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Created October 9, 2022 03:41
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skqulacs を Julia から呼び出す
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skqulacs_in_julia.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "caf4aa08-e2a6-4609-88be-2550a8e97fa8",
"metadata": {},
"source": [
"# アヤメデータセットの分類を skqulacs で行う (in Julia)\n",
"\n",
"このノートブックは https://qulacs-osaka.github.io/scikit-qulacs/notebooks/0_tutorial.html#quantum-neural-network で紹介されている qulacs/skqulacs の実行を Julia で行ったものである."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "502cc519",
"metadata": {
"ExecuteTime": {
"start_time": "2022-10-08T09:14:33.567Z"
}
},
"outputs": [],
"source": [
"using Random\n",
"\n",
"using StatsPlots\n",
"import DataFrames\n",
"using MLUtils: splitobs, shuffleobs\n",
"using OneHotArrays: onehotbatch, onecold\n",
"using MLDatasets: Iris\n",
"using ScikitLearn\n",
"@sk_import metrics: f1_score\n",
"using PyCall"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "cfe668ba",
"metadata": {
"ExecuteTime": {
"end_time": "2022-10-08T18:09:42.101000+09:00",
"start_time": "2022-10-08T09:09:09.559Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(pymetadata.distribution(\"qulacs\")).version = \"0.5.0\"\n",
"(pymetadata.distribution(\"skqulacs\")).version = \"0.4.1\"\n"
]
}
],
"source": [
"pyrange = pybuiltin(\"range\")\n",
"pymetadata = pyimport(\"importlib.metadata\")\n",
"# @show は Python での print(f\"{object=}\") の文法に相当する\n",
"@show pymetadata.distribution(\"qulacs\").version\n",
"# 最後のセミコロンは不要な出力を抑制するものであり Julia の文法において本質的なものではない\n",
"@show pymetadata.distribution(\"skqulacs\").version;"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "30749421",
"metadata": {
"ExecuteTime": {
"end_time": "2022-10-08T18:09:43.174000+09:00",
"start_time": "2022-10-08T09:09:09.560Z"
}
},
"outputs": [],
"source": [
"# Python のライブラリを呼び出すことができる. もちろん事前に pip3 install skqulacs のように依存関係を解決しておく必要がある\n",
"skqulacs = pyimport(\"skqulacs\")\n",
"qnn = pyimport(\"skqulacs.qnn\")\n",
"\n",
"QNNClassifier = qnn.QNNClassifier\n",
"Bfgs = qnn.solver.Bfgs\n",
"create_qcl_ansatz = pyimport(\"skqulacs.circuit\").pre_defined.create_qcl_ansatz;"
]
},
{
"cell_type": "markdown",
"id": "8fde2a13-2f68-4d29-975f-37d37471e153",
"metadata": {},
"source": [
"# データセットの準備"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "041adc64",
"metadata": {
"ExecuteTime": {
"end_time": "2022-10-08T18:09:59.816000+09:00",
"start_time": "2022-10-08T09:09:09.562Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"irisdata.metadata[\"feature_names\"] = [\"sepallength\", \"sepalwidth\", \"petallength\", \"petalwidth\"]\n"
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"<div class=\"data-frame\"><p>150 rows × 5 columns</p><table class=\"data-frame\"><thead><tr><th></th><th>sepallength</th><th>sepalwidth</th><th>petallength</th><th>petalwidth</th><th>class</th></tr><tr><th></th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"Float64\">Float64</th><th