinRN1KuQXeUp8N0_gkAAAAAAAAA</th>\n",
" <td>52</td>\n",
" <td>51.97</td>\n",
" <td>262</td>\n",
" <td>588.5</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>no</td>\n",
" <td>31.0</td>\n",
" <td>408-327-9289</td>\n",
" <td>OH</td>\n",
" <td>yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NACawapaqeWu6A369Ao35EIAAAAAAAAA</th>\n",
" <td>80</td>\n",
" <td>30.32</td>\n",
" <td>268</td>\n",
" <td>384.9</td>\n",
" <td>0</td>\n",
" <td>3.0</td>\n",
" <td>no</td>\n",
" <td>0.0</td>\n",
" <td>408-327-9957</td>\n",
" <td>DC</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ND4QlLEAv1kpCLfHPKC7BoIAAAAAAAAA</th>\n",
" <td>118</td>\n",
" <td>60.48</td>\n",
" <td>224</td>\n",
" <td>626.3</td>\n",
" <td>0</td>\n",
" <td>4.0</td>\n",
" <td>no</td>\n",
" <td>0.0</td>\n",
" <td>408-328-1222</td>\n",
" <td>CO</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NK6_HrQbovr8uM7AoXuezRAAAAAAAAAA</th>\n",
" <td>137</td>\n",
" <td>45.68</td>\n",
" <td>283</td>\n",
" <td>453.7</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>no</td>\n",
" <td>0.0</td>\n",
" <td>408-328-2110</td>\n",
" <td>OH</td>\n",
" <td>no</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
"<p>5 rows × 11 columns</p>"
],
"text/plain": [
" account_length call_charges call_count \\\n",
"NCZgNKO6O7FR7IL7QvUWYwwAAAAAAAAA 105 55.31 308 \n",
"NJB4UinRN1KuQXeUp8N0_gkAAAAAAAAA 52 51.97 262 \n",
"NACawapaqeWu6A369Ao35EIAAAAAAAAA 80 30.32 268 \n",
"ND4QlLEAv1kpCLfHPKC7BoIAAAAAAAAA 118 60.48 224 \n",
"NK6_HrQbovr8uM7AoXuezRAAAAAAAAAA 137 45.68 283 \n",
"\n",
" call_duration churn \\\n",
"NCZgNKO6O7FR7IL7QvUWYwwAAAAAAAAA 589.5 0 \n",
"NJB4UinRN1KuQXeUp8N0_gkAAAAAAAAA 588.5 0 \n",
"NACawapaqeWu6A369Ao35EIAAAAAAAAA 384.9 0 \n",
"ND4QlLEAv1kpCLfHPKC7BoIAAAAAAAAA 626.3 0 \n",
"NK6_HrQbovr8uM7AoXuezRAAAAAAAAAA 453.7 0 \n",
"\n",
" customer_service_calls international_plan \\\n",
"NCZgNKO6O7FR7IL7QvUWYwwAAAAAAAAA 4.0 no \n",
"NJB4UinRN1KuQXeUp8N0_gkAAAAAAAAA 1.0 no \n",
"NACawapaqeWu6A369Ao35EIAAAAAAAAA 3.0 no \n",
"ND4QlLEAv1kpCLfHPKC7BoIAAAAAAAAA 4.0 no \n",
"NK6_HrQbovr8uM7AoXuezRAAAAAAAAAA 0.0 no \n",
"\n",
" number_vmail_messages phone_number state \\\n",
"NCZgNKO6O7FR7IL7QvUWYwwAAAAAAAAA 0.0 408-327-6764 NE \n",
"NJB4UinRN1KuQXeUp8N0_gkAAAAAAAAA 31.0 408-327-9289 OH \n",
"NACawapaqeWu6A369Ao35EIAAAAAAAAA 0.0 408-327-9957 DC \n",
"ND4QlLEAv1kpCLfHPKC7BoIAAAAAAAAA 0.0 408-328-1222 CO \n",
"NK6_HrQbovr8uM7AoXuezRAAAAAAAAAA 0.0 408-328-2110 OH \n",
"\n",
" voice_mail_plan \n",
"NCZgNKO6O7FR7IL7QvUWYwwAAAAAAAAA no \n",
"NJB4UinRN1KuQXeUp8N0_gkAAAAAAAAA yes \n",
"NACawapaqeWu6A369Ao35EIAAAAAAAAA no \n",
"ND4QlLEAv1kpCLfHPKC7BoIAAAAAAAAA no \n",
"NK6_HrQbovr8uM7AoXuezRAAAAAAAAAA no \n",
"\n",
"[5 rows x 11 columns]"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_churn = ed.DataFrame(es_client, 'churn')\n",
"\n",
