poprawki
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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": 17,
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"id": "0b35a076",
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"metadata": {},
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"outputs": [],
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"source": [
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"from load_data import get_dataset\n",
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"import numpy as np\n",
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"from collections import Counter\n",
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"from tabulate import tabulate\n",
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"from statistics import mean"
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]
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},
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{
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"cell_type": "markdown",
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"id": "elementary-purchase",
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"metadata": {},
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"source": [
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"# Zadanie 1 (4 pkt)\n",
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"\n",
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"Napisz kod klasy <code>KNearestNeighbor</code> implementującej klasyfikator <i>knn</i>. Należy zimplementować następujące metody:\n",
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" - <code>konstruktor</code> pobierający listę obrazów treningowych (zgodną zw składową 'values' wczytanego słownika) oraz listę ich etykiet\n",
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" - metoda <code>l_p_metric(image1, image2, p):</code> zwracająca wartość odległości pomiędzy dwoma obrazami, mierzoną normą typu <i>l_p</i> - parametr <code>p</code> określa 'potęgę' normy\n",
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" - metoda <code>predict(test_images, k,p):</code> zwracająca listę prognozowanych etykiet dla obrazów testowych (parametr <code>test_images</code>). Paramter drugi określa liczbę przeszukiwanych sąsiadów, natomiast paramter trzeci określa potęgę wybranej metryki.\n",
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" - metoda <code>accuracy(test_images, k,p)</code> zwracająca dokładność klasyfikatora na zbiorze testowym. Parametr drugi i trzeci są jak w metodzie <code>predict()</code>\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": 18,
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"id": "great-earthquake",
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"metadata": {
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"nbgrader": {
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"grade": true,
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"grade_id": "cell-50c8d2866e4d875e",
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"locked": false,
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"points": 4,
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"schema_version": 3,
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"solution": true,
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"task": false
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}
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},
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"outputs": [],
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"source": [
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"class KNearestNeighbor():\n",
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" def __init__(self, values, labels):\n",
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" self.values = values\n",
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" self.labels = labels\n",
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"\n",
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" def l_p_metric(self, image1, image2, p):\n",
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" return np.sum(np.abs(image1 - image2) ** p) ** (1/p)\n",
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"\n",
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" def predict(self, X, K=1, P=1):\n",
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" predicted = []\n",
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"\n",
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" for image in X:\n",
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" metrics = []\n",
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"\n",
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" for value in self.values:\n",
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" metrics.append(self.l_p_metric(image, value, P))\n",
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"\n",
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" mins = sorted(range(len(metrics)), key = lambda sub: metrics[sub])[:K]\n",
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" nearest = [self.labels[x] for x in mins]\n",
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" pred = max(nearest, key=Counter(nearest).get)\n",
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"\n",
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" predicted.append(pred)\n",
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"\n",
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" return predicted\n",
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"\n",
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" def accuracy(self, expected, predicted):\n",
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" return sum(1 for x, y in zip(expected, predicted) if x == y) / len(expected)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "brave-replacement",
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"metadata": {},
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"source": [
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"# Zadanie 2 (2 pkt)\n",
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"\n",
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"Napisz kod funkcji <code>crossValidation(X, y, n = 10, k=1, p=1):</code> obliczającą algorytm <code>kNN</code> z n-krotną walidacją krzyżową."
