{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Naiwna klasyfikacja bayesowska\n", "\n", "Naiwna klasyfikacja bayesowska jest to klasyfikacja polegająca na przydzielaniu obiektom prawdopodobieństwa przynależności do danej klasy. Naiwny klasyfikator jest to bardzo prosta metoda klasyfikacji jednak mimo swej prostoty sprawdza się w wielu przypadkach gdy bardziej złożone metody zawodzą.Jednym z powodów dla których naiwny klasyfikator wypadak dobrze jest jego założenie o niezależności predykatów, własnie dlatego nazywany jest klasyfikatorem naiwnym, naiwnie zakłada niezależność atrybutów opisujących dany przykład, co często nie ma odzwierciedlenia w rzeczywistości. Klasyfikator nazywany jets bayesowskim dlatego że wykorzystuje twierdzenie bayesa do obliczania prawdopodobieństw.\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Twierdzenie Bayesa\n", "\n", "Twierdzneie bayesa jets to twierdzenie tworii prawdopodobieństwa wiążące prawdopodobieństwa warunkowe dwóch zdarzeń warunkujących się nawzajem. Prawdopodobieństwo wystąpienia zdarzenia A gdy wystąpiło zdarzenie B oblicza się wzorem:\n", "\n", "$P(A|B) = \\frac{P(B|A) P(A)}{P(B)}$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wykorzystanie twierdzenia Bayesa w naiwnej klasyfikacji bayesowskiej\n", "\n", "1\\) Nawiązując do twierdzenia bayesa prawdopodobieństwo problem klasyfikacji danego obiektu możemy zapisać w następujący sposb:\n", "


\n", "$P(K|X)$ Zapis ten oznacza że mając obiekt X chcemy obliczyć prawdopodobieństo przynaleźności do klasy K\n", "\n", "$P(K|X) = \\frac{P(X|K) P(K)}{P(X)}$ \n", "\n", "$P(K)$ - jest to prawdopodobieństwo a-priori klasy K\n", "\n", "$P(X|K)$ - ten zapis możemy interpretować w taki sposób: jeżeli klasa K to szansa że X do niej należy \n", "\n", "$P(X)$ - ale jak interpretować prawdopodobieństwo od odibkeu X? okazuje się że nie trzeba tego obliczać gdyż obliczając prawpodobieństwa przynależności X do klasy K w mianowniku zawsze bęzie $P(X)$ więc możemy to pomijać\n", "\n", "Najlepiej będzie pokazać to na przykładzie:\n", "



\n", "2\\) Prawdopobobieństwa a-priori\n", "


\n", "Prawdopodobieństwo a priori jest to prawdopodobieństwo obliczane przed realizacją odpowiednich eksperymentów lub obserwacji.\n", "\n", "![klasy](https://www.statsoft.pl/textbook/graphics/xNaiveBayesIntro1.gif.pagespeed.ic.tNwdOtpcQH.webp)\n", "\n", "Powyższa ilustracja przedstawia 2 klasy czerwona(20 obiektów) i zielona(40 obiektów), prawdopodobieństwa a priori tym przykładzie to nic innego jak \n", "\n", "$\\frac{liczebność\\ klasy}{liczebność\\ wszystkich\\ elementów}$, więc prawdopodobieńswta a priori dla powyższego przykładu są równe\n", "\n", "\n", "a priori klasy zielonej = $\\frac{2}{3}\\\\$\n", "a priori klasy czerwonej = $\\frac{1}{3}\\\\$\n", "\n", "To jest nasze $P(K)$\n", "



\n", "3\\) Klasyfikacja nowego obiektu\n", "