"df_churn.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create classification model to predict customer churn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cleanup old analysis/jobs"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"ret = es_client.indices.delete(index='churn_analysis', ignore=[400, 404])\n",
"ret = es_client.ml.stop_data_frame_analytics(id='churn_analysis', ignore=[400, 404])\n",
"ret = es_client.ml.delete_data_frame_analytics(id='churn_analysis', ignore=[400, 404])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create ML regression analysis job"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ObjectApiResponse({'id': 'churn_analysis', 'create_time': 1644260656156, 'version': '8.0.0', 'description': '', 'source': {'index': ['churn'], 'query': {'match_all': {}}}, 'dest': {'index': 'churn_analysis', 'results_field': 'ml'}, 'analysis': {'classification': {'dependent_variable': 'churn', 'num_top_feature_importance_values': 5, 'class_assignment_objective': 'maximize_minimum_recall', 'num_top_classes': 2, 'prediction_field_name': 'churn_prediction', 'training_percent': 80.0, 'randomize_seed': 1, 'early_stopping_enabled': True}}, 'analyzed_fields': {'includes': [], 'excludes': []}, 'model_memory_limit': '300mb', 'allow_lazy_start': False, 'max_num_threads': 1})"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_config = {\n",
" \"description\": \"\",\n",
" \"source\": {\n",
" \"index\": \"churn\"\n",
" },\n",
" \"dest\": {\n",
" \"index\": \"churn_analysis\"\n",
" },\n",
" \"analyzed_fields\": {\n",
" \"excludes\": []\n",
" },\n",
" \"analysis\": {\n",
" \"classification\": {\n",
" \"dependent_variable\": \"churn\",\n",
" \"num_top_feature_importance_values\": 5,\n",
" \"training_percent\": 80,\n",
" \"randomize_seed\": 1\n",
" }\n",
" },\n",
" \"model_memory_limit\": \"300mb\"\n",
"}\n",
"\n",
"es_client.ml.put_data_frame_analytics(id='churn_analysis', body=analysis_config)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Start ML job and monitor progress"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2022-02-07 19:04:17.478732 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",
"2022-02-07 19:04:22.579840 started reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:26 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2022-02-07 19:04:27.680743 started reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:67 fine_tuning_parameters:0 final_training:0 writing_results:0 inference:0 \n",
"2022-02-07 19:04:32.782875 started 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",
"2022-02-07 19:04:37.885409 started 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",
"2022-02-07 19:04:42.992936 started reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:28 final_training:0 writing_results:0 inference:0 \n",
"2022-02-07 19:04:48.094549 started 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",
"2022-02-07 19:04:53.196949 started reindexing:100 loading_data:100 feature_selection:100 coarse_parameter_search:100 fine_tuning_parameters:100 final_training:100 writing_results:100 inference:98 \n",