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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": 19,
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"id": "entire-advancement",
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"metadata": {},
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"outputs": [],
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"source": [
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"def crossValidation(X_train, y_train, X_test, y_test, n=10, k=1, p=1):\n",
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" tab = []\n",
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" X_folds = []\n",
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" y_folds = []\n",
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" \n",
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" f_size = len(X_train)//n\n",
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" index = 0\n",
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" \n",
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" for i in range(n):\n",
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" if i == n-1:\n",
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" X_folds.append(X_train[index:])\n",
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" y_folds.append(y_train[index:])\n",
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" continue\n",
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" X_folds.append(X_train[index : index+f_size])\n",
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" y_folds.append(y_train[index : index+f_size])\n",
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" index = index + f_size\n",
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" \n",
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" for i in range(n):\n",
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" X_train_temp = X_folds[:i] + X_folds[i+1:]\n",
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" X_train = np.concatenate((X_train_temp))\n",
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"\n",
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" y_train_temp = y_folds[:i] + y_folds[i+1:]\n",
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" y_train = np.concatenate((y_train_temp))\n",
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" \n",
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" X_test = X_folds[i]\n",
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" y_test = y_folds[i]\n",
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"\n",
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" Knn = KNearestNeighbor(X_train, y_train)\n",
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" \n",
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" pred = Knn.predict(X_test, k, p)\n",
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" a = Knn.accuracy(y_test, pred)\n",
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" tab.append(a)\n",
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" \n",
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" result = mean(tab)\n",
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" \n",
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" return result"
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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": 20,
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"id": "searching-globe",
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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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" K P kNN accuracy CrossValidation accuracy\n",
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"--- --- -------------- --------------------------\n",
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" 1 1 0.583012 0.658228\n",
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" 1 2 0.552124 0.617332\n",
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" 5 1 0.555985 0.574489\n",
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" 5 2 0.544402 0.565725\n",
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" 10 1 0.501931 0.523856\n",
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" 10 2 0.501931 0.534567\n"
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]
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}
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],
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"source": [
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"X_train, y_train, X_test, y_test = get_dataset(new_size=64)\n",
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"\n",
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"kNN = KNearestNeighbor(X_train, y_train)\n",
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"\n",
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"Ks = [1, 5, 10]\n",
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"Ps = [1, 2]\n",
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"\n",
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"accuracy = [ [\n",
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" k, p, \n",
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" kNN.accuracy(y_test, kNN.predict(X_test, K=k, P=p)),\n",
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" crossValidation(X_train, y_train, X_test, y_test, n=len(X_train), k=k, p=p)] for k in Ks for p in Ps ]\n",
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"\n",
