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{
"cells": [
{
"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
"outputs": [],
"source": [
"import pandas as pd \n",
"import numpy as np\n",
"from sklearn.model_selection import train_test_split\n",
"import matplotlib.pyplot as plt\n",
"\n",
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"# Wizualizacja\n",
"def autolabel(rects, values ,ax):\n",
" # Attach some text labels.\n",
" for (rect, value) in zip(rects, values):\n",
" ax.text(rect.get_x() + rect.get_width() / 2.,\n",
" rect.get_y() + rect.get_height() / 2.,\n",
" '%.3f'%value,\n",
" ha = 'center',\n",
" va = 'center',\n",
" fontsize= 15,\n",
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" color ='black') \n",
"def plot_priori(labels, posteriori, name): \n",
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" keys =[ r\"$\\bf{\" + (x.split('.',1)[0]).replace('_', ' ')+ \"}$\" + '\\n' + x.split('.',1)[1] for i in range(1) for j in range(len(labels[i])) for x in labels[i][j].keys()]\n",
" aprori = [list(x) for i in range(1) for j in range(len(labels[i])) for x in labels[i][j].values()]\n",
" yes_aprori = np.array(aprori)[:,0]\n",
" no_aprori = np.array(aprori)[:,1]\n",
" \n",
" width = 0.55\n",
"\n",
" fig = plt.figure(figsize=(25,10))\n",
" \n",
" ax1 = fig.add_subplot(121)\n",
" rec1 = ax1.bar(keys,yes_aprori,width, color ='lime', label= 'Positive stroke')\n",
" rec2 = ax1.bar(keys,no_aprori,width, color ='crimson', bottom = yes_aprori, label= 'Negative stroke')\n",
" ax1.set_yticks(np.arange(0, 1.1,0.1))\n",
" ax1.set_ylabel('Probability',fontsize=18)\n",
" ax1.set_xlabel('\\nFeatures',fontsize=18)\n",
" ax1.tick_params(axis='x', which='major', labelsize=12)\n",
" autolabel(rec1,yes_aprori, ax1)\n",
" autolabel(rec2,no_aprori, ax1)\n",
" ax1.legend(fontsize=15)\n",
" \n",
" ax2 = fig.add_subplot(122)\n",
" rec3 = ax2.bar(0, posteriori[1],capsize=1 ,color=['crimson'], label='Negative stroke')\n",
" rec4 = ax2.bar(1, posteriori[0], color=['lime'],label='Positive stroke')\n",
" ax2.set_ylabel('Probability',fontsize=18)\n",
" ax2.set_xlabel('\\nClasses',fontsize=18)\n",
" ax2.set_xticks([0,1])\n",
" ax2.set_yticks(np.arange(0, 1.1,0.1))\n",
" ax2.tick_params(axis='x', which='major', labelsize=15)\n",
" autolabel(rec3,[posteriori[1]], ax2)\n",
" autolabel(rec4,[posteriori[0]], ax2)\n",
" ax2.legend(fontsize=15)\n",
" \n",
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"# plt.show()\n",
" plt.savefig(name + \".png\", dpi=100)\n",
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"\n",
"\n",
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"#Wczytanie i normalizacja danych\n",
"def NormalizeData(data):\n",
" for col in data.columns:\n",
" if data[col].dtype == object: \n",
" data[col] = data[col].str.lower()\n",
" if col == 'smoking_status':\n",
" data[col] = data[col].str.replace(\" \", \"_\")\n",
" if col == 'stroke':\n",
" data[col] = data[col].replace({1: 'yes'})\n",
" data[col] = data[col].replace({0: 'no'})\n",
" if col == 'hypertension':\n",
" data[col] = data[col].replace({1: 'yes'})\n",
" data[col] = data[col].replace({0: 'no'})\n",
" if col == 'heart_disease':\n",
" data[col] = data[col].replace({1: 'yes'})\n",
" data[col] = data[col].replace({0: 'no'})\n",
" if col == 'bmi':\n",
" bins = [19,25,30,35,40,90]\n",
" labels=['correct','overweight','obesity_1','obesity_2','extreme']\n",
" data[col] = pd.cut(data[col], bins, labels = labels,include_lowest = True)\n",
" if col == 'age':\n",
" bins = [0, 30, 40, 50, 60, 70, 80, 90]\n",
" labels = ['0-29', '30-39', '40-49', '50-59', '60-69', '70-79', '80-89',]\n",