\n", "![nowy obiekt](https://www.statsoft.pl/textbook/graphics/xNaiveBayesIntro4.gif.pagespeed.ic.kzJ0xzlOjU.webp)\n", "\n", "Klasyfikując nowy obiekt korzystamy z prawdopodobieństw a-priori, jednak same prawdopodobieństwa a-priori są niweystarczające, w tym przykładzie rozsądnym założeniem jest klasyfikowanie obiektu również na podstawie jego najbliższych sąsiadów. Zaznaczając okręgiem obszar możemy obliczyć prawdopodobieństwo że nowy obiekt będzie czerwony albo zielony, prawdopodobieństwa te obliczane są ze wzoru, prawdopodobieństwa te zostaną użyte do obliczenia prawdopodobieństwa a posteriori.\n", "\n", "$\\frac{liczba\\ obiektów\\ danej\\ klasy\\ w\\ sąsiedztwie\\ nowego\\ obiektu}{liczba\\ obiektów\\ danej\\ klasy}$ i wynoszą odpowiednio:\n", "\n", "prawdopodobieństow że obiekt będzie w klasie zielonej = $\\frac{1}{40}\\\\$\n", "prawdopodobieństow że obiekt będzie w klasie czerwonej = $\\frac{3}{20}\\\\$\n", "\n", "To jets nasze $P(X|K)$\n", "



\n", "4\\) Prawdopodobieństwo a posteriori\n", "


\n", "Mając już obliczone wszystkie potrzebne prawdopodobieństwa możemy obliczyć prawdopodobieństwa a posteriori\n", "\n", "Prawdopodobieństwo a posteriori jest to prawdopodobieństwo pewnego zdarzenia gdy wiedza o tym zdarzeniu wzbogacona jest przez pewne obserwacje lub eksperymenty.\n", "\n", "W naszym przykładzie prawdopodobieństwo a posteriori obliczymy ze wzoru \n", "\n", "$(prawdopodobieństwo\\ a\\ priori\\ przynależności\\ do\\ danej\\ klasy) * (prawdopodobieństwo\\ że\\ nowy\\ obiekt\\ będzie\\ w\\ danej\\ klasie\\ na\\ podstawie\\ jego\\ sąsiadów)$ czyli $P(X|K) P(K)$\n", "\n", "A więc prawdopodobieństwa a posteriori są równe:\n", "\n", "Prawdopodobieństwo a posteriori ze nowy obiekt będzie w klasie zielonej = $\\frac{2}{3} * \\frac{1}{40} = \\frac{1}{60}\\\\$\n", "Prawdopodobieństwo a posteriori ze nowy obiekt będzie w klasie czerwonej = $\\frac{1}{3} * \\frac{3}{40} = \\frac{1}{40}\\\\$\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Naiwna klasyfikacja bayesowska przy wielu cechach\n", "


\n", "Klasyfikator bayesowski możemy stosować na wielu cechach, wykonuje się to w następujący sposób:\n", "\n", "W naszym przypadku obiekt X posiada wiele cech $X = (x1, x2, x3, ... xn)$ wtedy stosując znany już wzór:\n", "\n", "$P(K|X) = \\frac{P(X|K) P(K)}{P(X)}$ \n", "\n", "$P(K)$ pozostaje niezmienne jest to $\\frac{liczba\\ elementów\\ klasy\\ K}{wszystkie\\ elementy\\ zbioru}$\n", "\n", "Element X posiada wiele cech więc:\n", "\n", "$P(X|K) = P(x1|K)*P(x2|K)*...*P(xn|K)$\n", "\n", "Zauażmy że w rzeczywistości $P(X|K)$ obliczymy z twierdzenia bayesa, jednak dzięki temu że nasz klasyfikator jest naiwny zakładamy niezależność cech $x1, x2,...,xn$ więc możemy uprościć obliczenia do powyższego wzoru\n", "


\n", "$P(xk|K) = \\frac{Liczba\\ elementów\\ klasy\\ K\\ dla\\ których\\ wartość\\ cechy\\ Ak\\ jest\\ równa\\ xk}{liczba\\ wszystkich\\ obiektów\\ klasy\\ K}$\n", "



\n", "Powyższy wzór ma jedna pewne problemy z zerowymi prawdopodobieństwami\n", "


\n", "Co w przypadku gdy dla którejś cechy $P(xk|K) = 0$\n", "\n", "Może się tak zdarzyć gdy cecha $Ak$ nie będzie przyjmowała wartości $xk$ danego obiektu $X$ wtedy zgodnie ze wzorem otrzymamy $\\frac{0}{liczba\\ wszystkich\\ obiektów\\ klasy\\ K}$\n", "Gdy tak się stanie obliczone prawdopodobieństwo będzie równe 0, a przecież brak wartości xk dla cechy Ak klasy K nie musi wcale oznaczać śe dany obiekt nie może należeć do klasy K. Aby temu zaradzić stosuje się wygładzanie\n", "