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"print(tabulate(accuracy, headers=['K', 'P', 'kNN accuracy', 'CrossValidation accuracy']))\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a85bb37f",
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"metadata": {},
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"source": [
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"# Zadanie 3 (4 pkt)\n",
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"\n",
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"Napisz kod klasy <code>LogisticRegression</code> implementującej klasyfikator <i>wieloklasowej regresji logistycznej</i> z funkcją <code>softmax()</code> (ze standardowymi nazwami dwóch kluczowych funkcji: <i>fit()</i>, <i>predict()</i>). Zastosuj ten kod do pobranych danych (zbiór walidacyjny losujemy ze zbioru treningowego) - oblicz następujące charakterystyki modelu dla danych walidacyjnych oraz treningowych: dokładność (accuracy), precyzję (precision), czułość(recall) oraz F1 - dla poszczególnych klas oraz globalnie (zob. np. <a href=\"https://medium.com/synthesio-engineering/precision-accuracy-and-f1-score-for-multi-label-classification-34ac6bdfb404\">tu</a>).\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": 21,
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"id": "e433be08",
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"metadata": {},
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"outputs": [],
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"source": [
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"class LogisticRegression():\n",
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" def mapY(self, y, cls):\n",
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" m = len(y)\n",
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" yBi = np.matrix(np.zeros(m)).reshape(m, 1)\n",
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" yBi[y == cls] = 1.\n",
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" return yBi\n",
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"\n",
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" def indicatorMatrix(self, y):\n",
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" classes = np.unique(y.tolist())\n",
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" m = len(y)\n",
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" k = len(classes)\n",
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" Y = np.matrix(np.zeros((m, k)))\n",
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" for i, cls in enumerate(classes):\n",
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" Y[:, i] = self.mapY(y, cls)\n",
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" return Y\n",
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" \n",
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" # Zapis macierzowy funkcji softmax\n",
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" def softmax(self, X):\n",
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" return np.exp(X) / np.sum(np.exp(X))\n",
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" \n",
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" # Funkcja regresji logistcznej\n",
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" def h(self, theta, X):\n",
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" return 1.0/(1.0 + np.exp(-X * theta))\n",
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" \n",
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" # Funkcja kosztu dla regresji logistycznej\n",
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" def J(self, h, theta, X, y):\n",
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" m = len(y)\n",
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" h_val = h(theta, X)\n",
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" s1 = np.multiply(y, np.log(h_val))\n",
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" s2 = np.multiply((1 - y), np.log(1 - h_val))\n",
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" return -np.sum(s1 + s2, axis=0) / m\n",
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"\n",
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" # Gradient dla regresji logistycznej\n",
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" def dJ(self, h, theta, X, y):\n",
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" return 1.0 / len(y) * (X.T * (h(theta, X) - y))\n",
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"\n",
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" # Metoda gradientu prostego dla regresji logistycznej\n",
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" def GD(self, h, fJ, fdJ, theta, X, y, alpha=0.01, eps=10**-3, maxSteps=1000):\n",
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" errorCurr = fJ(h, theta, X, y) # fJ -> J, fdJ -> dJ\n",
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" errors = [[errorCurr, theta]]\n",
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" while True:\n",
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" # oblicz nowe theta\n",
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" theta = theta - alpha * fdJ(h, theta, X, y)\n",
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" # raportuj poziom błędu\n",
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" errorCurr, errorPrev = fJ(h, theta, X, y), errorCurr\n",
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" # kryteria stopu\n",