" data[col] = pd.cut(data[col], bins, labels = labels,include_lowest = True)\n",
" if col == 'avg_glucose_level':\n",
" bins = [50,70,90,110,130,150,170,190,210,230,250,270]\n",
" labels = ['50-70', '70-90', '90-110','110-130','130-150','150-170','170-190','190-210', '210-230','230-250','250-270']\n",
" data[col] = pd.cut(data[col], bins, labels = labels,include_lowest = True)\n",
" data = data.dropna()\n",
" return data\n",
"\n",
"def count_a_priori_prob(dataset):\n",
" is_stroke_amount = len(dataset[dataset.stroke == 'yes'])\n",
" no_stroke_amount = len(dataset[dataset.stroke == 'no'])\n",
" data_length = len(dataset.stroke)\n",
" return {'yes': float(is_stroke_amount)/float(data_length), 'no': float(no_stroke_amount)/float(data_length)}\n",
"\n",
"def separate_labels_from_properties(X_train):\n",
"\n",
" labels = X_train.columns\n",
" labels_values = {}\n",
" for label in labels:\n",
" labels_values[label] = set(X_train[label])\n",
" \n",
" to_return = []\n",
" for x in labels:\n",
" to_return.append({x: labels_values[x]})\n",
"\n",
" return to_return\n",
"\n",
"data = pd.read_csv(\"healthcare-dataset-stroke-data.csv\")\n",
"data = NormalizeData(data)\n",
"\n",
"#podział danych na treningowy i testowy \n",
"data_train, data_test = train_test_split(data, random_state = 42)\n",
"\n",
"#rozdzielenie etykiet i cech\n",
"X_train =data_train[['gender', 'age', 'bmi','smoking_status', 'work_type','hypertension','heart_disease']]\n",
"Y_train = data_train['stroke']\n",
"\n",
"#rozdzielenie etykiet i cech\n",
"# Dane wejściowe - zbiór danych, wektor etykiet, wektor prawdopodobieństw a priori dla klas.\n",
"\n",
"# Wygenerowanie wektora prawdopodobieństw a priori dla klas.\n",
"a_priori_prob = count_a_priori_prob(data_train)\n",
"labels = separate_labels_from_properties(X_train)\n",
"\n",
"class NaiveBayes():\n",
" def __init__(self, dataset, labels, a_priori_prob):\n",
" self.dataset = dataset\n",
" self.labels = labels\n",
" self.a_priori_prob = a_priori_prob\n",
" \n",
" def count_bayes(self):\n",
" label_probs_return = []\n",
" posteriori_return = []\n",
" final_probs = {'top_yes': 0.0, 'top_no': 0.0, 'total': 0.0}\n",
" \n",
" # self.labels - Wartości etykiet które nas interesują, opcjonalnie podane sa wszystkie.\n",
" # [{'gender': {'female', 'male', 'other'}}, {'age': {'50-59', '40-49', '60-69', '70+', '18-29', '30-39'}}, {'ever_married': {'no', 'yes'}}, {'Residence_type': {'rural', 'urban'}}, {'bmi': {'high', 'mid', 'low'}}, {'smoking_status': {'unknown', 'smokes', 'never_smoked', 'formerly_smoked'}}, {'work_type': {'self_employed', 'private', 'never_worked', 'govt_job'}}, {'hypertension': {'no', 'yes'}}, {'heart_disease': {'no', 'yes'}}]\n",
" # Dla kazdej z klas - 'yes', 'no'\n",
" for idx, cls in enumerate(list(set(self.dataset['stroke']))):\n",
" label_probs = []\n",
" for label in self.labels:\n",
" label_name = list(label.keys())[0]\n",
" for label_value in label[label_name]:\n",
" # Oblicz ilość występowania danej cechy w zbiorze danych np. heart_disease.yes\n",
"\n",
" amount_label_value_yes_class = len(self.dataset.loc[(self.dataset['stroke'] == 'yes') & (self.dataset[label_name] == label_value)])\n",
" amount_label_value_no_class = len(self.dataset.loc[(self.dataset['stroke'] == 'no') & (self.dataset[label_name] == label_value)])\n",
" amount_yes_class = len(self.dataset.loc[(self.dataset['stroke'] == 'yes')])\n",
" amount_no_class = len(self.dataset.loc[(self.dataset['stroke'] == 'no')]) \n",
" # Obliczenie P(heart_disease.yes|'stroke'|), P(heart_disease.yes|'no stroke') itd. dla kazdej cechy.\n",
" # Zapisujemy do listy w formacie (cecha.wartość: prob stroke, cecha.wartość: prob no stroke)\n",
