Delete 'TestBayes.ipynb'
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TestBayes.ipynb
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TestBayes.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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np \n",
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"import pandas as pd \n",
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"import matplotlib.pyplot as plt \n",
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"import math\n",
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"\n",
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"\n",
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"def accuracy_score(y_true, y_pred):\n",
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"\n",
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" \"\"\"\tscore = (y_true - y_pred) / len(y_true) \"\"\"\n",
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"\n",
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" return round(float(sum(y_pred == y_true))/float(len(y_true)) * 100 ,2)\n",
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"\n",
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"def pre_processing(df):\n",
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"\n",
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" \"\"\" partioning data into features and target \"\"\"\n",
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"\n",
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" X = df.drop([df.columns[-1]], axis = 1)\n",
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" y = df[df.columns[-1]]\n",
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"\n",
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" return X, y\n",
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"\n",
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"\n",
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"\n",
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"class NaiveBayes:\n",
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"\n",
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"def __init__(self):\n",
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"\n",
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" self.features = list\n",
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" self.likelihoods = {}\n",
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" self.class_priors = {}\n",
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" self.pred_priors = {}\n",
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"\n",
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" self.X_train = np.array\n",
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" self.y_train = np.array\n",
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" self.train_size = int\n",
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" self.num_feats = int\n",
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"\n",
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"\tdef fit(self, X, y):\n",
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"\n",
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" self.features = list(X.columns)\n",
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" self.X_train = X\n",
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" self.y_train = y\n",
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" self.train_size = X.shape[0]\n",
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" self.num_feats = X.shape[1]\n",
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"\n",
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" for feature in self.features:\n",
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" self.likelihoods[feature] = {}\n",
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" self.pred_priors[feature] = {}\n",
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"\n",
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" for feat_val in np.unique(self.X_train[feature]):\n",
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" self.pred_priors[feature].update({feat_val: 0})\n",
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"\n",
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" for outcome in np.unique(self.y_train):\n",
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" self.likelihoods[feature].update({feat_val+'_'+outcome:0})\n",
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" self.class_priors.update({outcome: 0})\n",
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"\n",
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" self._calc_class_prior()\n",
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" self._calc_likelihoods()\n",
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" self._calc_predictor_prior()\n",
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"\n",
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" def _calc_class_prior(self):\n",
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"\n",
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" \"\"\" P(c) - Prior Class Probability \"\"\"\n",
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"\n",
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" for outcome in np.unique(self.y_train):\n",
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" outcome_count = sum(self.y_train == outcome)\n",
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" self.class_priors[outcome] = outcome_count / self.train_size\n",
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"\n",
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" def _calc_likelihoods(self):\n",
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"\n",
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" \"\"\" P(x|c) - Likelihood \"\"\"\n",
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"\n",
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" for feature in self.features:\n",
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"\n",
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" for outcome in np.unique(self.y_train):\n",
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" outcome_count = sum(self.y_train == outcome)\n",
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" feat_likelihood = self.X_train[feature][self.y_train[self.y_train == outcome].index.values.tolist()].value_counts().to_dict()\n",
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"\n",
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" for feat_val, count in feat_likelihood.items():\n",
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" self.likelihoods[feature][feat_val + '_' + outcome] = count/outcome_count\n",
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"\n",
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"\n",
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" def _calc_predictor_prior(self):\n",
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"\n",
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" \"\"\" P(x) - Evidence \"\"\"\n",
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"\n",
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" for feature in self.features:\n",
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" feat_vals = self.X_train[feature].value_counts().to_dict()\n",
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"\n",
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" for feat_val, count in feat_vals.items():\n",
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" self.pred_priors[feature][feat_val] = count/self.train_size\n",
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"\n",
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"\n",
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" def predict(self, X):\n",
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"\n",
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" \"\"\" Calculates Posterior probability P(c|x) \"\"\"\n",
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"\n",
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" results = []\n",
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" X = np.array(X)\n",
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"\n",
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" for query in X:\n",
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" probs_outcome = {}\n",
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" for outcome in np.unique(self.y_train):\n",
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" prior = self.class_priors[outcome]\n",
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" likelihood = 1\n",
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" evidence = 1\n",
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"\n",
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" for feat, feat_val in zip(self.features, query):\n",
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" likelihood *= self.likelihoods[feat][feat_val + '_' + outcome]\n",
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" evidence *= self.pred_priors[feat][feat_val]\n",
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"\n",
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" posterior = (likelihood * prior) / (evidence)\n",
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"\n",
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" probs_outcome[outcome] = posterior\n",
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"\n",
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" result = max(probs_outcome, key = lambda x: probs_outcome[x])\n",
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" results.append(result)\n",
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"\n",
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" return np.array(results)\n",
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"\n",
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"\n",
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"\n",
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"if __name__ == \"__main__\":\n",
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"\n",
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" #Weather Dataset\n",
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" print(\"\\nWeather Dataset:\")\n",
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"\n",
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" df = pd.read_table(\"../Data/weather.txt\")\n",
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" #print(df)\n",
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"\n",
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" #Split fearures and target\n",
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" X,y = pre_processing(df)\n",
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"\n",
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" nb_clf = NaiveBayes()\n",
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" nb_clf.fit(X, y)\n",
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"\n",
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" print(\"Train Accuracy: {}\".format(accuracy_score(y, nb_clf.predict(X))))\n",
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"\n",
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" #Query 1:\n",
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" query = np.array([['Rainy','Mild', 'Normal', 't']])\n",
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" print(\"Query 1:- {} ---> {}\".format(query, nb_clf.predict(query)))\n",
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"\n",
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" #Query 2:\n",
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" query = np.array([['Overcast','Cool', 'Normal', 't']])\n",
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" print(\"Query 2:- {} ---> {}\".format(query, nb_clf.predict(query)))\n",
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"\n",
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" #Query 3:\n",
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" query = np.array([['Sunny','Hot', 'High', 't']])\n",
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" print(\"Query 3:- {} ---> {}\".format(query, nb_clf.predict(query)))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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