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" if abs(errorPrev - errorCurr) <= eps:\n",
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" break\n",
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" if len(errors) > maxSteps:\n",
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" break\n",
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" errors.append([errorCurr, theta]) \n",
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" return theta, errors\n",
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"\n",
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" def trainMaxEnt(self, X, Y):\n",
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" n = X.shape[1]\n",
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" thetas = []\n",
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" for c in range(Y.shape[1]):\n",
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" YBi = Y[:,c]\n",
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" theta = np.matrix(np.random.random(n)).reshape(n,1)\n",
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" # Macierz parametrów theta obliczona dla każdej klasy osobno.\n",
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" thetaBest, errors = self.GD(self.h, self.J, self.dJ, theta, \n",
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" X, YBi, alpha=0.1, eps=10**-4)\n",
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" thetas.append(thetaBest)\n",
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" return thetas\n",
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"\n",
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" def classify(self, thetas, X):\n",
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" regs = np.array([(X*theta).item() for theta in thetas])\n",
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" probs = self.softmax(regs)\n",
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" result = np.argmax(probs)\n",
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" return result\n",
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"\n",
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" def class_score(self, expected, predicted):\n",
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" # accuracy = TP + TN / FP + FN + TP + TN\n",
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" accuracy = sum(1 for exp, pred in zip(expected, predicted) if exp == pred) / len(expected)\n",
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" # precision = TP / FP + TP\n",
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" precision = sum(\n",
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" 1 for exp, pred in zip(expected, predicted) if exp == 1.0 and pred == 1.0) / sum(\n",
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" 1 for exp, pred in zip(expected, predicted) if exp == 1.0)\n",
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" # recall = TP / FN + TP\n",
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" recall = sum(\n",
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" 1 for exp, pred in zip(expected, predicted) if exp == 1.0 and pred == 1.0) / sum(\n",
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" 1 for exp, pred in zip(expected, predicted) if pred == 1.0)\n",
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" f1 = (2 * precision * recall) / (precision + recall)\n",
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" return accuracy, precision, recall, f1\n",
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"\n",
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" def fit(self, X_train, y_train):\n",
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" Y = self.indicatorMatrix(y_train)\n",
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" self.thetas = self.trainMaxEnt(X_train, Y)\n",
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"\n",
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" def predict(self, X_test):\n",
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" return np.array([self.classify(self.thetas, x) for x in X_test])\n",
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" \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": 22,
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"id": "ba36ecbb",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_42943/2006812993.py:30: RuntimeWarning: divide by zero encountered in log\n",
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" s2 = np.multiply((1 - y), np.log(1 - h_val))\n",
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"/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_42943/2006812993.py:30: RuntimeWarning: invalid value encountered in multiply\n",
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" s2 = np.multiply((1 - y), np.log(1 - h_val))\n",
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"/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_42943/2006812993.py:47: RuntimeWarning: invalid value encountered in subtract\n",
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" if abs(errorPrev - errorCurr) <= eps:\n"
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]
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}
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],
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"source": [
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"X_train, y_train, X_test, y_test = get_dataset(new_size=64) \n",