" label_probs.append({str(label_name + \".\" + label_value):(amount_label_value_yes_class/amount_yes_class, amount_label_value_no_class/amount_no_class)})\n",
"\n",
" # Suma prawdopodobienstw mozliwych wartosci danej cechy dla danej klasy, powinna sumować się do 1.\n",
"# print(label_probs)\n",
" label_probs_return.append(label_probs)\n",
" # Obliczanie licznika wzoru Bayesa (mnozymy wartosci prob cech z prawdop apriori danej klasy):\n",
" top = 1\n",
" for label_prob in label_probs:\n",
" top *= list(label_prob.values())[0][idx]\n",
" top *= self.a_priori_prob[cls]\n",
"\n",
" final_probs[cls] = top\n",
" final_probs['total'] += top\n",
" \n",
"# print(\"Prawdopodobieństwo a posteriori dla klasy yes-stroke\", final_probs['yes']/final_probs['total'])\n",
"# print(\"Prawdopodobieństwo a posteriori dla klasy no-stroke\", final_probs['no']/final_probs['total'])\n",
" posteriori_return.append(final_probs['yes']/final_probs['total'])\n",
" posteriori_return.append(final_probs['no']/final_probs['total'])\n",
" return posteriori_return, label_probs_return\n",
"\n",
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"labels = [{'age': {'70-79'}},{'hypertension': {'yes'}},{'heart_disease': {'yes'}},{'bmi': {'correct'}},{'gender': {'male'}},{'smoking_status': {'smokes'}}]\n",
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"naive_bayes = NaiveBayes(data_train, labels, a_priori_prob)\n",
"posteriori, labels = naive_bayes.count_bayes()"
]
},
{
"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
"outputs": [],
"source": [
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"# plot_priori(labels,posteriori)"
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]
},
{
"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 1800x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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2021-05-31 16:33:55 +02:00
"text/plain": [
"<Figure size 1800x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2021-05-31 18:56:34 +02:00
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2021-05-31 16:33:55 +02:00
"text/plain": [
"<Figure size 1800x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1800x720 with 2 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"labels = [[{'age': {'60-69'}},{'hypertension': {'no'}},{'heart_disease': {'yes'}},{'bmi': {'obesity_1'}},{'gender': {'female'}},{'smoking_status': {'smokes'}}],\n",
" [{'age': {'70-79'}},{'hypertension': {'yes'}},{'heart_disease': {'yes'}},{'bmi': {'correct'}},{'gender': {'male'}},{'smoking_status': {'never_smoked'}}],\n",
" [{'age': {'70-79'}},{'hypertension': {'yes'}},{'heart_disease': {'yes'}},{'bmi': {'correct'}},{'gender': {'female'}},{'smoking_status': {'never_smoked'}}],\n",
" [{'age': {'30-39'}},{'hypertension': {'no'}},{'heart_disease': {'yes'}},{'bmi': {'obesity_2'}},{'avg_glucose_level': {'250-270'}},{'gender': {'female'}},{'smoking_status': {'smokes'}}]\n",
" \n",
" ]\n",
"\n",
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"name = 1\n",
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"for i in labels:\n",
" naive_bayes = NaiveBayes(data_train, i, a_priori_prob)\n",
" posteriori, labels = naive_bayes.count_bayes()\n",
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" plot_priori(labels,posteriori, str(name))\n",
" name = name + 1"
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]
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},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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}
],
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