title=\"InlineStrings.String15\">String15</th></tr></thead><tbody><tr><th>1</th><td>5.1</td><td>3.5</td><td>1.4</td><td>0.2</td><td>Iris-setosa</td></tr><tr><th>2</th><td>4.9</td><td>3.0</td><td>1.4</td><td>0.2</td><td>Iris-setosa</td></tr><tr><th>3</th><td>4.7</td><td>3.2</td><td>1.3</td><td>0.2</td><td>Iris-setosa</td></tr><tr><th>4</th><td>4.6</td><td>3.1</td><td>1.5</td><td>0.2</td><td>Iris-setosa</td></tr><tr><th>5</th><td>5.0</td><td>3.6</td><td>1.4</td><td>0.2</td><td>Iris-setosa</td></tr><tr><th>6</th><td>5.4</td><td>3.9</td><td>1.7</td><td>0.4</td><td>Iris-setosa</td></tr><tr><th>7</th><td>4.6</td><td>3.4</td><td>1.4</td><td>0.3</td><td>Iris-setosa</td></tr><tr><th>8</th><td>5.0</td><td>3.4</td><td>1.5</td><td>0.2</td><td>Iris-setosa</td></tr><tr><th>9</th><td>4.4</td><td>2.9</td><td>1.4</td><td>0.2</td><td>Iris-setosa</td></tr><tr><th>10</th><td>4.9</td><td>3.1</td><td>1.5</td><td>0.1</td><td>Iris-setosa</td></tr><tr><th>11</th><td>5.4</td><td>3.7</td><td>1.5</td><td>0.2</td><td>Iris-setosa</td></tr><tr><th>12</th><td>4.8</td><td>3.4</td><td>1.6</td><td>0.2</td><td>Iris-setosa</td></tr><tr><th>13</th><td>4.8</td><td>3.0</td><td>1.4</td><td>0.1</td><td>Iris-setosa</td></tr><tr><th>14</th><td>4.3</td><td>3.0</td><td>1.1</td><td>0.1</td><td>Iris-setosa</td></tr><tr><th>15</th><td>5.8</td><td>4.0</td><td>1.2</td><td>0.2</td><td>Iris-setosa</td></tr><tr><th>16</th><td>5.7</td><td>4.4</td><td>1.5</td><td>0.4</td><td>Iris-setosa</td></tr><tr><th>17</th><td>5.4</td><td>3.9</td><td>1.3</td><td>0.4</td><td>Iris-setosa</td></tr><tr><th>18</th><td>5.1</td><td>3.5</td><td>1.4</td><td>0.3</td><td>Iris-setosa</td></tr><tr><th>19</th><td>5.7</td><td>3.8</td><td>1.7</td><td>0.3</td><td>Iris-setosa</td></tr><tr><th>20</th><td>5.1</td><td>3.8</td><td>1.5</td><td>0.3</td><td>Iris-setosa</td></tr><tr><th>21</th><td>5.4</td><td>3.4</td><td>1.7</td><td>0.2</td><td>Iris-setosa</td></tr><tr><th>22</th><td>5.1</td><td>3.7</td><td>1.5</td><td>0.4</td><td>Iris-setosa</td></tr><tr><th>23</th><td>4.6</td><td>3.6</td><td>1.0</td><td>0.2</td><td>Iris-setosa</td></tr><tr><th>24</th><td>5.1</td><td>3.3</td><td>1.7</td><td>0.5</td><td>Iris-setosa</td></tr><tr><th>25</th><td>4.8</td><td>3.4</td><td>1.9</td><td>0.2</td><td>Iris-setosa</td></tr><tr><th>26</th><td>5.0</td><td>3.0</td><td>1.6</td><td>0.2</td><td>Iris-setosa</td></tr><tr><th>27</th><td>5.0</td><td>3.4</td><td>1.6</td><td>0.4</td><td>Iris-setosa</td></tr><tr><th>28</th><td>5.2</td><td>3.5</td><td>1.5</td><td>0.2</td><td>Iris-setosa</td></tr><tr><th>29</th><td>5.2</td><td>3.4</td><td>1.4</td><td>0.2</td><td>Iris-setosa</td></tr><tr><th>30</th><td>4.7</td><td>3.2</td><td>1.6</td><td>0.2</td><td>Iris-setosa</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table></div>"
],
"text/latex": [
"\\begin{tabular}{r|ccccc}\n",
"\t& sepallength & sepalwidth & petallength & petalwidth & class\\\\\n",
"\t\\hline\n",
"\t& Float64 & Float64 & Float64 & Float64 & String15\\\\\n",
"\t\\hline\n",
"\t1 & 5.1 & 3.5 & 1.4 & 0.2 & Iris-setosa \\\\\n",
"\t2 & 4.9 & 3.0 & 1.4 & 0.2 & Iris-setosa \\\\\n",
"\t3 & 4.7 & 3.2 & 1.3 & 0.2 & Iris-setosa \\\\\n",
"\t4 & 4.6 & 3.1 & 1.5 & 0.2 & Iris-setosa \\\\\n",
"\t5 & 5.0 & 3.6 & 1.4 & 0.2 & Iris-setosa \\\\\n",
"\t6 & 5.4 & 3.9 & 1.7 & 0.4 & Iris-setosa \\\\\n",
"\t7 & 4.6 & 3.4 & 1.4 & 0.3 & Iris-setosa \\\\\n",
"\t8 & 5.0 & 3.4 & 1.5 & 0.2 & Iris-setosa \\\\\n",
"\t9 & 4.4 & 2.9 & 1.4 & 0.2 & Iris-setosa \\\\\n",
"\t10 & 4.9 & 3.1 & 1.5 & 0.1 & Iris-setosa \\\\\n",
