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+{
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "id": "40152b50",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": []
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+ "cell_type": "code",
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+ "execution_count": 18,
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+ "id": "be8d8613",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "training + evaluating time : 1.9439990520477295, score = 0.9504444444444444\n",
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+ " [ 1 2 0 8 8 0 0 5 0 415]]\n",
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+ "training + evaluating time : 7.785594463348389, score = 0.9304444444444444\n",
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+ "training + evaluating time : 27.00832462310791, score = 0.9651111111111111\n",
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "import matplotlib.pyplot as plt\n",
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+ "from sklearn.neural_network import MLPClassifier\n",
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+ "from sklearn.neighbors import KNeighborsClassifier\n",
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+ "from sklearn.svm import SVC\n",
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+ "from sklearn.model_selection import train_test_split\n",
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+ "from sklearn.model_selection import KFold\n",
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+ "from sklearn.metrics import precision_score\n",
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+ "from sklearn.metrics import confusion_matrix\n",
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+ "import random\n",
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+ "import time\n",
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+ "from sklearn.datasets import fetch_openml \n",
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+ "\n",
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+ "#mnist = fetch_openml('mnist_784') \n",
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+ "\n",
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+ "indices = [i for i in range(len(mnist.data))]\n",
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+ "random.shuffle(indices)\n",
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+ "indices = indices[:15000]\n",
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+ "\n",
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+ "data = [mnist.data.values[i] for i in indices]\n",
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+ "target = [mnist.target[i] for i in indices]\n",
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+ "\n",
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+ "classifiers = [KNeighborsClassifier(3), MLPClassifier(hidden_layer_sizes = (100,)), SVC()]\n",
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+ "xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.7)\n",
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+ "\n",
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+ "\n",
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+ "for clf in classifiers:\n",
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+ " start = time.time()\n",
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+ " clf.fit(xtrain, ytrain)\n",
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+ " score = clf.score(xtest, ytest)\n",
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+ " end = time.time()\n",
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+ " print(f\"training + evaluating time : {end - start}, score = {score}\")\n",
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+ " print(confusion_matrix(ytest, clf.predict(xtest)))\n"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "id": "1aff265d",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": []
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+ }
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+ ],
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+ "metadata": {
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+ "kernelspec": {
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+ "display_name": "Python 3 (ipykernel)",
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+ "language": "python",
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+ "name": "python3"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 3
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+ },
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
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+ "version": "3.9.9"
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+ }
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 5
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+}
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