"2022-02-07 19:04:58.304464 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(id='churn_analysis')\n",
"\n",
"while True:\n",
" results = es_client.ml.get_data_frame_analytics_stats(id='churn_analysis')\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(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Explore classification results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Evaluate results"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"evaluate_config = {\n",
" \"index\": \"churn_analysis\",\n",
" \"query\": {\n",
" \"bool\": {\n",
" \"filter\": [\n",
" {\n",
" \"term\": {\n",
" \"ml.is_training\": False\n",
" }\n",
" }\n",
" ]\n",
" }\n",
" },\n",
" \"evaluation\": {\n",
" \"classification\": {\n",
" \"actual_field\": \"churn\",\n",
" \"predicted_field\": \"ml.churn_prediction\",\n",
" \"metrics\": {\n",
" \"accuracy\": {}\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"evaluate_results = es_client.ml.evaluate_data_frame(body=evaluate_config)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overall accuracy 91.1544227886057%\n"
]
}
],
"source": [
"print(\"Overall accuracy {}%\".format(evaluate_results['classification']['accuracy']['overall_accuracy']*100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Explore most important features"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"df_churn_analysis = ed.DataFrame(es_client, 'churn_analysis')"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"# Convert eland.DataFrame to pandas to summarise feature importance\n",
"pd_df = ed.eland_to_pandas(df_churn_analysis[df_churn_analysis.churn=='1']['ml.feature_importance'])\n",
"results = list()\n",
"for row in pd_df.items():\n",
" for features in row[1]:\n",
" result0 = dict()\n",
" result0['feature_name'] = features['feature_name']\n",
" result0['class_name'] = features['classes'][0]['class_name']\n",
" result0['importance'] = features['classes'][0]['importance']\n",
" results.append(result0)\n",
"\n",
" result1 = dict()\n",
" result1['feature_name'] = features['feature_name']\n",
" result1['class_name'] = features['classes'][1]['class_name']\n",
" result1['importance'] = features['classes'][1]['importance']\n",
" results.append(result1)\n",
"\n",
"pd_df = pd.DataFrame(results)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"feature_name\n",
"call_count 19.734349\n",
"account_length 19.883474\n",
"state 24.806856\n",
"number_vmail_messages 67.342942\n",
"voice_mail_plan 78.428761\n",
"call_duration 124.958134\n",
"international_plan 247.432267\n",
"customer_service_calls 503.962450\n",
"call_charges 943.362646\n",
"Name: importance, dtype: float64"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd_df[pd_df['class_name']==1].groupby('feature_name')['importance'].sum().sort_values()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This shows that the most important features in terms of *why* customers churn are:\n",
"\n",
"* High *call_charges* \n",