"\t11 & 5.4 & 3.7 & 1.5 & 0.2 & Iris-setosa \\\\\n",
"\t12 & 4.8 & 3.4 & 1.6 & 0.2 & Iris-setosa \\\\\n",
"\t13 & 4.8 & 3.0 & 1.4 & 0.1 & Iris-setosa \\\\\n",
"\t14 & 4.3 & 3.0 & 1.1 & 0.1 & Iris-setosa \\\\\n",
"\t15 & 5.8 & 4.0 & 1.2 & 0.2 & Iris-setosa \\\\\n",
"\t16 & 5.7 & 4.4 & 1.5 & 0.4 & Iris-setosa \\\\\n",
"\t17 & 5.4 & 3.9 & 1.3 & 0.4 & Iris-setosa \\\\\n",
"\t18 & 5.1 & 3.5 & 1.4 & 0.3 & Iris-setosa \\\\\n",
"\t19 & 5.7 & 3.8 & 1.7 & 0.3 & Iris-setosa \\\\\n",
"\t20 & 5.1 & 3.8 & 1.5 & 0.3 & Iris-setosa \\\\\n",
"\t21 & 5.4 & 3.4 & 1.7 & 0.2 & Iris-setosa \\\\\n",
"\t22 & 5.1 & 3.7 & 1.5 & 0.4 & Iris-setosa \\\\\n",
"\t23 & 4.6 & 3.6 & 1.0 & 0.2 & Iris-setosa \\\\\n",
"\t24 & 5.1 & 3.3 & 1.7 & 0.5 & Iris-setosa \\\\\n",
"\t25 & 4.8 & 3.4 & 1.9 & 0.2 & Iris-setosa \\\\\n",
"\t26 & 5.0 & 3.0 & 1.6 & 0.2 & Iris-setosa \\\\\n",
"\t27 & 5.0 & 3.4 & 1.6 & 0.4 & Iris-setosa \\\\\n",
"\t28 & 5.2 & 3.5 & 1.5 & 0.2 & Iris-setosa \\\\\n",
"\t29 & 5.2 & 3.4 & 1.4 & 0.2 & Iris-setosa \\\\\n",
"\t30 & 4.7 & 3.2 & 1.6 & 0.2 & Iris-setosa \\\\\n",
"\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"\u001b[1m150×5 DataFrame\u001b[0m\n",
"\u001b[1m Row \u001b[0m│\u001b[1m sepallength \u001b[0m\u001b[1m sepalwidth \u001b[0m\u001b[1m petallength \u001b[0m\u001b[1m petalwidth \u001b[0m\u001b[1m class \u001b[0m\n",
"\u001b[1m \u001b[0m│\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m String15 \u001b[0m\n",
"─────┼──────────────────────────────────────────────────────────────────\n",
" 1 │ 5.1 3.5 1.4 0.2 Iris-setosa\n",
" 2 │ 4.9 3.0 1.4 0.2 Iris-setosa\n",
" 3 │ 4.7 3.2 1.3 0.2 Iris-setosa\n",
" 4 │ 4.6 3.1 1.5 0.2 Iris-setosa\n",
" 5 │ 5.0 3.6 1.4 0.2 Iris-setosa\n",
" 6 │ 5.4 3.9 1.7 0.4 Iris-setosa\n",
" 7 │ 4.6 3.4 1.4 0.3 Iris-setosa\n",
" 8 │ 5.0 3.4 1.5 0.2 Iris-setosa\n",
" 9 │ 4.4 2.9 1.4 0.2 Iris-setosa\n",
" 10 │ 4.9 3.1 1.5 0.1 Iris-setosa\n",
" 11 │ 5.4 3.7 1.5 0.2 Iris-setosa\n",
" ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮\n",
" 141 │ 6.7 3.1 5.6 2.4 Iris-virginica\n",
" 142 │ 6.9 3.1 5.1 2.3 Iris-virginica\n",
" 143 │ 5.8 2.7 5.1 1.9 Iris-virginica\n",
" 144 │ 6.8 3.2 5.9 2.3 Iris-virginica\n",
" 145 │ 6.7 3.3 5.7 2.5 Iris-virginica\n",
" 146 │ 6.7 3.0 5.2 2.3 Iris-virginica\n",
" 147 │ 6.3 2.5 5.0 1.9 Iris-virginica\n",
" 148 │ 6.5 3.0 5.2 2.0 Iris-virginica\n",
" 149 │ 6.2 3.4 5.4 2.3 Iris-virginica\n",
" 150 │ 5.9 3.0 5.1 1.8 Iris-virginica\n",
"\u001b[36m 129 rows omitted\u001b[0m"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"irisdata =Iris(as_df=true)\n",
"@show irisdata.metadata[\"feature_names\"]\n",
"df = irisdata.dataframe"
]
},
{
"cell_type": "markdown",
"id": "a1068c94-9fac-483e-84d9-45b1fd2675a4",
"metadata": {},
"source": [
"## データの可視化"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "42729fe4",
"metadata": {
"ExecuteTime": {
"start_time": "2022-10-08T09:09:09.563Z"
}
},
"outputs": [
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]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@df df scatter(:petallength, :petalwidth, xlabel=\"petallength\", ylabel=\"petalwidth\", group=:class, legend=:topleft)"
]
},
{
"cell_type": "markdown",
"id": "ce7572d4-140f-4c6e-987a-af00bec42730",
"metadata": {},
"source": [
