Add lab1
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Iwona Christop
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9f64b3a3a8
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456
sw_lab1.ipynb
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456
sw_lab1.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": 75,
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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": 76,
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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": 77,
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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": 78,
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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": 79,
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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=10000):\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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" def score(self, expected, predicted):\n",
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" score = {\n",
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" 'Class' : [], \n",
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" 'Accuracy': [],\n",
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" 'Precision': [],\n",
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" 'Recall': [],\n",
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" 'F1': []}\n",
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"\n",
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" oh_expected = self.indicatorMatrix(expected).T.tolist()\n",
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" oh_predicted = self.indicatorMatrix(predicted).T.tolist()\n",
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" n_classes = len(oh_expected)\n",
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"\n",
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" for i in range(n_classes):\n",
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" e = oh_expected[i]\n",
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" p = oh_predicted[i]\n",
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" a, p, r, f1 = self.class_score(e, p)\n",
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" score['Class'].append(i)\n",
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" score['Accuracy'].append(a)\n",
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" score['Precision'].append(p)\n",
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" score['Recall'].append(r)\n",
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" score['F1'].append(f1)\n",
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"\n",
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" score['Class'].append('Global')\n",
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" score['Accuracy'].append(sum(1 for exp, pred in zip(expected, predicted) if exp == pred) / len(expected))\n",
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" score['Precision'].append(np.mean(score['Precision']))\n",
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" score['Recall'].append(np.mean(score['Recall']))\n",
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" score['F1'].append(np.mean(score['F1']))\n",
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"\n",
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" return score\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": 86,
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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/7c/v61kq2b95dzbt7s47fxy0grm0000gn/T/ipykernel_2525/2624688826.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/7c/v61kq2b95dzbt7s47fxy0grm0000gn/T/ipykernel_2525/2624688826.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/7c/v61kq2b95dzbt7s47fxy0grm0000gn/T/ipykernel_2525/2624688826.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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Class Accuracy Precision Recall F1\n",
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"------- ---------- ----------- -------- --------\n",
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"0 0.96139 0.823529 0.976744 0.893617\n",
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"1 0.857143 0.557692 0.674419 0.610526\n",
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"2 0.872587 0.788462 0.650794 0.713043\n",
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"3 0.861004 0.596154 0.673913 0.632653\n",
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"4 0.776062 0.557692 0.453125 0.5\n",
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"Global 0.664093 0.664706 0.685799 0.669968\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=32) \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)\n",
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"score = logreg.score(y_test, predicted)\n",
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"\n",
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"print(tabulate(score, headers='keys'))"
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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": 85,
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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": [
|
||||
" Klasa Dodatnia strona Ujemna strona\n",
|
||||
"------- ----------------- ---------------\n",
|
||||
" 0 48 211\n",
|
||||
" 1 52 207\n",
|
||||
" 2 65 194\n",
|
||||
" 3 47 212\n",
|
||||
" 4 47 212\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"one_hot = logreg.indicatorMatrix(predicted)\n",
|
||||
"length = len(one_hot)\n",
|
||||
"one_hot = one_hot.sum(axis=0).tolist()[0]\n",
|
||||
"\n",
|
||||
"hyperplane = [\n",
|
||||
" [i for i in np.unique(predicted)], \n",
|
||||
" [int(x) for x in one_hot],\n",
|
||||
" [length - int(x) for x in one_hot]]\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"print(tabulate(np.array(hyperplane).T, headers=['Klasa', 'Dodatnia strona', 'Ujemna strona']))"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.10.5 64-bit",
|
||||
"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": "3.10.5"
|
||||
},
|
||||
"latex_envs": {
|
||||
"LaTeX_envs_menu_present": true,
|
||||
"autoclose": false,
|
||||
"autocomplete": true,
|
||||
"bibliofile": "biblio.bib",
|
||||
"cite_by": "apalike",
|
||||
"current_citInitial": 1,
|
||||
"eqLabelWithNumbers": true,
|
||||
"eqNumInitial": 1,
|
||||
"hotkeys": {
|
||||
"equation": "Ctrl-E",
|
||||
"itemize": "Ctrl-I"
|
||||
},
|
||||
"labels_anchors": false,
|
||||
"latex_user_defs": false,
|
||||
"report_style_numbering": false,
|
||||
"user_envs_cfg": false
|
||||
},
|
||||
"toc": {
|
||||
"base_numbering": 1,
|
||||
"nav_menu": {},
|
||||
"number_sections": false,
|
||||
"sideBar": true,
|
||||
"skip_h1_title": false,
|
||||
"title_cell": "Table of Contents",
|
||||
"title_sidebar": "Contents",
|
||||
"toc_cell": false,
|
||||
"toc_position": {},
|
||||
"toc_section_display": true,
|
||||
"toc_window_display": false
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "7e1998ff7f8aa20ada591c520b972326324e5ea05489af9e422744c7c09f6dad"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user