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"\n",
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"logreg = LogisticRegression()\n",
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"logreg.fit(X_train, y_train)\n",
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"\n",
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"predicted = logreg.predict(X_test)"
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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": 23,
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"id": "af8d9107",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.2857142857142857"
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]
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},
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sklearn import metrics\n",
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"\n",
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"metrics.accuracy_score(y_test,predicted)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7f8326ba",
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"metadata": {},
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"source": [
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"# Zadanie 4 (1 pkt)\n",
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"\n",
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"Oblicz ile danych z poszczególnych klas znajduje się po dodatniej/ujemnej stronie hiperpłaszczyzny klasyfikacyjnej dla danej klasy."
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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": 24,
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"id": "09f0a567",
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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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" Klasa Dodatnia strona Ujemna strona\n",
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"------- ----------------- ---------------\n",
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" 0 23 236\n",
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" 2 229 30\n",
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" 4 7 252\n"
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]
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}
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],
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"source": [
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"one_hot = logreg.indicatorMatrix(predicted)\n",
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"length = len(one_hot)\n",
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"one_hot = one_hot.sum(axis=0).tolist()[0]\n",
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"\n",
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"hyperplane = [\n",
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" [i for i in np.unique(predicted)], \n",
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" [int(x) for x in one_hot],\n",
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" [length - int(x) for x in one_hot]]\n",
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" \n",
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"\n",
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"print(tabulate(np.array(hyperplane).T, headers=['Klasa', 'Dodatnia strona', 'Ujemna strona']))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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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": "3.9.13"
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},
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"latex_envs": {
|
||||
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||||
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"cite_by": "apalike",
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@ -0,0 +1,358 @@
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{
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||||
"cells": [
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||||
{
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||||
"cell_type": "code",
|
||||
"execution_count": 1,
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||||
"id": "32d973e1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from load_data import get_dataset\n",
|
||||
"import numpy as np\n",
|
||||
"from tabulate import tabulate\n",
|
||||
"\n",
|
||||
"X_train, y_train, X_test, y_test = get_dataset(new_size=64)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e63a5e75-7a26-4a97-b53d-67b858345126",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Zadanie 1 (2 pkt)\n",
|
||||
"\n",
|
||||
"Rozwiń algorytm regresji logistycznej z lab. 1, wprowadzając do niego człon regularyzacyjny."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "ea300c45",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class LogisticRegression():\n",
|
||||
" def __init__(self, l2=1):\n",
|
||||
" self.l2 = l2\n",
|
||||
"\n",
|
||||
" def mapY(self, y, cls):\n",
|
||||
" m = len(y)\n",
|
||||
" yBi = np.matrix(np.zeros(m)).reshape(m, 1)\n",
|
||||
" yBi[y == cls] = 1.\n",
|
||||
" return yBi\n",
|
||||
"\n",
|
||||
" def indicatorMatrix(self, y):\n",
|
||||
" classes = np.unique(y.tolist())\n",
|
||||
" m, k = len(y), len(classes)\n",
|
||||
" # k = len(classes)\n",
|
||||
" Y = np.matrix(np.zeros((m, k)))\n",
|
||||
" for i, cls in enumerate(classes):\n",
|
||||
" Y[:, i] = self.mapY(y, cls)\n",
|
||||