\n", "Wygładzanie Laplace'a\n", "


\n", "Wygładzanie Laplace'a zwane jest również wygładzaniem + 1, jest to bardzo prosty sposób wystarczy dla każdego $P(xk|K)$ dodać do licznika 1 a do mianownika dodać liczbę klas obiektu, dodając 1 do licznika możemy to interpretować jako dodanie nowego obiektu do klasy, robiąc to dla każdej klasy \"mamy\" dodatkowe obiekty równe liczbie klas dlatego do mianownika musimy dodać liczbę klas.\n", "\n", "$P(xk|K) = \\frac{(Liczba\\ elementów\\ klasy\\ K\\ dla\\ których\\ wartość\\ cechy\\ Ak\\ jest\\ równa\\ xk\\\\)\\ + 1}{(liczba\\ wszystkich\\ obiektów\\ klasy\\ K\\\\) + liczba klas}$\n", "\n", "Łatwo można zauważyć że samo dodanie 1 do licznika nie jest wystarczające ponieważ wtedy $P(xk|K)$ mogłoby być $> 1$\n", "


\n", "Dzięki wygładzaniu Laplace'a $P(X|K)$ nigdy nie bęzie zerowe minimalna wartość to\n", "\n", "$\\frac{1}{n *(liczba\\ wszystkich\\ obiektów\\ klasy\\ K\\\\) + liczba\\ klas}$\n", "


\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Implementacja\n", "\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "import pandas as pd\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n", "import scipy.stats as stats\n", "import numpy as np\n", "import plotly\n", "from plotly.subplots import make_subplots\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import random" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "class NaiveBayes():\n", "\n", " def __init__(self, classes, className, attribsNames, data):\n", " self.classes = classes\n", " self.className = className\n", " self.attribsNames = attribsNames\n", " self.data = data\n", "\n", " #przygotowanie prawdopodobienstw wartosci danych cech w zaleznosci od klasy\n", " def getDictOfAttribProbs(self):\n", " dictionaries = {}\n", " for value in self.classes:\n", " classFreq = {}\n", " for i in range(len(self.attribsNames)):\n", " classData = self.data[self.data[self.className] == value]\n", " freq = {}\n", " attribData = classData[self.attribsNames[i]]\n", " for attrib in attribData:\n", " count = freq.get(attrib, 1) + 1\n", " freq[attrib] = count\n", " freq = {k: v / len(classData) for k, v in freq.items()}\n", " classFreq[self.attribsNames[i]] = freq\n", " dictionaries[value] = classFreq\n", " return dictionaries\n", "\n", " #a priori dla klas\n", " def classProb(self, class_):\n", " x = len(self.data[self.data[self.className] == class_][self.className])\n", " y = len(self.data[self.className])\n", " return x / y\n", "\n", " #prawdopodobienstwo dla wartosic danej cechy w zaelznosci od klasy\n", " def getAttribProbs(self, attrib, value, data, clas, dictProbs):\n", " return dictProbs[clas][attrib].get(value, 1.0 / len(data))\n", "\n", " #a posteriori dla danego obiektu\n", " def getPosteriori(self, attribs, attribsNames, clas, dictProbs):\n", " dic = {}\n", " for i in range(len(attribs)):\n", " dic[attribsNames[i]] = attribs[i]\n", " sum = 0.0\n", " for key in dic:\n", " sum = sum + np.log(self.getAttribProbs(key, dic[key], X_train, clas, dictProbs))\n", " return sum + np.log(self.classProb(clas))\n", "\n", " #predykcja dla danych\n", " def predict(self, data, model):\n", " attribNames = data.columns\n", " predictions = []\n", " for i in range(len(data)):\n", " probs = {}\n", " for name in self.classes:\n", " probs[name] = self.getPosteriori(list(data.iloc[i]), list(attribNames), name, model)\n", " keyMax = max(zip(probs.values(), probs.keys()))[1]\n", " predictions.append(keyMax)\n", " return predictions\n", "\n", " def fitModel(self):\n", " probabilities = self.getDictOfAttribProbs()\n", " return probabilities\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "features = [\n", " 'edible', 'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',\n", " 'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',\n", " 'stalk-shape', 'stalk-root', 'stalk-surface-above-ring',\n", " 'stalk-surface-below-ring', 'stalk-color-above-ring',\n", " 'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number',\n", " 'ring-type', 'spore-print-color', 'population', 'habitat'\n", "]\n", "\n", "mushrooms = pd.read_csv('mushrooms.tsv', sep='\\t', names=features)\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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ediblecap-shapecap-surfacecap-colorbruisesodorgill-attachmentgill-spacinggill-sizegill-color...stalk-surface-below-ringstalk-color-above-ringstalk-color-below-ringveil-typeveil-colorring-numberring-typespore-print-colorpopulationhabitat
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1poisonousconvexscalybrownbruisespungentfreeclosenarrowbrown...smoothwhitewhitepartialwhiteonependantbrownseveralgrasses
2ediblebellscalywhitebruisesalmondfreeclosebroadwhite...smoothwhitewhitepartialwhiteonependantbrownnumerousmeadows
3ediblebellsmoothwhitebruisesanisefreeclosebroadgray...smoothwhitewhitepartialwhiteonependantblackscatteredmeadows
4edibleconvexscalyyellowbruisesanisefreeclosebroadbrown...smoothwhitewhitepartialwhiteonependantbrownnumerousmeadows
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5 rows × 23 columns