"* High number of *customer_service_calls*\n",
"* If the customer is on an *international_plan* they are more likely to churn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can validate this, buy looking at the distribution of these values for churn/not churn:"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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5kp6X1CZpSZFySbo1KX9G0sxkfr2kJyQ9LWmzpOsL2kyStEbSi8nr0ZXbLDMzG8iACSB5nONtwFzgeOBCScf3qDYXmJEMi4Dbk/ldwGcj4kSgGZiTPDMYYAmwNiJmAGuTaTMzq5I0RwCnAm0R8VJE7AdWAPN71JkP3BV5jwMTJU1JpjuSOmOSIQra3JmM3wmcW8Z2mJnZICn/ON9+KkjnA3Mi4mvJ9EXAaRGxuKDOKmBpRDyaTK8Fro2IJ5MjiA3AR4HbIuLapM7rETGxYBl/iIhe3UCSFpE/qqChoeHkFStWlLShHR0djBs3rqS21eQ4KyttnLv2dVUhmr6NOdTF24cdUfHlHju+ssss+n7u+11F15Ha+A/2WTTa/j/LNXv27A0R0dJzfpqHwqvIvJ5Zo886EXEQaJY0EfiVpE9ExKYU6yVpvxxYDtDS0hKtra1pm75HLpej1LbV5DgrK22ct6x5YeiD6Udj51Z21E+v+HIvaP3jii6v6Pu57saKriO11gV9Fo22/8+hkqYLqB2YWjDdBLwy2DoR8TqQA+Yks16VNAUged2VNmgzMytfmgSwHpghabqkscACYGWPOiuBi5OrgWYBeyNip6QPJN/8kfQ+4HPAcwVtFibjC4EHytsUMzMbjAG7gCLigKTFwENAHXBHRGyWdEVSvgxYDcwD2oC3gEuT5lOAO5PzAIcB90bEqqRsKXCvpMuBbcCXKrdZZmY2kDTnAIiI1eR38oXzlhWMB3BlkXbPACf1sczdwJmDCdaypRL98o2dXcPev29Wq/xLYDOzjHICMDPLKCcAM7OMcgIwM8soJwAzs4xyAjAzyygnADOzjHICMDPLKCcAM7OMcgIwM8soJwAzs4xyAjAzyygnADOzjHICMDPLKCcAM7OMcgIwM8uoVAlA0hxJz0tqk7SkSLkk3ZqUPyNpZjJ/qqR1krZI2izpqoI210naIWljMsyr3GaZmdlABnwiWPI4x9uAs8g//H29pJUR8WxBtbnAjGQ4Dbg9eT0AXBMRT0kaD2yQtKag7S0RcXPlNsfMzNJKcwRwKtAWES9FxH5gBTC/R535wF2R9zgwUdKUiNgZEU8BRMQ+YAvQWMH4zcysRMo/zrefCtL5wJyI+FoyfRFwWkQsLqizClgaEY8m02uBayPiyYI604BHgE9ExBuSrgMuAd4AniR/pPCHIutfBCwCaGhoOHnFihUlbWhHRwfjxo0rqW01Oc537drXVfYyxhzq4u3DjqhANENrqOI8dnxll1n0777vdxVdR2rjP9hnkT9H7zV79uwNEdHSc36ah8KryLyeWaPfOpLGAb8EvhkRbySzbwduSOrdAHwfuKzXQiKWA8sBWlpaorW1NUXIveVyOUptW02O812VeSj8VnbUT69ANENrqOK8oPWPK7q8on/3dTdWdB2ptS7os8ifo3TSdAG1A1MLppuAV9LWkTSG/M7/ZxFxf3eFiHg1Ig5GxCHgx+S7mszMrErSJID1wAxJ0yWNBRYAK3vUWQlcnFwNNAvYGxE7JQn4CbAlIn5Q2EDSlILJ84BNJW+FmZkN2oBdQBFxQNJi4CGgDrgjIjZLuiIpXwasBuYBbcBbwKVJ808CFwH/LmljMu/bEbEauElSM/kuoJeBr1dom8zMLIU05wBIdtire8xbVjAewJVF2j1K8fMDRMRFg4rUzMwqKlUCMCtLiScJZ23bXdZqH//worLam412TgA2as3atpw9E5qZtWtNVdfrxGMjhe8FZGaWUU4AZmYZ5QRgZpZRTgBmZhnlBGBmllFOAGZmGeUEYGaWUU4AZmYZ5QRgZpZRTgBmZhnlBGBmllFOAGZmGeUEYGaWUakSgKQ5kp6X1CZpSZFySbo1KX9G0sxk/lRJ6yRtkbRZ0lUFbSZJWiPpxeT16MptlpmZDWTA20FLqgNuA84i/+zf9ZJWRsSzBdXmAjOS4TTyD3w/DTgAXBMRT0kaD2yQtCZpuwRYGxFLk6SyBLi2gttmlnm3rHmhostr7Ozqtczu5zacftzkiq7Lhl6aI4BTgbaIeCki9gMrgPk96swH7oq8x4GJkqZExM6IeAogIvYBW4DGgjZ3JuN3AueWtylmZjYYaRJAI7C9YLqdd3fiqetImgacBPxbMqshInYCJK/Hpo7azMzKluaJYMWe6RuDqSNpHPBL4JsR8Ub68EDSImARQENDA7lcbjDN39HR0VFy22oalXF2TC9pHW9OmFpSu0IH6o5kz4TmspczGI2dWwfdZsyhrpLaVVuxOLvf31xHlR8w2M//36j8HA2BNH+xdqDwk9gEvJK2jqQx5Hf+P4uI+wvqvNrdTSRpCrCr2MojYjmwHKClpSVaW1tThNxbLpej1LbVNCrjLPGZwL/ZVd4zgSG/c5q0d2PZyxmMF0p4JGRj51Z21JeWKKupWJzdj9ys+jmA1gV9Fo3Kz9EQSNMFtB6YIWm6pLHAAmBljzorgYuTq4FmAXuTHbuAnwBbIuIHRdosTMYXAg+UvBVmZjZoAx4BRMQBSYuBh4A64I6I2CzpiqR8GbAamAe0AW8BlybNPwlcBPy7pI3JvG9HxGpgKXCvpMuBbcCXKrZVZmY2oFSddskOe3WPecsKxgO4ski7Ryl+foCI2A2cOZhgzcyscvxLYDOzjHICMDPLKCcAM7OMcgIwM8soJwAzs4xyAjAzyygnADOzjHICMDPLKCcAM7OMcgIwM8soJwAzs4xyAjAzyygnADOzjHICMDPLKCcAM7OMcgIwM8uoVAlA0hxJz0tqk7SkSLkk3ZqUPyNpZkHZHZJ2SdrUo811knZI2pgM88rfHDMzS2vAJ4JJqgNuA84i//D39ZJWRsSzBdXmAjOS4TTg9uQV4KfAj4C7iiz+loi4ueTorepuWfMCAI2dXe+MD2TWtvIf7m5mlZfmCOBUoC0iXoqI/cAKYH6POvOBuyLvcWCipCkAEfEIsKeSQZuZWfnSPBO4EdheMN3Ou9/u+6vTCOwcYNmLJV0MPAlcExF/6FlB0iJgEUBDQwO5XC5FyL11dHSU3Laaaj3Oxs4uAMYc6qKxc2uqNnsmNA9hRP07UHdk1def9n0pNJj3czgVi7P7/c11pHrEeOX08zmp9c9Rt+GOM81frNhD3aOEOj3dDtyQ1LsB+D5wWa+FRCwHlgO0tLREa2vrAIstLpfLUWrbaqr1ON/tAtrKjvrpqdrM2rVmKEPq154JzUzau7Gq63zhw4sG3WYw7+dwKhZn99/39OMmVzeY1gV9FtX656jbcMeZpguoHZhaMN0EvFJCnfeIiFcj4mBEHAJ+TL6ryczMqiRNAlgPzJA0XdJYYAGwskedlcDFydVAs4C9EdFv90/3OYLEecCmvuqamVnlDdgFFBEHJC0GHgLqgDsiYrOkK5LyZcBqYB7QBrwFXNrdXtI9QCtwjKR24LsR8RPgJknN5LuAXga+XrnNMjOzgaQ6axMRq8nv5AvnLSsYD+DKPtpe2Mf8i9KHaWZmlVbl0/Zmo9+sbcsH3WbPhOayT5Y/XsLJZ8s23wrCzCyjnADMzDLKCcDMLKN8DsDMRqZ1N/Zd1jG9//JyzP7W0Cx3GPgIwMwso5wAzMwyyl1AZqNEKZefDlYlLle12uEjADOzjHICMDPLKCcAM7OMcgIwM8soJwAzs4xyAjAzyygnADOzjHICMDPLqFQJQNIcSc9LapO0pEi5JN2alD8jaWZB2R2Sdkna1KPNJElrJL2YvB5d/uaYmVlaAyYASXXAbcBc4HjgQknH96g2F5iRDIuA2wvKfgrMKbLoJcDaiJgBrE2mzcysStIcAZwKtEXESxGxH1gBzO9RZz5wV+Q9Dkzsfuh7RDwC7Cmy3PnAncn4ncC5JcRvZmYlSnMvoEZge8F0O3BaijqNwM5+ltsQETsBImKnpGOLVZK0iPxRBQ0NDeRyuRQh99bR0VFy22qq9TgbO7sAGHOoi8bOrana7JnQPIQR9e9A3ZHDuv60RkOcuY7aubVYx6EjyHVMH5qFV/DzOdyf9zR/MRWZFyXUKUlELAeWA7S0tERra2tJy8nlcpTatppqPc5b1rwAQGPnVnbUp/uADefNw/ZMaGbS3o3Dtv60RkOcpx83ubrB9CPXMZ3Wcem+oAxa64KKLWq4P+9puoDagakF003AKyXU6enV7m6i5HVXiljMzKxC0iSA9cAMSdMljQUWACt71FkJXJxcDTQL2NvdvdOPlcDCZHwh8MAg4jYzszINmAAi4gCwGHgI2ALcGxGbJV0h6Yqk2mrgJaAN+DHwF93tJd0D/Ab4mKR2SZcnRUuBsyS9CJyVTJuZWZWkOmsTEavJ7+QL5y0rGA/gyj7aXtjH/N3AmakjNTOzivIvgc3MMsoJwMwso5wAzMwyygnAzCyjnADMzDLKCcDMLKOcAMzMMsoJwMwso5wAzMwyygnAzCyjnADMzDLKCcDMLKNq5xE+NvTW3Vj2ImZt2w3kHwwynA96MbPy+QjAzCyjnADMzDLKXUAjXPczetPo7r4xM4OURwCS5kh6XlKbpCVFyiXp1qT8GUkzB2or6TpJOyRtTIZ5ldkkMzNLY8AEIKkOuA2YCxwPXCjp+B7V5gIzkmERcHvKtrdERHMyrMbMzKomzRHAqUBbRLwUEfuBFcD8HnXmA3dF3uPARElTUrY1M7NhkOYcQCOwvWC6HTgtRZ3GFG0XS7oYeBK4JiL+0HPlkhaRP6qgoaGBXC6XIuTeOjo6Sm5bTYONs7GzK3XdPROaBx9QHw7UHVnR5Q0Vx1lZ/cWZ66idU4odh44g1zF9aBZewf3IcO+X0vzFVGRepKzTX9vbgRuS6RuA7wOX9aocsRxYDtDS0hKtra0pQu4tl8tRattqGmycgzoJXMHr9vdMaGbS3o0VW95QcZyV1V+cpx83ubrB9CPXMZ3WcVuHZuGtCyq2qOHeL6VJAO3A1ILpJuCVlHXG9tU2Il7tninpx8Cq1FGbmVnZ0pwDWA/MkDRd0lhgAbCyR52VwMXJ1UCzgL0RsbO/tsk5gm7nAZvK3BYzMxuEAY8AIuKApMXAQ0AdcEdEbJZ0RVK+DFgNzAPagLeAS/trmyz6JknN5LuAXga+XsHtMjOzAaQ6a5Ncorm6x7xlBeMBXJm2bTL/okFFamZmFeVbQZiZZZQTgJlZRjkBmJllVO38csPMRrTfvFS9mw3W0m8ORjIfAZiZZZQTgJlZRrkLyMxsMCrwaNV3dExPv7zZ36rcehM+AjAzyygnADOzjHIX0BAYzB06e2rs7CqrvZlZWj4CMDPLKCcAM7OMchfQMJi1bXmfZXsmNFf0wS1mZn3JdgKo5OVcBWZtq94vIs3MSuUuIDOzjHICMDPLqFRdQJLmAD8k/1Svf4iIpT3KlZTPI/9EsEsi4qn+2kqaBPwcmEb+iWAXRMQfyt8kMxvtBrrx3JsTpvKbXZXpih3NN54bMAFIqgNuA84i//D39ZJWRsSzBdXmAjOS4TTgduC0AdouAdZGxFJJS5Lpayu3ae+1a1/v6+vdV29mWZamC+hUoC0iXoqI/cAKYH6POvOBuyLvcWBi8tD3/trOB+5Mxu8Ezi1vU8zMbDDSdAE1AtsLptvJf8sfqE7jAG0bImInQETslHRssZVLWgQsSiY7JD2fIuZijgF+X2LbanKcleU4K8txVtYg4vx2Oev5SLGZaRKAisyLlHXStO1XRCwH+r5wPiVJT0ZES7nLGWqOs7IcZ2U5zsoa7jjTdAG1A1MLppuAV1LW6a/tq0k3EcnrrvRhm5lZudIkgPXADEnTJY0FFgAre9RZCVysvFnA3qR7p7+2K4GFyfhC4IEyt8XMzAZhwC6giDggaTHwEPlLOe+IiM2SrkjKlwGryV8C2kb+MtBL+2ubLHopcK+ky4FtwJcqumW9ld2NVCWOs7IcZ2U5zsoa1jgVMagueTMzGyX8S2Azs4xyAjAzy6hRmQAkTZW0TtIWSZslXZXMnyRpjaQXk9ejhznOeklPSHo6ifP6WowzialO0m8lrarhGF+W9O+SNkp6sobjnCjpPknPJf+jp9danJI+lryP3cMbkr5Za3EmsV6dfH42Sbon+VzVYpxXJTFulvTNZN6wxjkqEwBwALgmIv4UmAVcKel43r39xAxgbTI9nLqAz0bEiUAzMCe5iqrW4gS4CthSMF2LMQLMjojmgmurazHOHwIPRsSfACeSf19rKs6IeD55H5uBk8lf3PEraixOSY3AfwVaIuIT5C82WUDtxfkJ4L+QvzvCicDZkmYw3HFGxKgfyF9iehbwPDAlmTcFeH64YyuI8UjgKfK/lK6pOMn/fmMt8FlgVTKvpmJM4ngZOKbHvJqKE3g/sJXkAoxajbNHbJ8H/m8txsm7dxuYRP6qxlVJvLUW55fI3wyze/p/AH813HGO1iOAd0iaBpwE/Bs9bj8BFL39RDUlXSsbyf8Qbk1E1GKcf0/+n/VQwbxaixHyvzL/taQNyS1EoPbiPA54DfhfSZfaP0g6itqLs9AC4J5kvKbijIgdwM3kLyXfSf43SL+mxuIENgGfljRZ0pHkL5ufyjDHOaoTgKRxwC+Bb0bEG8MdTzERcTDyh9lNwKnJoWLNkHQ2sCsiNgx3LCl8MiJmkr877ZWSPj3cARVxODATuD0iTgLepDa6pYpKfsB5DvCL4Y6lmKTPfD4wHfgQcJSkrw5vVL1FxBbg74A1wIPA0+S7qofVqE0AksaQ3/n/LCLuT2bX7O0nIuJ1IAfMobbi/CRwjqSXyd/N9bOS7qa2YgQgIl5JXneR768+ldqLsx1oT470AO4jnxBqLc5uc4GnIuLVZLrW4vwcsDUiXouIt4H7gTOovTiJiJ9ExMyI+DSwB3iRYY5zVCYASQJ+AmyJiB8UFNXU7SckfUDSxGT8feT/mZ+jhuKMiG9FRFNETCPfFfBwRHyVGooRQNJRksZ3j5PvB95EjcUZEb8Dtkv6WDLrTOBZaizOAhfybvcP1F6c24BZko5MPvdnkj+pXmtxouSOx5I+DHyR/Ps6vHEO54mRITzh8iny/cHPABuTYR4wmfzJzBeT10nDHOcJwG+TODcBf53Mr6k4C+Jt5d2TwDUVI/m+9aeTYTPwnVqMM4mpGXgy+bv/E3B0jcZ5JLAbmFAwrxbjvJ78F6dNwD8CR9RonP+HfLJ/GjizFt5P3wrCzCyjRmUXkJmZDcwJwMwso5wAzMwyygnAzCyjnADMzDLKCcDMLKOcAMzMMur/A3D4wDWRfdApAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"feature = 'call_charges'\n",
"df_churn_analysis[df_churn_analysis.churn=='0'][feature].hist(density=True, alpha=0.5)\n",
"df_churn_analysis[df_churn_analysis.churn=='1'][feature].hist(density=True, alpha=0.5)\n",
"plt.title(feature)\n",
"plt.legend(['churn=0','churn=1'])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"feature = 'customer_service_calls'\n",
"df_churn_analysis[df_churn_analysis.churn=='0'][feature].hist(density=True, alpha=0.5)\n",
"df_churn_analysis[df_churn_analysis.churn=='1'][feature].hist(density=True, alpha=0.5)\n",
"plt.title(feature)\n",
"plt.legend(['churn=0','churn=1'])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The histograms above show different distributions for churn/no churn indicating that visually data shows the feature has an influence on churn.\n",
"\n",
"Now plot a feature that doesn't have high 'feature_importance'"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"feature = 'account_length'\n",
"df_churn_analysis[df_churn_analysis.churn=='0'][feature].hist(density=True, alpha=0.5)\n",
"df_churn_analysis[df_churn_analysis.churn=='1'][feature].hist(density=True, alpha=0.5)\n",
"plt.title(feature)\n",