" return Y\n",
|
||||
" \n",
|
||||
" # Zapis macierzowy funkcji softmax\n",
|
||||
" def softmax(self, X):\n",
|
||||
" return np.exp(X) / np.sum(np.exp(X))\n",
|
||||
" \n",
|
||||
" # Funkcja regresji logistcznej\n",
|
||||
" def h(self, theta, X):\n",
|
||||
" return 1.0 /(1.0 + np.exp(-X * theta))\n",
|
||||
" \n",
|
||||
" # Funkcja kosztu dla regresji logistycznej\n",
|
||||
" def J(self, h, theta, X, y):\n",
|
||||
" m = len(y)\n",
|
||||
" h_val = h(theta, X)\n",
|
||||
" s1 = np.multiply(y, np.log(h_val))\n",
|
||||
" s2 = np.multiply((1 - y), np.log(1 - h_val))\n",
|
||||
" s3 = np.sum(s1+s2, axis=0)/m\n",
|
||||
" s4 = (self.l2 * np.sum(np.square(theta))) / 2*m\n",
|
||||
" return -s3 + s4\n",
|
||||
"\n",
|
||||
" # Gradient dla regresji logistycznej\n",
|
||||
" def dJ(self, h, theta, X, y):\n",
|
||||
" return 1.0 / (self.l2/len(y)) * (X.T * (h(theta, X) - y))\n",
|
||||
"\n",
|
||||
" # Metoda gradientu prostego dla regresji logistycznej\n",
|
||||
" def GD(self, h, fJ, fdJ, theta, X, y, alpha=0.01, eps=10**-3, maxSteps=1000):\n",
|
||||
" errorCurr = fJ(h, theta, X, y)\n",
|
||||
" errors = [[errorCurr, theta]]\n",
|
||||
" while True:\n",
|
||||
" # oblicz nowe theta\n",
|
||||
" theta = theta - alpha * fdJ(h, theta, X, y)\n",
|
||||
" # raportuj poziom błędu\n",
|
||||
" errorCurr, errorPrev = fJ(h, theta, X, y), errorCurr\n",
|
||||
" # kryteria stopu\n",
|
||||
" if abs(errorPrev - errorCurr) <= eps:\n",
|
||||
" break\n",
|
||||
" if len(errors) > maxSteps:\n",
|
||||
" break\n",
|
||||
" errors.append([errorCurr, theta]) \n",
|
||||
" # return theta, errors\n",
|
||||
" return theta\n",
|
||||
"\n",
|
||||
" def trainMaxEnt(self, X, Y):\n",
|
||||
" n = X.shape[1]\n",
|
||||
" thetas = []\n",
|
||||
" for c in range(Y.shape[1]):\n",
|
||||
" YBi = Y[:,c]\n",
|
||||
" theta = np.matrix(np.random.random(n)).reshape(n,1)\n",
|
||||
" # Macierz parametrów theta obliczona dla każdej klasy osobno.\n",
|
||||
" # thetaBest, errors = self.GD(self.h, self.J, self.dJ, theta, \n",
|
||||
" # X, YBi, alpha=0.1, eps=10**-4)\n",
|
||||
" # thetas.append(thetaBest)\n",
|
||||
" thetas.append(self.GD(self.h, self.J, self.dJ, theta, X, YBi, alpha=0.1, eps=10**-4))\n",
|
||||
" return thetas\n",
|
||||
"\n",
|
||||
" def classify(self, thetas, X):\n",
|
||||
" regs = np.array([(X*theta).item() for theta in thetas])\n",
|
||||
" return np.argmax(self.softmax(regs))\n",
|
||||
" # probs = self.softmax(regs)\n",
|
||||
" # result = np.argmax(probs)\n",
|
||||
" # return result\n",
|
||||
"\n",
|
||||
" def class_score(self, expected, predicted):\n",
|
||||
" # accuracy = TP + TN / FP + FN + TP + TN\n",
|
||||
" accuracy = sum(1 for exp, pred in zip(expected, predicted) if exp == pred) / len(expected)\n",
|
||||
" # precision = TP / FP + TP\n",
|
||||
" precision = sum(\n",
|
||||
" 1 for exp, pred in zip(expected, predicted) if exp == 1.0 and pred == 1.0) / sum(\n",
|
||||
" 1 for exp, pred in zip(expected, predicted) if exp == 1.0)\n",
|
||||
" # recall = TP / FN + TP\n",
|
||||
" recall = sum(\n",
|
||||
" 1 for exp, pred in zip(expected, predicted) if exp == 1.0 and pred == 1.0) / sum(\n",
|
||||
" 1 for exp, pred in zip(expected, predicted) if pred == 1.0)\n",
|
||||
" f1 = (2 * precision * recall) / (precision + recall)\n",
|
||||
" return accuracy, precision, recall, f1\n",
|
||||
"\n",
|
||||
" def fit(self, X_train, y_train):\n",
|
||||
" # Y = self.indicatorMatrix(y_train)\n",
|
||||
" # self.thetas = self.trainMaxEnt(X_train, Y)\n",
|
||||
" self.thetas = self.trainMaxEnt(X_train, self.indicatorMatrix(y_train))\n",
|
||||
"\n",
|
||||
" def predict(self, X_test):\n",
|
||||
" return np.array([self.classify(self.thetas, x) for x in X_test])\n",
|
||||
"\n",
|
||||
" def accuracy(self, expected, predicted):\n",
|
||||
" return sum(1 for x, y in zip(expected, predicted) if x == y) / len(expected)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "84fc2187",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_43367/551403934.py:33: RuntimeWarning: divide by zero encountered in log\n",
|
||||
" s2 = np.multiply((1 - y), np.log(1 - h_val))\n",
|
||||
"/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_43367/551403934.py:33: RuntimeWarning: invalid value encountered in multiply\n",
|
||||
" s2 = np.multiply((1 - y), np.log(1 - h_val))\n",
|
||||
"/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_43367/551403934.py:26: RuntimeWarning: overflow encountered in exp\n",
|
||||
" return 1.0 /(1.0 + np.exp(-X * theta))\n",
|
||||
"/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_43367/551403934.py:32: RuntimeWarning: divide by zero encountered in log\n",
|
||||
" s1 = np.multiply(y, np.log(h_val))\n",
|
||||
"/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_43367/551403934.py:32: RuntimeWarning: invalid value encountered in multiply\n",
|
||||
" s1 = np.multiply(y, np.log(h_val))\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 16x16, l2=0.01 -> 5m 11.4s\n",
|
||||
"# 32x32, l2=0.1 -> 20m 31.3s\n",
|
||||
"# 64x64, l2=0.1 -> 219m 10.8s\n",
|
||||
"logreg = LogisticRegression(l2=0.1)\n",
|
||||
"logreg.fit(X_train, y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "f2a4b169",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_43367/551403934.py:22: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
" return np.exp(X) / np.sum(np.exp(X))\n",
|
||||
"/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_43367/551403934.py:22: RuntimeWarning: overflow encountered in exp\n",
