{ "cells": [ { "cell_type": "markdown", "id": "b4454654", "metadata": {}, "source": [ "### Machine Learning: naive Bayes classifier for continuous data from scratch in Python\n", "$P(C_k|\\textbf{x})\\propto P(C_k) P(\\textbf{x}|C_k)$\n", "###### by Hamed Shah-Hosseini\n", "Explanation at: https://www.pinterest.com/HamedShahHosseini/\n", "
https://github.com/ostad-ai/Machine-Learning" ] }, { "cell_type": "code", "execution_count": 18, "id": "50b8a8b6", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from math import pi,sqrt,exp" ] }, { "cell_type": "code", "execution_count": 19, "id": "31065e4c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of samples in dataset:150\n", "Number of classes: 3\n", "Features:['Sepal-length', ' Sepal-width', 'Petal-length', 'Petal-width']\n" ] } ], "source": [ "# Reading dataset\n", "iris=pd.read_csv('./datasets/iris-uci.csv')\n", "print(f'Number of samples in dataset:{len(iris)}')\n", "print(f'Number of classes: {iris[\"Class\"].unique().shape[0]}')\n", "print(f'Features:{iris.columns[:-1].to_list()}')" ] }, { "cell_type": "code", "execution_count": 20, "id": "d8583f14", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Sepal-lengthSepal-widthPetal-lengthPetal-widthClass
05.13.51.40.2Iris-setosa
14.93.01.40.2Iris-setosa
24.73.21.30.2Iris-setosa
34.63.11.50.2Iris-setosa
45.03.61.40.2Iris-setosa
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" ], "text/plain": [ " Sepal-length Sepal-width Petal-length Petal-width Class\n", "0 5.1 3.5 1.4 0.2 Iris-setosa\n", "1 4.9 3.0 1.4 0.2 Iris-setosa\n", "2 4.7 3.2 1.3 0.2 Iris-setosa\n", "3 4.6 3.1 1.5 0.2 Iris-setosa\n", "4 5.0 3.6 1.4 0.2 Iris-setosa" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The first five rows of dataset\n", "iris.head()" ] }, { "cell_type": "code", "execution_count": 27, "id": "37b96427", "metadata": {}, "outputs": [], "source": [ "# Naive Bayes for continuous data\n", "class NBContinuous:\n", " def __init__(self):\n", " pass\n", " def fit(self,data_frame,class_column):\n", " df=data_frame\n", " self.groups_m_v=df.groupby(class_column).agg(['mean','var'])\n", " self.features=df.columns.to_list()\n", " self.features.remove(class_column)\n", " self.priors=(pd.value_counts(df[class_column])/len(df)).to_dict() #prior \n", " self.cNames=df[class_column].unique() #class categories\n", " def gaussian(self,x,mean,var):\n", " return (1./sqrt(2*pi*var))*exp(-(x-mean)**2/(2*var))\n", " def predict(self,query,with_priors=True):\n", " probs_classes=dict() \n", " for cName in self.cNames:\n", " if with_priors:\n", " prob=self.priors[cName]\n", " else:\n", " prob=1.\n", " for q,feature in zip(query,self.features):\n", " mean=self.groups_m_v.loc[cName][feature]['mean']\n", " var=self.groups_m_v.loc[cName][feature]['var']\n", " prob*=self.gaussian(q,mean,var) \n", " probs_classes[cName]=prob\n", " predict=max(probs_classes,key=probs_classes.get)\n", " return predict,probs_classes" ] }, { "cell_type": "code", "execution_count": 28, "id": "15ae913b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy:% 96.67\n" ] }, { "data": { "text/html": [ "
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Iris-setosaIris-versicolorIris-virginica
Iris-setosa1300
Iris-versicolor081
Iris-virginica008
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" ], "text/plain": [ " Iris-setosa Iris-versicolor Iris-virginica\n", "Iris-setosa 13 0 0\n", "Iris-versicolor 0 8 1\n", "Iris-virginica 0 0 8" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Experiment with the Naive Baye's Classifier\n", "classCol='Class'\n", "class_names=list(iris[classCol].unique())\n", "# Splitting dataset into training and test sets\n", "iris_train=iris.sample(frac=.8)\n", "iris_test=iris.drop(iris_train.index)\n", "Nts=iris_test.shape[0] #number of test samples\n", "columns=iris.columns.to_list()\n", "classCol_inx=columns.index(classCol)\n", "nbayes=NBContinuous()\n", "# Training with training set\n", "nbayes.fit(iris_train,classCol)\n", "# Testing with test set\n", "accuracy=0.; actuals=[]; predicteds=[]\n", "for n in range(Nts):\n", " query=iris_test.iloc[n].to_list()\n", " #remove the value of class column\n", " class_q=query.pop(classCol_inx)\n", " actuals.append(class_q)\n", " predicted=nbayes.predict(query)[0]\n", " predicteds.append(predicted)\n", " accuracy+=predicted==class_q\n", "accuracy/=Nts \n", "print('Accuracy:%',round(100*accuracy,2))\n", "#-Computing confusion matrix\n", "Nclasses=len(class_names)\n", "confusion_mat=pd.DataFrame(0,index=class_names,\n", " columns=class_names)\n", "for n in range(Nts):\n", " confusion_mat.loc[actuals[n],predicteds[n]]+=1\n", "confusion_mat\n", "#--Could use pd.crosstab for confusion matrix" ] }, { "cell_type": "code", "execution_count": null, "id": "da82c766", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }