{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import sklearn.metrics as met\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.cluster import KMeans\n", "import matplotlib.pyplot as plt\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Prvih 5 instanci\n", " breed height weight\n", "0 Border Collie 20 45\n", "1 Boston Terrier 16 20\n", "2 Brittany Spaniel 18 35\n", "3 Bullmastiff 27 120\n", "4 Chihuahua 8 8\n" ] } ], "source": [ "#ucitavanje podataka\n", "df = pd.read_csv(\"C:/Users/mira/Desktop/ipIndustija4/ipVezbe112021/primer1/dogs.csv\")\n", "\n", "#prikaz imena kolona + 5 prvih instanci\n", "print('Prvih 5 instanci')\n", "print(df.head())\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#za klasterovanje ce se koristiti atributi visina i tezina psa,\n", "#a rasa nece biti uzeta u obzir\n", "features = df.columns[1:]\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#normalizacija podataka\n", "scaler = MinMaxScaler().fit(df[features])\n", "x = pd.DataFrame(scaler.transform(df[features]))\n", "\n", "#dodela imena kolonama u transformisanom skupu\n", "x.columns = features\n" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "\n", "K-means\n", "\n", "parametri:\n", "\n", "n_clusters : broj klastera\n", " default: 8\n", "max_iter : maksimalan broj iteracija\n", " default: 300\n", "n_init : broj izvrsavanja algoritma sa razlicitim inicijalnim vrednostima\n", " default: 10\n", "init : metode za inicijalizaciju {‘k-means++’, ‘random’ or an ndarray}\n", " default:‘k-means++’:\n", "tol : tolerancija za konvergenciju\n", " default: 1e-4\n", "\n", "atributi:\n", "\n", "cluster_centers_ : centroidi klastera\n", "labels_ : klaster svake instance\n", "inertia_ : suma rastojanja izmedju instance i najblizeg centorida\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#Definisanje boja koje se koriste pri crtanju instanci klastera.\n", "#Instance jednog klastera ce biti prikazane istom bojom.\n", "colors = ['red', 'green', 'gold', 'blue', 'black']\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5813924452451538\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# primena klasterovanja\n", "est=KMeans(n_clusters=3)\n", "est.fit(x)\n", "#svakoj instanci se pridruzuje oznaka klastera kome je dodeljena\n", "df['labels']= est.labels_\n", "\n", "#crtanje klastera\n", "for j in range(0,3):\n", " \n", " #izdvajanje instanci klastera koji se obradjuje\n", " cluster= df.loc[df['labels'] == j, :]\n", "\n", " #crtanje instanci klastera pomocu seme sa rasprsenim elementima\n", " plt.scatter(cluster['height'], cluster['weight'], color=colors[j], s=30, marker='o', label=\"klaster {0}\".format(j))\n", "\n", "#transformacija izracunarih centroida u vrednosti originalnog skupa\n", "centers = pd.DataFrame(scaler.inverse_transform(est.cluster_centers_), columns=features)\n", "\n", "#crtanje crnom bojom centorida na semi sa rasprsenim elementima, na kojoj su oznaceni sa x\n", "plt.scatter(centers['height'], centers['weight'], color='black', marker='x', label='centroidi')\n", "t=plt.legend() #t=... je dodato da ne bi bilo ispisa iznad slike; moze samo plt.legend()\n", "\n", "print(met.silhouette_score(x, df['labels']))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#pravljenje slike sa graficima i definisanje dimenzija slike\n", "fig = plt.figure(figsize=(10,10))\n", "\n", "#indeks dela za iscrtavanje\n", "plt_ind=1\n", "\n", "#primena klasterovanja za razlicit broj klastera (parametar n_clusters): 2, 3 i 4\n", "for i in range(2,5):\n", "\n", " # primena i razlicite metode za inicijalizaciju centroida\n", " for init in ['k-means++', 'random']:\n", "\n", " #primena klasterovanja nad transformisanim skupom\n", " est=KMeans(n_clusters=i, init=init)\n", " est.fit(x)\n", "\n", " #Svakoj instanci u originalnom skupu se dodeljuje\n", " # oznaka klastera kome pripada.\n", " # Oznake klastera su u itervalu [0, i-1].\n", " df['labels']= est.labels_\n", "\n", " #Metod add_subplot(broj_redova, broj_kolona, indeks_celije_za_crtanje)\n", " #deli sliku na broj_redova*broj_kolona celija i omogucava zadavanje\n", " #celije u kojoj ce se izvrsiti naredna crtanja.\n", " #Celija u prvom redu i prvoj koloni ima indeks 1. Indeksi celija se\n", " # povecavaju po kolonama u jednom redu, a nakon poslednje celije u\n", " # jednom redu, prelazi se na naredni red.\n", " # Sa add_subplot(3, 2, plt_ind) slika se deli na 3 reda i svaki od\n", " # njih na 2 kolone.\n", " # x x \n", " # x x \n", " # x x \n", "\n", " # indeksi po celijama su\n", " # 1 2 \n", " # 3 4 \n", " # 5 6 \n", "\n", " fig.add_subplot(3, 2, plt_ind)\n", "\n", " #Svakom klasteru (oznake klastera su u intervalu [0, i-1]) se dodeljuje jedinstvena\n", " #boja. Instance jednog klastera se crtaju pomocu seme sa rasprsenim elementima i\n", " #boje se bojom koja je dodeljena njihovom klasteru. Na x osi se predstavlja prava\n", " #visina psa, a na y osi prava tezina psa. Instance su predstavljene kao tacke.\n", " for j in range(0,i):\n", "\n", " #izdvajanje instanci klastera koji se obradjuje\n", " cluster= df.loc[df['labels'] == j, :]\n", "\n", " #crtanje instanci klastera pomocu seme sa rasprsenim elementima\n", " plt.scatter(cluster['height'], cluster['weight'], color=colors[j], s=30, marker='o', label=\"klaster %d\"%j)\n", "\n", " #transformacija izracunarih centroida u vrednosti originalnog skupa\n", " centers = pd.DataFrame(scaler.inverse_transform(est.cluster_centers_), columns=features)\n", "\n", " #crtanje crnom bojom centorida na semi sa rasprsenim elementima, na kojoj su oznaceni sa x\n", " plt.scatter(centers['height'], centers['weight'], color='black', marker='x', label='centroidi')\n", "\n", " #postavljanje legende i naslova (koji sadrzi parametre klasterovanja i SSE) za svaku celiju\n", " plt.legend(loc='lower right')\n", " plt.title('K-means, num clasters: %d, init: %s, inertia: %.2f' % (i, init, est.inertia_), fontsize=10)\n", "\n", " #prelazak u narednu celiju u kojoj ce se prikazati\n", " #rezultat klasterovanja sa novim parametrima\n", " plt_ind+=1\n", "#prikaz slike bez poklapanja celija\n", "plt.tight_layout()\n", "plt.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.15" } }, "nbformat": 4, "nbformat_minor": 2 }