Add fixed bayes classification

.gitignore

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+env

README

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+Instalacja:
+
+python -m venv env
+source ./env/bin/activate
+
+pip install -r requirements.txt

bayes.py

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+import pandas as pd 
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+#Wczytanie i normalizacja danych
+def NormalizeData(data):
+    for col in data.columns:
+        if data[col].dtype == object: 
+            data[col] = data[col].str.lower()
+        if col == 'smoking_status':
+            data[col] = data[col].str.replace(" ", "_")
+        if col == 'work_type':
+            data[col] = data[col].str.replace("-", "_")
+        if col == 'bmi':
+            bins = [0, 21, 28, 40]
+            labels=['low','mid','high']
+            data[col] = pd.cut(data[col], bins=bins, labels=labels)
+        if col == 'age':
+            bins = [18, 30, 40, 50, 60, 70, 120]
+            labels = ['18-29', '30-39', '40-49', '50-59', '60-69', '70+']
+            data[col] = pd.cut(data[col], bins, labels = labels,include_lowest = True)
+        if col == 'stroke':
+            data[col] = data[col].replace({1: 'yes'})
+            data[col] = data[col].replace({0: 'no'})
+        if col == 'hypertension':
+            data[col] = data[col].replace({1: 'yes'})
+            data[col] = data[col].replace({0: 'no'})
+        if col == 'heart_disease':
+            data[col] = data[col].replace({1: 'yes'})
+            data[col] = data[col].replace({0: 'no'})
+    data = data.dropna()
+    return data
+
+def count_a_priori_prob(dataset):
+    is_stroke_amount = len(dataset[dataset.stroke == 'yes'])
+    no_stroke_amount = len(dataset[dataset.stroke == 'no'])
+    data_length = len(dataset.stroke)
+    return {'yes': float(is_stroke_amount)/float(data_length), 'no': float(no_stroke_amount)/float(data_length)}
+
+def separate_labels_from_properties(X_train):
+
+    labels = X_train.columns
+    labels_values = {}
+    for label in labels:
+        labels_values[label] = set(X_train[label])
+    
+    to_return = []
+    for x in labels:
+        to_return.append({x: labels_values[x]})
+
+    return to_return
+
+data = pd.read_csv("healthcare-dataset-stroke-data.csv")
+data = NormalizeData(data)
+
+#podział danych na treningowy i testowy 
+data_train, data_test = train_test_split(data, random_state = 42)
+
+#rozdzielenie etykiet i cech
+X_train =data_train[['gender', 'age', 'ever_married', 'Residence_type', 'bmi','smoking_status', 'work_type','hypertension','heart_disease']]
+Y_train = data_train['stroke']
+
+#rozdzielenie etykiet i cech
+# Dane wejściowe - zbiór danych, wektor etykiet,  wektor prawdopodobieństw a priori dla klas.
+
+# Wygenerowanie wektora prawdopodobieństw a priori dla klas.
+a_priori_prob = count_a_priori_prob(data_train)
+labels = separate_labels_from_properties(X_train)
+
+class NaiveBayes():
+    def __init__(self, dataset, labels, a_priori_prob):
+        self.dataset = dataset
+        self.labels = labels
+        self.a_priori_prob = a_priori_prob
+    
+    def count_bayes(self):
+
+        final_probs = {'top_yes': 0.0, 'top_no': 0.0, 'total': 0.0}
+        
+        # self.labels - Wartości etykiet które nas interesują, opcjonalnie podane sa wszystkie.
+        # [{'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'}}]
+        # Dla kazdej z klas - 'yes', 'no'
+        for idx, cls in enumerate(list(set(self.dataset['stroke']))):
+            label_probs = []
+            for label in self.labels:
+                label_name = list(label.keys())[0]
+                for label_value in label[label_name]:
+                    # Oblicz ilość występowania danej cechy w zbiorze danych np. heart_disease.yes
+
+                    amount_label_value_yes_class = len(self.dataset.loc[(self.dataset['stroke'] == 'yes') & (self.dataset[label_name] == label_value)])
+                    amount_label_value_no_class = len(self.dataset.loc[(self.dataset['stroke'] == 'no') & (self.dataset[label_name] == label_value)])
+
+                    amount_yes_class = len(self.dataset.loc[(self.dataset['stroke'] == 'yes')])
+                    amount_no_class = len(self.dataset.loc[(self.dataset['stroke'] == 'no')])  
+                    # Obliczenie P(heart_disease.yes|'stroke'|), P(heart_disease.yes|'no stroke') itd. dla kazdej cechy.
+                    # Zapisujemy do listy w formacie (cecha.wartość: prob stroke, cecha.wartość: prob no stroke)
+                    label_probs.append({str(label_name + "." + label_value):(amount_label_value_yes_class/amount_yes_class, amount_label_value_no_class/amount_no_class)})
+
+            # Suma prawdopodobienstw mozliwych wartosci danej cechy dla danej klasy, powinna sumować się do 1.
+            print(label_probs)
+
+            # Obliczanie licznika wzoru Bayesa (mnozymy wartosci prob cech z prawdop apriori danej klasy):
+            top = 1
+            for label_prob in label_probs:
+                top *= list(label_prob.values())[0][idx]
+            top *= self.a_priori_prob[cls]
+
+            final_probs[cls] = top
+            final_probs['total'] += top
+
+        print("Prawdopodobieństwo a posteriori dla klasy yes-stroke", final_probs['yes']/final_probs['total'])
+        print("Prawdopodobieństwo a posteriori dla klasy no-stroke", final_probs['no']/final_probs['total'])     
+
+labels = [{'Residence_type': {'urban'}}]
+naive_bayes = NaiveBayes(data_train, labels, a_priori_prob)
+naive_bayes.count_bayes()

requirements.txt

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+joblib==1.0.1
+numpy==1.20.3
+pandas==1.2.4
+python-dateutil==2.8.1
+pytz==2021.1
+scikit-learn==0.24.2
+scipy==1.6.3
+six==1.16.0
+sklearn==0.0
+threadpoolctl==2.1.0