"plt.legend(['churn=0','churn=1'])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The histogram above shows very similar distributions for churn/no churn indicating the data shows that 'account_length' has little influence on churn."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Use Model to Infer Results"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"def get_model_id(job_name):\n",
" response = es_client.ml.get_trained_models(\n",
" model_id=f\"{job_name}-*\"\n",
" )\n",
" \n",
" if response['count']!=1:\n",
" raise ValueError(response)\n",
" \n",
" model_id = response['trained_model_configs'][0]['model_id']\n",
" \n",
" return model_id\n",
"\n",
"def predict(model_id, doc):\n",
" predict_body = {\n",
" \"pipeline\": {\n",
" \"processors\": [\n",
" {\n",
" \"inference\": {\n",
" \"model_id\": model_id,\n",
" \"inference_config\": {\n",
" \"classification\": {\n",
" \"num_top_classes\": 2\n",
" }\n",
" },\n",
" \"field_map\": {\n",
" }\n",
" }\n",
" }\n",
" ]\n",
" },\n",
" \"docs\": [doc]\n",
"}\n",
" response = es_client.ingest.simulate(\n",
" body=predict_body\n",
" )\n",
" \n",
" return response"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'churn_analysis-1644260690180'"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_id = get_model_id('churn_analysis')\n",
"\n",
"model_id"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ObjectApiResponse({'docs': [{'doc': {'_index': '_index', '_id': '_id', '_source': {'number_vmail_messages': 15, 'account_length': 119, 'call_charges': 48.71000000000003, 'customer_service_calls': 5, 'call_duration': 451.8000000000003, 'voice_mail_plan': 'yes', 'call_count': 312, 'phone_number': '415-352-5118', 'international_plan': 'yes', 'state': 'NM', 'ml': {'inference': {'top_classes': [{'class_name': 1, 'class_probability': 0.9257330639262756, 'class_score': 0.9257330639262756}, {'class_name': 0, 'class_probability': 0.07426693607372437, 'class_score': 0.010181118370508155}], 'prediction_score': 0.9257330639262756, 'model_id': 'churn_analysis-1644260690180', 'churn_prediction': 1, 'prediction_probability': 0.9257330639262756, 'feature_importance': [{'feature_name': 'customer_service_calls', 'classes': [{'class_name': 0, 'importance': -3.2516961792308483}, {'class_name': 1, 'importance': 3.2516961792308483}]}, {'feature_name': 'call_charges', 'classes': [{'class_name': 0, 'importance': -1.1395578577273409}, {'class_name': 1, 'importance': 1.1395578577273409}]}, {'feature_name': 'international_plan', 'classes': [{'class_name': 0, 'importance': -0.8232603592682481}, {'class_name': 1, 'importance': 0.8232603592682481}]}, {'feature_name': 'account_length', 'classes': [{'class_name': 0, 'importance': -0.10347812658958981}, {'class_name': 1, 'importance': 0.10347812658958981}]}, {'feature_name': 'call_duration', 'classes': [{'class_name': 0, 'importance': -0.10199431077403041}, {'class_name': 1, 'importance': 0.10199431077403041}]}]}}}, '_ingest': {'timestamp': '2022-02-07T19:11:42.720630776Z'}}}]})"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"doc = {\n",
