359 lines
12 KiB
Plaintext
359 lines
12 KiB
Plaintext
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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": "32d973e1",
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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 tabulate import tabulate\n",
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"\n",
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"X_train, y_train, X_test, y_test = get_dataset(new_size=64)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e63a5e75-7a26-4a97-b53d-67b858345126",
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"metadata": {},
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"source": [
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"# Zadanie 1 (2 pkt)\n",
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"\n",
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"Rozwiń algorytm regresji logistycznej z lab. 1, wprowadzając do niego człon regularyzacyjny."
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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": 2,
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"id": "ea300c45",
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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 __init__(self, l2=1):\n",
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" self.l2 = l2\n",
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"\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, k = len(y), len(classes)\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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" s3 = np.sum(s1+s2, axis=0)/m\n",
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" s4 = (self.l2 * np.sum(np.square(theta))) / 2*m\n",
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" return -s3 + s4\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 / (self.l2/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)\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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" return theta\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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" thetas.append(self.GD(self.h, self.J, self.dJ, theta, X, YBi, alpha=0.1, eps=10**-4))\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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" return np.argmax(self.softmax(regs))\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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" self.thetas = self.trainMaxEnt(X_train, self.indicatorMatrix(y_train))\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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" 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)"
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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": 3,
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"id": "84fc2187",
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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_43367/551403934.py:33: 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_43367/551403934.py:33: 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_43367/551403934.py:26: RuntimeWarning: overflow encountered in exp\n",
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" return 1.0 /(1.0 + np.exp(-X * theta))\n",
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"/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_43367/551403934.py:32: RuntimeWarning: divide by zero encountered in log\n",
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" s1 = np.multiply(y, np.log(h_val))\n",
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"/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_43367/551403934.py:32: RuntimeWarning: invalid value encountered in multiply\n",
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" s1 = np.multiply(y, np.log(h_val))\n"
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]
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}
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],
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"source": [
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"# 16x16, l2=0.01 -> 5m 11.4s\n",
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"# 32x32, l2=0.1 -> 20m 31.3s\n",
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"# 64x64, l2=0.1 -> 219m 10.8s\n",
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"logreg = LogisticRegression(l2=0.1)\n",
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"logreg.fit(X_train, y_train)"
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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": 4,
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"id": "f2a4b169",
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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_43367/551403934.py:22: RuntimeWarning: invalid value encountered in true_divide\n",
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" return np.exp(X) / np.sum(np.exp(X))\n",
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"/var/folders/3r/c8tg1h051m18qhsdccdysrt40000gn/T/ipykernel_43367/551403934.py:22: RuntimeWarning: overflow encountered in exp\n",
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" return np.exp(X) / np.sum(np.exp(X))\n"
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]
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},
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{
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"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": 4,
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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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"logreg.accuracy(y_test, logreg.predict(X_test))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "945d1169-a13c-44a5-ad51-73ec62438487",
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"metadata": {},
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"source": [
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"# Zadanie 2 (4 pkt)\n",
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"\n",
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"Zaimplementuj algorytm SVM z miękkim marginesem (regularyzacją)."
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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": 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",
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"grade": true,
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"grade_id": "cell-2e22217733fd5f34",
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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 SVM():\n",
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" def __init__(self, lr=0.001, lambda_param=10**-6, n_iters=1000):\n",
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" self.lr = lr\n",
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" self.lambda_param = lambda_param\n",
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" self.n_iters = n_iters\n",
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"\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, k = len(y), 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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" def fit(self, X, y):\n",
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" n_classes = len(np.unique(y))\n",
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" y = self._indicatorMatrix(y)\n",
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" y = np.where(y == 0, -1, 1)\n",
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"\n",
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" n_features = X.shape[1]\n",
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" self.weights, self.biases = [], []\n",
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"\n",
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" for cls in range(n_classes):\n",
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" y_ = y[:,cls]\n",
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" y_ = np.where(y_ <= 0, -1, 1)\n",
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"\n",
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" w, b = np.zeros(n_features), 0\n",
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"\n",
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" for _ in range(self.n_iters):\n",
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" for idx, x_i in enumerate(X):\n",
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" condition = y_[idx] * (np.dot(x_i, w) - b) >= 1\n",
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" if condition:\n",
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" w -= self.lr * (2 * self.lambda_param * w)\n",
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" else:\n",
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" w -= self.lr * (2 * self.lambda_param * w - np.dot(x_i, y_[idx]))\n",
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" b -= self.lr * y_[idx]\n",
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" self.weights.append(w)\n",
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" self.biases.append(b)\n",
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"\n",
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" def _classify(self, x):\n",
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" cls = [np.sign(np.dot(x, self.weights[i]) - self.biases[i]) for i in range(len(self.biases))]\n",
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" return cls.index(1.0) if 1.0 in cls else 0\n",
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"\n",
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" def predict(self, X):\n",
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" return list(map(lambda x: self._classify(x), X))\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)"
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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": 6,
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"id": "62857aa5",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 16x16 -> 1m 28.8s\n",
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"# 32x32 -> 2m 2.2s\n",
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"# 64x64 -> 4m 55.3s\n",
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"\n",
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"svm = SVM()\n",
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"svm.fit(X_train, y_train)\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": 7,
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"id": "27bdbf4f",
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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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"0.32432432432432434"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"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": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "base",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.9.13"
|
||
|
|
},
|
||
|
|
"vscode": {
|
||
|
|
"interpreter": {
|
||
|
|
"hash": "83181d593ff87630d4a0d7997796f399a1d96b6a7eabd5b16ad531f517f6300b"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|