|
||||
" return np.exp(X) / np.sum(np.exp(X))\n"
|
||||
]
|
||||
},
|
||||
{
|
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"data": {
|
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"execution_count": 4,
|
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"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"logreg.accuracy(y_test, logreg.predict(X_test))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "945d1169-a13c-44a5-ad51-73ec62438487",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Zadanie 2 (4 pkt)\n",
|
||||
"\n",
|
||||
"Zaimplementuj algorytm SVM z miękkim marginesem (regularyzacją)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
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"id": "7a58dff1-f85d-4e07-b5e4-b639fc2af3ac",
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"metadata": {
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"deletable": false,
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"nbgrader": {
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"cell_type": "code",
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"checksum": "d0e86f0fc352ea3a95940ad21ba9cd6c",
|
||||
"grade": true,
|
||||
"grade_id": "cell-2e22217733fd5f34",
|
||||
"locked": false,
|
||||
"points": 4,
|
||||
"schema_version": 3,
|
||||
"solution": true,
|
||||
"task": false
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class SVM():\n",
|
||||
" def __init__(self, lr=0.001, lambda_param=10**-6, n_iters=1000):\n",
|
||||
" self.lr = lr\n",
|
||||
" self.lambda_param = lambda_param\n",
|
||||
" self.n_iters = n_iters\n",
|
||||
"\n",
|
||||
" def _mapY(self, y, cls):\n",
|
||||
" m = len(y)\n",
|
||||
" yBi = np.matrix(np.zeros(m)).reshape(m, 1)\n",
|
||||
" yBi[y == cls] = 1.\n",
|
||||
" return yBi\n",
|
||||
"\n",
|
||||
" def _indicatorMatrix(self, y):\n",
|
||||
" classes = np.unique(y.tolist())\n",
|
||||
" m, k = len(y), len(classes)\n",
|
||||
" Y = np.matrix(np.zeros((m, k)))\n",
|
||||
" for i, cls in enumerate(classes):\n",
|
||||
" Y[:, i] = self._mapY(y, cls)\n",
|
||||
" return Y\n",
|
||||
"\n",
|
||||
" def fit(self, X, y):\n",
|
||||
" n_classes = len(np.unique(y))\n",
|
||||
" y = self._indicatorMatrix(y)\n",
|
||||
" y = np.where(y == 0, -1, 1)\n",
|
||||
"\n",
|
||||
" n_features = X.shape[1]\n",
|
||||
" self.weights, self.biases = [], []\n",
|
||||
"\n",
|
||||
" for cls in range(n_classes):\n",
|
||||
" y_ = y[:,cls]\n",
|
||||
" y_ = np.where(y_ <= 0, -1, 1)\n",
|
||||
"\n",
|
||||
" w, b = np.zeros(n_features), 0\n",
|
||||
"\n",
|
||||
" for _ in range(self.n_iters):\n",
|
||||
" for idx, x_i in enumerate(X):\n",
|
||||
" condition = y_[idx] * (np.dot(x_i, w) - b) >= 1\n",
|
||||
" if condition:\n",
|
||||
" w -= self.lr * (2 * self.lambda_param * w)\n",
|
||||
" else:\n",
|
||||
" w -= self.lr * (2 * self.lambda_param * w - np.dot(x_i, y_[idx]))\n",
|
||||
" b -= self.lr * y_[idx]\n",
|
||||
" self.weights.append(w)\n",
|
||||
" self.biases.append(b)\n",
|
||||
"\n",
|
||||
" def _classify(self, x):\n",
|
||||
" cls = [np.sign(np.dot(x, self.weights[i]) - self.biases[i]) for i in range(len(self.biases))]\n",
|
||||
" return cls.index(1.0) if 1.0 in cls else 0\n",
|
||||
"\n",
|
||||
" def predict(self, X):\n",
|
||||
" return list(map(lambda x: self._classify(x), X))\n",
|
||||
"\n",
|
||||
" def accuracy(self, expected, predicted):\n",
|
||||
" return sum(1 for x, y in zip(expected, predicted) if x == y) / len(expected)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "62857aa5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 16x16 -> 1m 28.8s\n",
|
||||
"# 32x32 -> 2m 2.2s\n",
|
||||
"# 64x64 -> 4m 55.3s\n",
|
||||
"\n",
|
||||
"svm = SVM()\n",
|
||||
"svm.fit(X_train, y_train)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "27bdbf4f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
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"text/plain": [
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"0.32432432432432434"
|
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|
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},
|
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"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"svm.accuracy(y_test, svm.predict(X_test))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "0aa0c9d6",
|
||||
"metadata": {},
|
||||
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|
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"source": []
|
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}
|
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],
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|
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|
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"display_name": "base",
|
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|
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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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|
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|
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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": "3.9.13"
|
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},
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|
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|
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},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Loading…
Reference in New Issue