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@svoboda

[Julia]kNN実装と速度検証 Part1 (概要～Package情報)

Last updated at Posted at 2025-04-06

はじめに

　データ分析や機械学習などに利用できるプログラムとして、Juliaがあると知った。
　以下３点のメリットから今後利用することがあるかもしれないので、備忘録として初歩的なことから欲しかった資料をまとめていく。
　[1]Pythonと同じようにライブラリを利用できる
　[2]実行速度がC言語よりも速い
　[3]PythonやC言語と連携することもできる

実行環境

　参考資料[1]で構築したVSCにおけるWSL2で実行している。

kNNとは

　kNN（k-nearest neighbor）とは、対象データ周囲のk個のデータをもとに多数決を行い、対象データが何か判別する教師あり学習の一手法のこと。

image.png

　有名な手法で詳細を解説しているサイトは数多くあるため、詳細は割愛。

検証したプログラム

プログラム概要

　以下機能のJuliaプログラムについて、CSVデータ内のデータ数及び実行回数によって実行時間がどう変わっていくか確認した。
　①テストデータCSVを読み込む
　②数値データの欠損値を平均値で補完
　③Stringデータの欠損値をkNNで補完

CSVデータ

　使用したCSVデータについて、以下Pythonコードで作成した。

random.py
import random
import csv

# ランダムに生成するデータの件数
num_records = 10000  # 必要な件数を指定

# 各項目の選択肢
hr_range = range(1, 1000)
genders = ['F', 'M']
regions = ['アジア', '北米', '南米', 'ヨーロッパ', 'アフリカ', '中東', 'オセアニア']
main_weapons = ['片手剣', '大剣', '太刀', '双剣', 'ランス', 'ガンランス', 'ハンマー', '狩猟笛', 'スラッシュアックス', 'チャージアックス', '操虫棍', '弓', 'ライトボウガン', 'ヘビーボウガン']
sub_weapons = ['片手剣', '大剣', '太刀', '双剣', 'ランス', 'ガンランス', 'ハンマー', '狩猟笛', 'スラッシュアックス', 'チャージアックス', '操虫棍', '弓', 'ライトボウガン', 'ヘビーボウガン']
play_times = range(0, 301)

# データの生成
data = []
for _ in range(num_records):
   hr = random.choice(hr_range)
   gender = random.choice(genders)
   region = random.choice(regions)
   main_weapon = random.choice(main_weapons)
   sub_weapon = random.choice(sub_weapons)
   play_time = random.choice(play_times)
   
   data.append([hr, gender, region, main_weapon, sub_weapon, play_time])

# NULL（None）にランダムに上書き
null_probability = 0.2  # 20%の確率でNULLに上書き

for row in data:
   # サブ武器をランダムにNULLにする
   if random.random() < null_probability:
       row[4] = None
   
   # プレイ時間をランダムにNULLにする
   if random.random() < null_probability:
       row[5] = None

# CSVに出力
header = ['HR', 'gender', 'area', 'main_weapon', 'sub_weapon', 'play_time']
with open('random_data_with_null.csv', 'w', newline='', encoding='utf-8') as file:
   writer = csv.writer(file)
   writer.writerow(header)  # ヘッダー書き込み
   writer.writerows(data)   # データ書き込み

プログラム構成

　構成は以下の通りとなっている。

./
├─.data
|  └─.raw
|    └─random_data_100.csv
|    └─random_data_1000.csv
|    └─random_data_10000.csv
├─.src
|  └─main.jl
|  └─read_csv.jl
|  └─completion_numeric_data.jl
|  └─knn_imputation.jl
├─Mainfest.toml
└─Project.toml

Package情報

　参考資料[2]を参考に以下コマンドで必要なパッケージをインストールできるが、バージョン違いによって動作不調を起こす可能性があるため、Package情報も記載する。

using Pkg
Pkg.add([
    "Statistics",
    "MLJ",
    "MLJModelInterface",
    "NearestNeighborModels",
    "DataFrames",
    "CategoricalArrays",
    "Tables",
    "CSV"
])
Mainfest.toml（クリックで展開）
Mainfest.toml
# This file is machine-generated - editing it directly is not advised

julia_version = "1.11.4"
manifest_format = "2.0"
project_hash = "1e3bc912d01e1f4840e01eecac05c93ca37b4124"

[[deps.ARFFFiles]]
deps = ["CategoricalArrays", "Dates", "Parsers", "Tables"]
git-tree-sha1 = "678eb18590a8bc6674363da4d5faa4ac09c40a18"
uuid = "da404889-ca92-49ff-9e8b-0aa6b4d38dc8"
version = "1.5.0"

[[deps.Accessors]]
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version = "0.1.42"

    [deps.Accessors.extensions]
    AxisKeysExt = "AxisKeys"
    IntervalSetsExt = "IntervalSets"
    LinearAlgebraExt = "LinearAlgebra"
    StaticArraysExt = "StaticArrays"
    StructArraysExt = "StructArrays"
    TestExt = "Test"
    UnitfulExt = "Unitful"

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[[deps.CategoricalDistributions]]
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version = "0.1.15"

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    [deps.CategoricalDistributions.weakdeps]
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[[deps.ChainRulesCore]]
deps = ["Compat", "LinearAlgebra"]
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[[deps.CodecZlib]]
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[[deps.SimpleBufferStream]]
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[[deps.SortingAlgorithms]]
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[[deps.SpecialFunctions]]
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weakdeps = ["ChainRulesCore"]

    [deps.SpecialFunctions.extensions]
    SpecialFunctionsChainRulesCoreExt = "ChainRulesCore"

[[deps.SplittablesBase]]
deps = ["Setfield", "Test"]
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[[deps.StableRNGs]]
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    StaticArraysChainRulesCoreExt = "ChainRulesCore"
    StaticArraysStatisticsExt = "Statistics"

[[deps.StaticArraysCore]]
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[[deps.StatisticalMeasures]]
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[[deps.StatisticalMeasuresBase]]
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[[deps.Statistics]]
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    SparseArraysExt = ["SparseArrays"]

[[deps.StatsAPI]]
deps = ["LinearAlgebra"]
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[[deps.StatsBase]]
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git-tree-sha1 = "29321314c920c26684834965ec2ce0dacc9cf8e5"
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    StatsFunsInverseFunctionsExt = "InverseFunctions"

[[deps.StringManipulation]]
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[[deps.TranscodingStreams]]
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[[deps.Transducers]]
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git-tree-sha1 = "7deeab4ff96b85c5f72c824cae53a1398da3d1cb"
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    TransducersBlockArraysExt = "BlockArrays"
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    TransducersLazyArraysExt = "LazyArrays"
    TransducersOnlineStatsBaseExt = "OnlineStatsBase"
    TransducersReferenceablesExt = "Referenceables"

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[[deps.URIs]]
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[[deps.nghttp2_jll]]
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Project.toml（クリックで展開）
Project.toml
[deps]
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
NearestNeighborModels = "636a865e-7cf4-491e-846c-de09b730eb36"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"

参考資料

[1]svoboda, 『[Julia]VSCにおけるWSL2でのJulia　環境構築～サンプルコード実行まで』, https://qiita.com/svoboda/private/6a0ad624bc58a354edb3
[2]svoboda, 『[Julia]仮想環境（Package）構築』, https://qiita.com/svoboda/private/ad77b77913eb78ac0b41

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