Matematyka_Bayes/main.py

import pandas as pd
import numpy
import csv
data = pd.read_csv('heart.csv')


def p_target(target):
    count = 0
    for i in data['target']:
        if i == target:
            count += 1
    return count / len(data)


def p_property(property_name, value):
    count = 0
    if value == 0 or value == 1:
        for i in data[property_name]:
            if i == value:
                count += 1
    else:
        for i in data[property_name]:
            if value[0] <= i and i < value[1]:
                count += 1
    return count / len(data)


def p_property_if_target(property_name, property_value, target):
    property_if_target = 0
    count_target = 0
    for i in range(len(data['target'])):
        if data['target'][i] == target:
            count_target += 1
            if property_value == 0 or property_value == 1:
                if data[property_name][i] == property_value:
                    property_if_target += 1
            else:
                if property_value[0] <= data[property_name][i] and data[property_name][i] < property_value[1]:
                    property_if_target += 1
    return property_if_target / count_target


def p_target_if_property(properties_values, target):
    p_properties_if_target = []
    for p in properties_values:
        p_properties_if_target.append(p_property_if_target(p[0], p[1], target))
    numerator = numpy.prod(p_properties_if_target) * p_target(target)
    print(numerator)
    p_properties_if_target = [[], []]
    for t in [0, 1]:
        for p in properties_values:
            p_properties_if_target[t].append(p_property_if_target(p[0], p[1], t))
    denominator = sum([numpy.prod(p_properties_if_target[t]) * p_target(t) for t in [0, 1]])  # coś tutaj jest nie tak
    print(denominator)
    return numerator / denominator


print(p_property('sex', value=1), p_property('sex', value=0))
print(p_property('chol', value=[200, 1000]))
print(p_property('age', value=[50, 1000]))
print(p_property('fbs', value=1))
print(p_property('target', value=1))

print(p_property_if_target('sex', 1, 1))
print(p_property_if_target('age', [51, 1000], 1))
print(p_property_if_target('chol', [201, 1000], 1))
print(p_property_if_target('fbs', 1, 1))

print()

print(p_target_if_property([('sex', 1), ('chol', [201, 1000]), ('age', [51, 120]), ('fbs', 1)], 1))


print(p_property_if_target('sex', 1, 1))
print(p_property_if_target('sex', 0, 1))
d={'sex':['Woman', 'Man'], 'Probability': [p_property_if_target('sex', 0, 1), p_property_if_target('sex', 1, 1)]}
p=pd.DataFrame(d)

pd.DataFrame(p).to_csv('sex_if_heart_attack.csv',index=False, header=True)

age_sex_target = []
for a in range(30, 80, 5):
    print(a, a+5)
    for i in [0,1]:
        age_sex_target.append([[a, a + 5], i, p_target_if_property([('sex', i), ('age', [a, a + 5])], 1), p_target_if_property([('sex', i), ('age', [a, a + 5])], 0)])

with open('age_sex_heart_attack.csv', 'w', newline='') as file:
    writer = csv.writer(file)
    writer.writerow(["age", "sex", "heart attack", "no heart attack"])
    for i in age_sex_target:
        writer.writerow(i)