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" ], "text/plain": [ " edible cap-shape cap-surface cap-color bruises odor \\\n", "0 edible bell smooth white bruises anise \n", "1 poisonous convex scaly brown bruises pungent \n", "2 edible bell scaly white bruises almond \n", "3 edible bell smooth white bruises anise \n", "4 edible convex scaly yellow bruises anise \n", "\n", " gill-attachment gill-spacing gill-size gill-color ... \\\n", "0 free close broad brown ... \n", "1 free close narrow brown ... \n", "2 free close broad white ... \n", "3 free close broad gray ... \n", "4 free close broad brown ... \n", "\n", " stalk-surface-below-ring stalk-color-above-ring stalk-color-below-ring \\\n", "0 smooth white white \n", "1 smooth white white \n", "2 smooth white white \n", "3 smooth white white \n", "4 smooth white white \n", "\n", " veil-type veil-color ring-number ring-type spore-print-color population \\\n", "0 partial white one pendant brown numerous \n", "1 partial white one pendant brown several \n", "2 partial white one pendant brown numerous \n", "3 partial white one pendant black scattered \n", "4 partial white one pendant brown numerous \n", "\n", " habitat \n", "0 meadows \n", "1 grasses \n", "2 meadows \n", "3 meadows \n", "4 meadows \n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "NAMES_DICT = {\n", " 'edible': {\n", " 'p': 'poisonous',\n", " 'e': 'edible'\n", " },\n", " \"cap-shape\": {\n", " 'b': 'bell',\n", " 'c': 'conical',\n", " 'x': 'convex',\n", " 'f': 'flat',\n", " 'k': 'knobbed',\n", " 's': 'sunken'\n", " },\n", " \"cap-surface\": {\n", " 'f': 'fibrous',\n", " 'g': 'grooves',\n", " 'y': 'scaly',\n", " 's': 'smooth'\n", " },\n", " \"cap-color\": {\n", " 'n': 'brown',\n", " 'b': 'buff',\n", " 'c': 'cinnamon',\n", " 'g': 'gray',\n", " 'r': 'green',\n", " 'p': 'pink',\n", " 'u': 'purple',\n", " 'e': 'red',\n", " 'w': 'white',\n", " 'y': 'yellow'\n", " },\n", " \"bruises\": {\n", " 't': 'bruises',\n", " 'f': 'none'\n", " },\n", " \"odor\": {\n", " 'a': 'almond',\n", " 'l': 'anise',\n", " 'c': 'creosote',\n", " 'y': 'fishy',\n", " 'f': 'foul',\n", " 'm': 'musty',\n", " 'n': 'none',\n", " 'p': 'pungent',\n", " 's': 'spicy'\n", " },\n", " \"gill-attachment\": {\n", " 'a': 'attached',\n", " 'd': 'descending',\n", " 'f': 'free',\n", " 'n': 'notched'\n", " },\n", " \"gill-spacing\": {\n", " 'c': 'close',\n", " 'w': 'crowded',\n", " 'd': 'distant'\n", " },\n", " \"gill-size\": {\n", " 'b': 'broad',\n", " 'n': 'narrow'\n", " },\n", " \"gill-color\": {\n", " 'k': 'black',\n", " 'n': 'brown',\n", " 'b': 'buff',\n", " 'h': 'chocolate',\n", " 'g': 'gray',\n", " 'r': 'green',\n", " 'o': 'orange',\n", " 'p': 'pink',\n", " 'u': 'purple',\n", " 'e': 'red',\n", " 'w': 'white',\n", " 'y': 'yellow'\n", " },\n", " \"stalk-shape\": {\n", " 'e': 'enlarging',\n", " 't': 'tapering'\n", " },\n", " \"stalk-root\": {\n", " 'b': 'bulbous',\n", " 'c': 'club',\n", " 'u': 'cup',\n", " 'e': 'equal',\n", " 'z': 'rhizomorphs',\n", " 'r': 'rooted',\n", " '?': 'missing'\n", " },\n", " \"stalk-surface-above-ring\": {\n", " 'f': 'fibrous',\n", " 'y': 'scaly',\n", " 'k': 'silky',\n", " 's': 