Saturday, May 16, 2020

KNN Machine learning Algorithm on ElasticSearch

Untitled

KNN Machine learning Algorithm on ElasticSearch

Step 1

Import the library

In [1]:
try:
    import elasticsearch
    from elasticsearch import Elasticsearch
    
    import pandas as pd
    import json
    from ast import literal_eval
    from tqdm import tqdm
    import datetime
    import os
    import sys
    import numpy as np
    
    from elasticsearch import helpers
    print("Loaded  .. . . . . . . .")
except Exception as E:
    print("Some Modules are Missing {} ".format(e))

Loaded  .. . . . . . . .
In [21]:
ENDPOINT = "http://localhost:9200"
In [29]:
es = Elasticsearch(timeout=600,hosts=ENDPOINT)
es.ping()
Out[29]:
True

Step 2:

  • Preprocessing

Reading the Dataset

In [4]:
os.listdir()
Out[4]:
['.ipynb_checkpoints', 'netflix_titles.csv', 'Untitled.ipynb']
In [5]:
df= pd.read_csv("netflix_titles.csv")
In [7]:
df.head(1)
Out[7]:
show_id type title director cast country date_added release_year rating duration listed_in description
0 81145628 Movie Norm of the North: King Sized Adventure Richard Finn, Tim Maltby Alan Marriott, Andrew Toth, Brian Dobson, Cole... United States, India, South Korea, China September 9, 2019 2019 TV-PG 90 min Children & Family Movies, Comedies Before planning an awesome wedding for his gra...
In [8]:
titles = df["title"].to_list()
In [9]:
len(titles)
Out[9]:
6234

Step 3:

Convert the Title into Vector using Google Pre trained Machine Learning Model

In [10]:
import tensorflow as tf
import tensorflow_hub as hub

module_url = "https://tfhub.dev/google/nnlm-en-dim128/2"
embed = hub.KerasLayer(module_url)

vector = []

for c, title in enumerate(titles):
    x = tf.constant([title])
    embeddings = embed(x)
    x = np.asarray(embeddings)
    x = x[0].tolist()
    vector.append(x)
In [11]:
len(vector)
Out[11]:
6234
In [12]:
vector[0]
Out[12]:
[0.08594007790088654,
 -0.09169773757457733,
 -0.08221833407878876,
 0.1603367030620575,
 0.05244443565607071,
 0.11267174780368805,
 -0.08382084965705872,
 0.09882047772407532,
 0.021728241816163063,
 -0.18144601583480835,
 0.0012927550124004483,
 0.030685214325785637,
 -0.04533662274479866,
 -0.07281118631362915,
 -0.11955679953098297,
 0.017013853415846825,
 0.033623743802309036,
 -0.009736376814544201,
 0.033763282001018524,
 0.1921098679304123,
 0.00620125001296401,
 0.015555041842162609,
 0.06574436277151108,
 0.11323074996471405,
 -0.10774067789316177,
 0.1693897843360901,
 -0.13922490179538727,
 -0.10309454798698425,
 -5.2244857215555385e-05,
 0.023089049383997917,
 0.04559335112571716,
 -0.10510903596878052,
 -0.1005614772439003,
 -0.07881765812635422,
 0.025743374601006508,
 -0.05974612385034561,
 -0.1747055947780609,
 -0.05892287939786911,
 -0.06596986949443817,
 -0.09151236712932587,
 0.03593139722943306,
 -0.07345644384622574,
 -0.018012331798672676,
 0.036221787333488464,
 0.07314501702785492,
 -0.06195896118879318,
 -0.0023348417598754168,
 -0.1982719600200653,
 -0.3291093707084656,
 0.006821473129093647,
 0.1486814171075821,
 0.2550199031829834,
 0.1663597822189331,
 0.15605349838733673,
 0.12756910920143127,
 -0.057475071400403976,
 0.14456160366535187,
 -0.05416375771164894,
 0.06393317133188248,
 -0.08582285046577454,
 0.019529936835169792,
 0.030426720157265663,
 -0.13159017264842987,
 -0.01176383811980486,
 -0.05212199687957764,
 -0.007775180973112583,
 0.0005310662090778351,
 0.03532465547323227,
 0.14036867022514343,
 -0.04217003658413887,
 -0.0504852756857872,
 0.08859632164239883,
 0.02489238791167736,
 0.036609407514333725,
 0.012656561098992825,
 -0.031059175729751587,
 0.13535012304782867,
 -0.07467728853225708,
 -0.00639297952875495,
 -0.007216154597699642,
 0.10756982862949371,
 -0.03459356725215912,
 0.05434964969754219,
 0.10563021898269653,
 -0.023835688829421997,
 -0.1384897232055664,
 -0.10662095248699188,
 -0.11560706794261932,
 -0.018126854673027992,
 -0.11542601138353348,
 0.05233073979616165,
 -0.08457083255052567,
 0.04891547933220863,
 0.048610806465148926,
 -0.0861951932311058,
 -0.1646905094385147,
 0.05879170447587967,
 -0.09346245974302292,
 0.21104931831359863,
 0.07167480885982513,
 0.09941790252923965,
 -0.04874766618013382,
 -0.11821635812520981,
 -0.11691499501466751,
 -0.04042290896177292,
 -0.035517025738954544,
 0.006470585707575083,
 0.07046835869550705,
 0.032006461173295975,
 -0.017604319378733635,
 0.1958240568637848,
 0.01993837021291256,
 -0.01663972996175289,
 0.11849723011255264,
 -0.10080186277627945,
 -0.009301570244133472,
 0.03264541178941727,
 -0.03453604504466057,
 -0.032728590071201324,
 -0.06038405001163483,
 -0.014748498797416687,
 -0.08714324235916138,
 0.0329294428229332,
 -0.04497246816754341,
 -0.0888349711894989,
 0.02692333422601223,
 0.18709281086921692,
 -0.002944737207144499]

