Comparison of the 3 methods
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6
.ipynb_checkpoints/Untitled-checkpoint.ipynb
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6
.ipynb_checkpoints/Untitled-checkpoint.ipynb
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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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122
Untitled.ipynb
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Untitled.ipynb
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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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},
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{
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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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"[[470 0 1 0 0 0 1 0 1 0]\n",
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" [ 0 502 2 1 0 0 0 0 0 0]\n",
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" [ 4 9 388 2 0 0 2 13 2 0]\n",
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" [ 2 0 2 454 1 12 0 2 3 3]\n",
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" [ 0 8 0 0 388 0 2 2 0 20]\n",
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" [ 1 3 0 11 0 368 6 0 0 2]\n",
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" [ 6 2 0 0 1 2 454 0 0 0]\n",
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" [ 1 5 2 0 1 0 0 440 0 14]\n",
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" [ 2 12 4 12 2 7 2 2 398 4]\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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"[[465 0 3 0 0 0 3 1 0 1]\n",
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" [ 0 494 4 1 3 0 2 0 0 1]\n",
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" [ 3 13 378 8 4 0 4 6 3 1]\n",
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" [ 3 1 9 446 0 2 1 3 8 6]\n",
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" [ 1 3 1 0 392 2 5 4 3 9]\n",
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" [ 5 2 0 9 4 353 8 1 7 2]\n",
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" [ 5 2 4 0 7 1 444 0 1 1]\n",
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" [ 2 3 3 3 4 1 0 431 0 16]\n",
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" [ 1 15 10 8 1 6 4 3 394 3]\n",
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" [ 4 2 1 10 11 2 0 11 8 390]]\n",
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"training + evaluating time : 27.00832462310791, score = 0.9651111111111111\n",
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"[[467 0 1 0 0 1 3 0 1 0]\n",
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" [ 1 495 4 2 1 1 0 0 0 1]\n",
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" [ 1 1 401 3 4 2 2 4 2 0]\n",
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" [ 1 0 3 460 0 8 0 2 3 2]\n",
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" [ 0 1 0 0 407 0 3 1 0 8]\n",
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" [ 0 0 0 7 0 378 4 0 1 1]\n",
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" [ 3 0 0 0 2 4 455 0 1 0]\n",
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" [ 1 3 2 1 2 1 0 443 0 10]\n",
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" [ 1 6 3 5 2 5 1 0 421 1]\n",
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" [ 1 2 1 8 4 1 0 4 2 416]]\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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32
eval.py
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eval.py
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import matplotlib.pyplot as plt
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from sklearn.neural_network import MLPClassifier
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.svm import SVC
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import KFold
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from sklearn.metrics import precision_score
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from sklearn.metrics import confusion_matrix
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import random
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import time
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from sklearn.datasets import fetch_openml
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#mnist = fetch_openml('mnist_784')
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indices = [i for i in range(len(mnist.data))]
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random.shuffle(indices)
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indices = indices[:15000]
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data = [mnist.data.values[i] for i in indices]
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target = [mnist.target[i] for i in indices]
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classifiers = [KNeighborsClassifier(3), MLPClassifier(hidden_layer_sizes = (100,)), SVC()]
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xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.7)
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for clf in classifiers:
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start = time.time()
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clf.fit(xtrain, ytrain)
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score = clf.score(xtest, ytest)
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end = time.time()
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print(f"training + evaluating time : {end - start}, score = {score}")
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print(confusion_matrix(ytest, clf.predict(xtest)))
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