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forked from tdwojak/Python2019
Python2019/labs05/sklearn cz. 3.ipynb
2019-02-10 08:39:58 +01:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Uczenie nienadzorowane"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Do tej pory zajmowaliśmy się uczeniem nadzorowanym (ang. *supervised*), tj. takimi przypadkami, gdy zbiór trenujący składał się z dwóch zmiennych `X` i `y`, a naszym zadaniem było przewidzenia `y` na podstawie danych z `X`. Ponadto poznaliśmy odpowiednie metryki, które pozwalały nam zmierzyć jak dobrze (lub) źle działają modele, które wytrenowaliśmy.\n",
"\n",
"Przypomnijmy, że na uczenie maszynowe składają się trzy paradygmaty:\n",
" * supervised learning\n",
" * unsupervised learning\n",
" * reinforcement learning\n",
" \n",
"Dzisiejsze zajęcia są poświęcone drugiemu paradygmatowi, czyli uczeniu nienadzorowanym, a dokładniej automatycznemu klastrowaniu. Do klastrowania służą m.in. następujące algorytmy:\n",
" * K-średnich (ang. *k-means*)\n",
" * [DB-SCAN](https://en.wikipedia.org/wiki/DBSCAN)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Zadanie 0**: wczytaj do zmiennej `points` zbiór danych z pliku `points.csv`. Uwaga: kolumny są rozdzielone spacją. Plik nie zawiera nagłówka."
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [],
"source": [
"points = pd.read_csv('points.csv', sep=' ', header=None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Narysujmy wykres z wyżej wczytanych punktów."
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f02a0cecba8>"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f02a0cf7940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xs = points[0]\n",
"ys = points[1]\n",
"\n",
"points.plot(kind='scatter', x=0, y=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**zadanie 1** Ile dostrzegasz rozdzielnych grup punktów na powyższym wykresie?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Podstawowym akgorytmem do klastrowania danych jest $k$-średnich albo k-means, który został omówiony na wykładzie. Oczywiście biblioteka `sklearn` zawiera implementację tego algorytmu."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**zadanie 2** Wczytaj z biblioteki `sklearn.cluster` klasę `KMeans`."
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Algorytm k-means wymaga podania oczekiwanej liczby klas, dlatego podczas tworzenia obiektu `KMeans` musimy podać parametr `n_clusters`. W poniższym przykładzie ustawiamy powyższy parametr na 3."
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [],
"source": [
"kmeans = KMeans(n_clusters=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**zadanie 3** Wywołaj metodę `fit` na obiekcie `kmeans` i jako parametr przekaż zmienną `points`. W taki sposób wytrenujesz model."
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
" n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',\n",
" random_state=None, tol=0.0001, verbose=0)"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**zadanie 4** Mając wytrenowany model k-średnich, możemy wyznaczyć klaster, do którego został przydzielony każdy z punktów. Służy do tego komenda *predict*. Wywołaj tę komendę na zmiennej *points* i zapisz wynik do zmiennej *clusters*."
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wyświetlmy, w jaki sposób model podzielił punkty:"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f02a3ee7c88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x=points[0], y=points[1], c=clusters)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Informacje o centroidach są przechowywwane w atrybucie `cluster_centers_`:"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Claster ID: 0\tX: 1158.9296227871434\tY:-212.28055211754568\n",
"Claster ID: 1\tX: -844.3076877296985\tY:-450.0715318089522\n",
"Claster ID: 2\tX: 60.61234354820601\tY:444.84943020237415\n"
]
}
],
"source": [
"for idx, centroid in enumerate(kmeans.cluster_centers_):\n",
" print(\"Claster ID: {}\\tX: {}\\tY:{}\".format(idx, centroid[0], centroid[1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**zadanie 5** Sprawdź, w jaki sposób podzieli zbiór punktów model k-średnich, jeżeli ustawimy liczbę klastrów na 2 i 4."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Algorytm k-średnich minimalizuje sumę odległości do najbliżsego centroidu, co możemy traktować jako funkcje kosztu i wykorzystać to porównania pomiędzy modelami z różnymi liczbami klastrów."