Step 4:

Creating documents
In [13]:
requests = []
for i, doc in enumerate(titles):
    request = {}
    request["_op_type"] = "index"
    request["_index"] = "myml"
    request["_id"] = i
    request["title"] = doc
    request["title_vector"] = vector[i]
    requests.append(request)
In [14]:
requests[0]
Out[14]:
{'_op_type': 'index',
 '_index': 'myml',
 '_id': 0,
 'title': 'Norm of the North: King Sized Adventure',
 'title_vector': [0.08594007790088654,
  -0.09169773757457733,
  -0.08221833407878876,
  0.1603367030620575,
  0.05244443565607071,
  0.11267174780368805,
  -0.08382084965705872,
  0.09882047772407532,
  0.021728241816163063,
  -0.18144601583480835,
  0.0012927550124004483,
  0.030685214325785637,
  -0.04533662274479866,
  -0.07281118631362915,
  -0.11955679953098297,
  0.017013853415846825,
  0.033623743802309036,
  -0.009736376814544201,
  0.033763282001018524,
  0.1921098679304123,
  0.00620125001296401,
  0.015555041842162609,
  0.06574436277151108,
  0.11323074996471405,
  -0.10774067789316177,
  0.1693897843360901,
  -0.13922490179538727,
  -0.10309454798698425,
  -5.2244857215555385e-05,
  0.023089049383997917,
  0.04559335112571716,
  -0.10510903596878052,
  -0.1005614772439003,
  -0.07881765812635422,
  0.025743374601006508,
  -0.05974612385034561,
  -0.1747055947780609,
  -0.05892287939786911,
  -0.06596986949443817,
  -0.09151236712932587,
  0.03593139722943306,
  -0.07345644384622574,
  -0.018012331798672676,
  0.036221787333488464,
  0.07314501702785492,
  -0.06195896118879318,
  -0.0023348417598754168,
  -0.1982719600200653,
  -0.3291093707084656,
  0.006821473129093647,
  0.1486814171075821,
  0.2550199031829834,
  0.1663597822189331,
  0.15605349838733673,
  0.12756910920143127,
  -0.057475071400403976,
  0.14456160366535187,
  -0.05416375771164894,
  0.06393317133188248,
  -0.08582285046577454,
  0.019529936835169792,
  0.030426720157265663,
  -0.13159017264842987,
  -0.01176383811980486,
  -0.05212199687957764,
  -0.007775180973112583,
  0.0005310662090778351,
  0.03532465547323227,
  0.14036867022514343,
  -0.04217003658413887,
  -0.0504852756857872,
  0.08859632164239883,
  0.02489238791167736,
  0.036609407514333725,
  0.012656561098992825,
  -0.031059175729751587,
  0.13535012304782867,
  -0.07467728853225708,
  -0.00639297952875495,
  -0.007216154597699642,
  0.10756982862949371,
  -0.03459356725215912,
  0.05434964969754219,
  0.10563021898269653,
  -0.023835688829421997,
  -0.1384897232055664,
  -0.10662095248699188,
  -0.11560706794261932,
  -0.018126854673027992,
  -0.11542601138353348,
  0.05233073979616165,
  -0.08457083255052567,
  0.04891547933220863,
  0.048610806465148926,
  -0.0861951932311058,
  -0.1646905094385147,
  0.05879170447587967,
  -0.09346245974302292,
  0.21104931831359863,
  0.07167480885982513,
  0.09941790252923965,
  -0.04874766618013382,
  -0.11821635812520981,
  -0.11691499501466751,
  -0.04042290896177292,
  -0.035517025738954544,
  0.006470585707575083,
  0.07046835869550705,
  0.032006461173295975,
  -0.017604319378733635,
  0.1958240568637848,
  0.01993837021291256,
  -0.01663972996175289,
  0.11849723011255264,
  -0.10080186277627945,
  -0.009301570244133472,
  0.03264541178941727,
  -0.03453604504466057,
  -0.032728590071201324,
  -0.06038405001163483,
  -0.014748498797416687,
  -0.08714324235916138,
  0.0329294428229332,
  -0.04497246816754341,
  -0.0888349711894989,
  0.02692333422601223,
  0.18709281086921692,
  -0.002944737207144499]}
Define mappings
In [30]:
settings ={
  "settings": {
    "number_of_shards": 2,
    "number_of_replicas": 1,
    "index.knn": True
  },
   "mappings": {
    "dynamic": "true",
    "_source": {
      "enabled": "true"
    },
    "properties": {
      "title": {
        "type": "text"
      },
      "title_vector": {
        "type": "knn_vector",
        "dimension": 128
      }
    }
  }
}
In [31]:
IndexName = 'myml'
my = es.indices.create(index=IndexName, ignore=[400,404], body=settings)
In [32]:
my
Out[32]:
{'acknowledged': True, 'shards_acknowledged': True, 'index': 'myml'}