'smooth'\n", " },\n", " \"stalk-surface-below-ring\": {\n", " 'f': 'fibrous',\n", " 'y': 'scaly',\n", " 'k': 'silky',\n", " 's': 'smooth'\n", " },\n", " \"stalk-color-above-ring\": {\n", " 'n': 'brown',\n", " 'b': 'buff',\n", " 'c': 'cinnamon',\n", " 'g': 'gray',\n", " 'o': 'orange',\n", " 'p': 'pink',\n", " 'e': 'red',\n", " 'w': 'white',\n", " 'y': 'yellow'\n", " },\n", " \"stalk-color-below-ring\": {\n", " 'n': 'brown',\n", " 'b': 'buff',\n", " 'c': 'cinnamon',\n", " 'g': 'gray',\n", " 'o': 'orange',\n", " 'p': 'pink',\n", " 'e': 'red',\n", " 'w': 'white',\n", " 'y': 'yellow'\n", " },\n", " \"veil-type\": {\n", " 'p': 'partial',\n", " 'u': 'universal'\n", " },\n", " \"veil-color\": {\n", " 'n': 'brown',\n", " 'o': 'orange',\n", " 'w': 'white',\n", " 'y': 'yellow'\n", " },\n", " \"ring-number\": {\n", " 'n': 'none',\n", " 'o': 'one',\n", " 't': 'two'\n", " },\n", " \"ring-type\": {\n", " 'c': 'cobwebby',\n", " 'e': 'evanescent',\n", " 'f': 'flaring',\n", " 'l': 'large',\n", " 'n': 'none',\n", " 'p': 'pendant',\n", " 's': 'sheathing',\n", " 'z': 'zone'\n", " },\n", " \"spore-print-color\": {\n", " 'k': 'black',\n", " 'n': 'brown',\n", " 'b': 'buff',\n", " 'h': 'chocolate',\n", " 'r': 'green',\n", " 'o': 'orange',\n", " 'u': 'purple',\n", " 'w': 'white',\n", " 'y': 'yellow'\n", " },\n", " \"population\": {\n", " 'a': 'abundant',\n", " 'c': 'clustered',\n", " 'n': 'numerous',\n", " 's': 'scattered',\n", " 'v': 'several',\n", " 'y': 'solitary'\n", " },\n", " \"habitat\": {\n", " 'g': 'grasses',\n", " 'l': 'leaves',\n", " 'm': 'meadows',\n", " 'p': 'paths',\n", " 'u': 'urban',\n", " 'w': 'waste',\n", " 'd': 'woods'\n", " },\n", "}\n", "\n", "for key in NAMES_DICT.keys():\n", " mushrooms[key] = mushrooms[key].apply(lambda x: NAMES_DICT[key][x])\n", "mushrooms.head()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } }, "source": [ "##### Features' distribution \n", "\n", "Wśród cech zawartych w danych możemy zauważyć, że rodzaj woalki/pierścienia (veil-type) jest tylko jeden, zatem ta cecha nie powinna mieć wpływu na skuteczność algorytmu, jako że wszystkie obserwacje dzielą tę samą wartość (partial)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "domain": { "x": [ 0, 0.16799999999999998 ], "y": [ 0.848, 1 ] }, "hole": 0.4, "hoverinfo": "label+percent+name", "labels": [ "edible", "poisonous" ], "name": "edible", "textinfo": "none", "type": "pie", "values": [ 51.89472233705388, 48.10527766294612 ] }, { "domain": { "x": [ 0, 0.16799999999999998 ], "y": [ 0.6359999999999999, 0.7879999999999999 ] }, "hole": 0.4, "hoverinfo": 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"mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "font": { "family": "Arial", "size": 25 }, "text": "Features" } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def domain_plots(data):\n", " specs = [[{'type': 'domain'} for _ in range(5)] for _ in range(5)]\n", " fig = make_subplots(rows=5, cols=5, specs=specs)\n", " a, b, xx, yy, l = 1, 1, -0.172, 1.267, []\n", " for col in data.columns:\n", " fig.add_trace(\n", " plotly.graph_objects.Pie(\n", " labels=[count for count in data[col].value_counts().index],\n", " values=[\n", " val