" \"_source\": {\n",
" \"voice_mail_plan\": \"yes\",\n",
" \"call_count\": 312,\n",
" \"number_vmail_messages\": 15,\n",
" \"account_length\": 119,\n",
" \"phone_number\": \"415-352-5118\",\n",
" \"international_plan\": \"yes\",\n",
" \"state\": \"NM\",\n",
" \"call_charges\": 48.71000000000003,\n",
" \"customer_service_calls\": 5,\n",
" \"call_duration\": 451.8000000000003\n",
" }\n",
" }\n",
"\n",
"predict(model_id, doc)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ObjectApiResponse({'docs': [{'doc': {'_index': '_index', '_id': '_id', '_source': {'number_vmail_messages': 15, 'account_length': 119, 'call_charges': 48.71000000000003, 'customer_service_calls': 0, 'call_duration': 451.8000000000003, 'voice_mail_plan': 'yes', 'call_count': 312, 'phone_number': '415-352-5118', 'international_plan': 'no', 'state': 'NM', 'ml': {'inference': {'top_classes': [{'class_name': 0, 'class_probability': 0.9818461798810852, 'class_score': 0.13459949618868494}, {'class_name': 1, 'class_probability': 0.01815382011891471, 'class_score': 0.01815382011891471}], 'prediction_score': 0.13459949618868494, 'model_id': 'churn_analysis-1644260690180', 'churn_prediction': 0, 'prediction_probability': 0.9818461798810852, 'feature_importance': [{'feature_name': 'customer_service_calls', 'classes': [{'class_name': 0, 'importance': 0.4188916029842075}, {'class_name': 1, 'importance': -0.4188916029842075}]}, {'feature_name': 'call_duration', 'classes': [{'class_name': 0, 'importance': 0.2899166329886114}, {'class_name': 1, 'importance': -0.2899166329886114}]}, {'feature_name': 'international_plan', 'classes': [{'class_name': 0, 'importance': 0.2515285070732366}, {'class_name': 1, 'importance': -0.2515285070732366}]}, {'feature_name': 'voice_mail_plan', 'classes': [{'class_name': 0, 'importance': 0.09262890957496756}, {'class_name': 1, 'importance': -0.09262890957496756}]}, {'feature_name': 'call_charges', 'classes': [{'class_name': 0, 'importance': 0.07301274831374663}, {'class_name': 1, 'importance': -0.07301274831374663}]}]}}}, '_ingest': {'timestamp': '2022-02-07T19:11:43.249158785Z'}}}]})"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"doc = {\n",
" \"_source\": {\n",
" \"voice_mail_plan\": \"yes\",\n",
" \"call_count\": 312,\n",
" \"number_vmail_messages\": 15,\n",
" \"account_length\": 119,\n",
" \"phone_number\": \"415-352-5118\",\n",
" \"international_plan\": \"no\",\n",
" \"state\": \"NM\",\n",
" \"call_charges\": 48.71000000000003,\n",
" \"customer_service_calls\": 0,\n",
" \"call_duration\": 451.8000000000003\n",
" }\n",
" }\n",
"\n",
"predict(model_id, doc)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Display the source blob
Display the rendered blob
Raw
energy_price_forecasting.ipynb
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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
{"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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