"### データの前処理"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ebf7884c-6351-4bf0-af35-2a366f2d7ef8",
"metadata": {},
"outputs": [],
"source": [
"X = Matrix(df[!, [\"petallength\", \"petalwidth\"]])'\n",
"@assert size(X) == (2, irisdata.metadata[\"n_observations\"])\n",
"y_str = df[!, \"class\"]\n",
"labels = y_str |> unique\n",
"num_class = length(labels)\n",
"# ラベルは数値でエンコードされていないのでプログラマ側が手動で行う必要がある\n",
"# Python ライブラリの出力の整合性を与えるために正解値ラベルの番号を０始まりに整える\n",
"# 通常, Julia の配列は MATLAB や Fortran のように 1 始まりになっていることに注意する\n",
"y = onecold(onehotbatch(y_str, labels), pyrange(num_class));"
]
},
{
"cell_type": "markdown",
"id": "032692b0-893c-453e-b2a2-e9ec1f93fe8b",
"metadata": {},
"source": [
"## split train/test"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "38e5c4ea",
"metadata": {
"ExecuteTime": {
"start_time": "2022-10-08T09:09:09.565Z"
}
},
"outputs": [],
"source": [
"rng = MersenneTwister(12345) # 乱数生成アルゴリズムとそのシードを指定\n",
"X, y = shuffleobs(rng, (X, y)) # データのシャッフル\n",
"(Xtrain, ytrain), (Xtest, ytest) = splitobs((X, y), at = 0.75); # training データ側が７５％を保持するように設定"
]
},
{
"cell_type": "markdown",
"id": "52149578-81e6-4d70-b430-4276ffd8b52c",
"metadata": {},
"source": [
"# Quantum neural network を用いた分類実行"
]
},
{
"cell_type": "markdown",
"id": "4769d084-e928-4819-8403-82e4c48f6194",
"metadata": {},
"source": [
"## セットアップ"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "460e6355-00e3-42b8-a5ae-dd495f074906",
"metadata": {},
"outputs": [],
"source": [
"nqubit = 5\n",
"c_depth = 3\n",
"time_step = 1.\n",
"\n",
"solver = Bfgs()\n",
"maxiter = 20\n",
"circuit = create_qcl_ansatz(\n",
" nqubit,\n",
" c_depth,\n",
" time_step,\n",
")\n",
"qcl = QNNClassifier(circuit, num_class, Bfgs());"
]
},
{
"cell_type": "markdown",
"id": "09f2d0fa-4963-4b80-a759-f86b95b464bd",
"metadata": {},
"source": [
"## 学習の実行\n",
"scikit-learn のように `fit` で学習を開始することができる."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "88103aa5-1a44-4d85-9330-b4b1ea52744b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"opt_loss = 0.37474689975671543\n"
]
},
{
"data": {
"text/plain": [
"0.37474689975671543"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# qcl.fit の第一引数は (data 数, 特徴量の次元) のように整える必要がある\n",
"opt_loss, opt_params = qcl.fit(Xtrain', ytrain, maxiter);\n",
"# 概ね 0.35 程度の値を出力する\n",
"@show opt_loss"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c9a8db41-2c29-4c4b-b780-2121287e0f4b",
"metadata": {},
"outputs": [],
"source": [
"# y\\hat + <tab>\n",
"ŷ = qcl.predict(Xtest');"
]
},
{
"cell_type": "markdown",
"id": "44824b26-07e7-4bd6-a611-216bd55c5a0e",
"metadata": {},
"source": [
"F1 値の算出. 概ね 0.9 以上の数値が出れば良い.\n",
"0.6 のように低い値が出ている場合はモデル (`qcl`) の出力 (`ŷ`) と (`ytest`) の仕様の整合性が取れてない可能性がある. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "8000c53e-33a8-4d14-a88d-cd50406b81d3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9484126984126986"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f1_score(ytest , ŷ, average=\"weighted\")"
]
}
],
"metadata": {
"@webio": {
"lastCommId": null,
"lastKernelId": null
},
"kernelspec": {
"display_name": "julia 1.8.2",
"language": "julia",
"name": "julia-1.8"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.8.2"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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