Step 5:

In [33]:
try:
    res = helpers.bulk(es, requests)
    print("Working")
except Exception as e:
    print(e)

Working

Testing KNN model

we are using cosine similarity to get result in ELK
In [36]:
title = input("Enter query: ")

x = tf.constant([title])
embeddings = embed(x)
x = np.asarray(embeddings)
x = x[0].tolist()


script_query = {
    "script_score": {
        "query": {"match_all": {}},
        "script": {
            "source": "cosineSimilarity(params.query_vector, doc['title_vector']) + 1.0",
            "params": {"query_vector": x}
        }
    }
}

script_query = {
    "knn": {
        "title_vector": {
            "vector": x,
            "k": 2
        }
    }
}

response = es.search(
    index="myml",
    body={
        "size": 10,
        "query": script_query,
        "_source": {"includes": ["title", "body"]}
    }
)

for hit in response["hits"]["hits"]:
    print("id: {}, score: {}".format(hit["_id"], hit["_score"]))
    print(hit["_source"])
    print()

Enter query: Swiss Army Man
id: 3241, score: 1.0
{'title': 'Swiss Army Man'}

id: 1349, score: 0.51294893
{'title': 'American Son'}

id: 6150, score: 0.48458296
{'title': 'Glitter Force'}

id: 4484, score: 0.48386106
{'title': 'A Family Man'}

id: 5785, score: 0.47989023
{'title': 'American Crime'}

id: 3784, score: 0.4735783
{'title': 'Star Men'}

id: 953, score: 0.46827134
{'title': 'Phantom Boy'}

id: 5621, score: 0.46712905
{'title': 'American Vandal'}

id: 5723, score: 0.46373478
{'title': 'Man Down'}

id: 4167, score: 0.46257648
{'title': 'Mercenary'}
at

3 comments:

  1. RiziqSeptember 23, 2020 at 12:44 AM

    Hi, when i try to execute "my = es.indices.create(index=IndexName, ignore=[400,404], body=settings)" why always show error

    {'error': {'root_cause': [{'type': 'illegal_argument_exception',
    'reason': 'unknown setting [index.knn] please check that any required plugins are installed, or check the breaking changes documentation for removed settings'}],
    'type': 'illegal_argument_exception',
    'reason': 'unknown setting [index.knn] please check that any required plugins are installed, or check the breaking changes documentation for removed settings'},
    'status': 400}

    Do you have solutions?

    ReplyDelete
  2. recyclageFebruary 12, 2021 at 4:59 PM

    Please, How do I fix error message : RequestError: RequestError(400, 'parsing_exception', 'unknown query [knn]')

    ReplyDelete
  3. ViktorOctober 10, 2022 at 12:46 AM

    to fix the error, you need to use latest version of elasticsearch syntax.
    Example from documentation:
    PUT my-index-2
    {
    "mappings": {
    "properties": {
    "my_vector": {
    "type": "dense_vector",
    "dims": 3,
    "index": true,
    "similarity": "dot_product"
    }
    }
    }
    }

    ReplyDelete

Subscribe to: Post Comments (Atom)

How to Use Publish-Audit-Merge Workflow in Apache Iceberg: A Beginner’s Guide

publish How to Use Publish-Audit-Merge Workflow in Apache Iceberg: A Beginner’s Guide ¶ In [24]: from ...