for val in data[col].value_counts() * 100 /\n", " sum(data[col].value_counts())\n", " ],\n", " name=col), a, b)\n", " l.append(\n", " dict(text=col,\n", " x=xx + (0.225 * b),\n", " y=yy - (0.222 * a),\n", " font_size=10,\n", " showarrow=False))\n", " a += 1\n", " if a > 5:\n", " a = 1\n", " b += 1\n", " fig.update(layout_title_text='Features', layout_showlegend=False)\n", "\n", " fig.update_layout(title_font_family='Arial',\n", " title_font_size=25,\n", " annotations=l)\n", " fig.update_traces(hole=.4, hoverinfo=\"label+percent+name\", textinfo='none')\n", " fig.show()\n", "\n", "\n", "domain_plots(mushrooms)\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } }, "source": [ "##### Korelacja zmiennych\n", "\n", "Na poniższym wykresie możemy zauważyć silny związek gill-color z gill-attachment, lecz, to co nas bardziej interesuje, korelacja jadalności grzyba z zapachem, rozmiarem jego blaszek oraz kolorem zarodników. Możemy wyszukać cechy o najwyższym współczynniku korelacji z jadalnością grzyba i z dużym prawdopodobieństwiem uzyskać wyniki klasyfikacji " ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def cramers_v(x, y):\n", " confusion_matrix = pd.crosstab(x, y)\n", " chi2 = stats.chi2_contingency(confusion_matrix)[0]\n", " n = confusion_matrix.sum().sum()\n", " phi2 = chi2 / n\n", " r, k = confusion_matrix.shape\n", " phi2corr = max(0, phi2 - ((k - 1) * (r - 1)) / (n - 1))\n", " rcorr = r - ((r - 1) ** 2) / (n - 1)\n", " kcorr = k - ((k - 1) ** 2) / (n - 1)\n", " return np.sqrt(phi2corr / min((kcorr - 1), (rcorr - 1)))\n", "\n", "\n", "reduced_data = mushrooms.drop(['veil-type'], axis=1)\n", "corr_list = [[\n", " round(cramers_v(reduced_data[col], reduced_data[corr_col]) * 100) / 100\n", " for corr_col in reduced_data.columns\n", "] for col in reduced_data.columns]\n", "corr_df = pd.DataFrame(np.array(corr_list), columns=[reduced_data.columns])\n", "corr_df.index = corr_df.columns\n", "\n", "plt.figure(figsize=(14, 12))\n", "corr_mask = np.triu(np.ones_like(corr_df, dtype=bool))\n", "sns.heatmap(corr_df, mask=corr_mask, cmap='viridis', annot=True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_chosen_features(data,\n", " col,\n", " hue=None,\n", " color=['orange', 'lightgreen'],\n", " labels=None):\n", " fig, ax = plt.subplots(figsize=(15, 7))\n", " sns.countplot(x=col,\n", " hue=hue,\n", " palette=color,\n", " saturation=0.6,\n", " data=data,\n", " dodge=True,\n", " ax=ax)\n", " ax.set(title=f\"Mushroom {col.title()} Quantity\",\n", " xlabel=f\"{col.title()}\",\n", " ylabel=\"Quantity\")\n", " if labels != None:\n", " ax.set_xticklabels(labels)\n", " if hue != None:\n", " ax.legend(('Poisonous', 'Edible'), loc=0)\n", " \n", " plt.show()\n", "\n", "\n", "training_cols = [\n", " 'odor',\n", " 'spore-print-color',\n", " 'gill-color',\n", " 'ring-type',\n", " 'stalk-surface-above-ring',\n", " 