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f02a4eb62b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n_clusters = [1, 2, 3, 4, 5]\n",
"inertias = []\n",
"\n",
"for n_cluster in n_clusters:\n",
" model = KMeans(n_clusters=n_cluster)\n",
" model.fit(points)\n",
" inertias.append(model.inertia_)\n",
"\n",
"plt.plot(n_clusters, inertias)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Powyższy wykres przedstawia zależność pomiędzy liczbą klastrów, a funkcją kosztu. Można łatwo zauważyć, powyżej 3 klastrów zależność na wygładza się. Stąd, liczba 3 wydaje się być najlepszym wyborem."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Drugim popularnym algorytmem jest DB-SCAN, który nie wymaga `a priori` podania liczby klastrów, którą sam ją wyznacza. Ponadto, cechą tego modelu jest możliwość pominięcia niektórych punktów, które są oddalone od skupisk."
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.cluster import DBSCAN"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Model DB-SCAN przyjmuje dwa parametry: eps - odległość pomiędzy punktami i minimalną liczbę punktów potrzebna do utworzenia klastra."
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, ..., 2, 2, 2])"
]
},
"execution_count": 168,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db = DBSCAN(eps=130, min_samples=10)\n",
"labels = db.fit_predict(points)\n",
"labels"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f02adea9550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x=points[0], y=points[1], c=labels)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**zadanie 6** Przeskaluj dane, tak aby miały rozkład standardowy (średnia = 0 , std = 1). I uruchom model SB-SCAN i k-średnich. Czy normalizacja zmieniła coś?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Redukcja wymiaru\n",
"\n",
"Jedną z wad algorytmu k-średnich jest czas trenowania, który rośnie z wymiarem danych, jak ich z liczbą przykładów trenujących. Podstawową techniką w takim przypadku jest zmniejszenie wymiarowości danych. Najprostszą techniką jest [PCA](https://en.wikipedia.org/wiki/Principal_component_analysis)."
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ściągnijmy zbiór dancych MNIST, który pojawił się na naszych zajęciach."
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {},
"outputs": [],
"source": [
"mnist = fetch_mldata('MNIST original')\n",
"X = mnist.data.astype('float64')\n",
"y = mnist.target"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Podczas tworzeania PCA, możemy podać wyjsciową liczbę wymiarów (argument *n_components*). "
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PCA(copy=True, iterated_power='auto', n_components=10, random_state=None,\n",
" svd_solver='auto', tol=0.0, whiten=False)"
]
},
"execution_count": 190,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pca = PCA(n_components=10)\n",
"pca.fit(X)"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {},
"outputs": [],
"source": [
"mnist_pca = pca.transform(X)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**zadanie 7** Wytrenuj K-Means na wyjściu z PCA. Ustaw liczbę klastrów na 10. Ponadto zapisz do `mnist_clasters` numer klastra, do którego został on przydzielony."
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**zadanie 8** Zmienna `y` zawiera informację o prawidłowych oznaczeniach: tj. liczby od 0 do 9 (włącznie). Dla każdej cyfry *i* znajdz klaster *j*, w którym znajduje się najwięcej cyfr *i*."
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 7, 8, 3, 0, 2, 4, 1, 6, 0])"
]
},
"execution_count": 198,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**zadanie 9** mając wyznaczone klasy z poprzedniego zadania, sumuj liczbę elementów w najpopularniejszym klastrze."
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5762857142857143"
]
},
"execution_count": 200,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**zadanie 10** Oblicz accuracy biorąc wynik z poprzedniego zadania."
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**zadanie 11** Spróbuj podwyższych wynik, stosując np. normalizację lub zmieniając parametry."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Gratuluję!**"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}