'gill-size',\n", "]\n", "\n", "plot_chosen_features(mushrooms,\n", " col='odor',\n", " labels=NAMES_DICT['odor'].values(),\n", " hue='edible')\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_chosen_features(mushrooms,\n", " col='spore-print-color',\n", " labels=NAMES_DICT['spore-print-color'].values(),\n", " hue='edible')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_chosen_features(mushrooms,\n", " col='gill-color',\n", " labels=NAMES_DICT['gill-color'].values(),\n", " hue='edible')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_chosen_features(mushrooms,\n", " col='ring-type',\n", " labels=list(NAMES_DICT['ring-type'].values()).append('cobwebby'),\n", " hue='edible')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_chosen_features(mushrooms,\n", " col='stalk-surface-above-ring',\n", " labels=NAMES_DICT['stalk-surface-above-ring'].values(),\n", " hue='edible')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_chosen_features(mushrooms,\n", " col='gill-size',\n", " labels=NAMES_DICT['gill-size'].values(),\n", " hue='edible')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "X_train, X_test = train_test_split(mushrooms,\n", " test_size=0.2,\n", " random_state=0,\n", " stratify=mushrooms['edible'])\n", "columns = [\n", " 'odor',\n", " 'spore-print-color',\n", " 'gill-color',\n", " 'ring-type',\n", " 'stalk-surface-above-ring',\n", " 'gill-size',\n", "]\n", "className = 'edible'\n", "classValue = list(set(X_train['edible']))" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "X_test_data = X_test[columns]\n", "X_test_results = X_test[className]\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "bayModel = NaiveBayes(classValue, className, columns, X_train)\n", "model = bayModel.fitModel()\n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "accuracy score naiwnego klasyfikatora\n", "acuracy score = 0.9944903581267218\n", "\n", "accuracy score losowych predykcji\n", "acuracy score = 0.48829201101928377\n" ] } ], "source": [ "pred = bayModel.predict(X_test[columns], model)\n", "print('accuracy score naiwnego klasyfikatora')\n", "print(\"acuracy score = \", accuracy_score(list(X_test_results), pred))\n", "\n", "print('\\naccuracy score losowych predykcji')\n", "randomPred = ['poisonous' if random.randint(0, 1) == 1 else 'edible' for _ in range(len(list(X_test_results)))]\n", "print(\"acuracy score = \", accuracy_score(list(X_test_results), randomPred))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "accuracy score naiwnego klasyfikatora\n", "acuracy score = 0.8980716253443526\n" ] } ], "source": [ "columns_wihtout_odor = [\n", " 'spore-print-color',\n", " 'gill-color',\n", " 'ring-type',\n", " 'stalk-surface-above-ring',\n", " 'gill-size',\n", "]\n", "\n", "X_test_data = X_test[columns_wihtout_odor]\n", "X_test_results = X_test[className]\n", "\n", "bayModel = NaiveBayes(classValue, className, columns_wihtout_odor, X_train)\n", "model = bayModel.fitModel()\n", "pred = bayModel.predict(X_test[columns_wihtout_odor], model)\n", "print('accuracy score naiwnego klasyfikatora')\n", "print(\"acuracy score = \", accuracy_score(list(X_test_results), pred))\n" ] } ], "metadata": { "interpreter": { "hash": "393784674bcf6e74f2fc9b1b5fb3713f9bd5fc6f8172c594e5cfa8e8c12849bc" }, "kernelspec": { "display_name": "Python 3.9.2 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": "3.9.2" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }