I have a question.
I am currently generating training data for my bayesian network as follows:
(also as code down below)
-> infected stands for people who are infected (0= not infected, 1= infected)
-> p_tests is the result of the test (0= test negative, 1= test positive)
Wether there is a 0 or 1 in TP, FN, FP and TN is decided like this:
data = np.random.randint(2, size=(10, 2)) columns = ['infected', 'p_tests'] df = pd.DataFrame(data=data, columns=columns) df["TP"] = ((df['infected'] == 1) & (df['p_tests'] == 1)).astype(int) df["FN"] = ((df['infected'] == 1) & (df['p_tests'] == 0)).astype(int) df["FP"] = ((df['infected'] == 0) & (df['p_tests'] == 1)).astype(int) df["TN"] = ((df['infected'] == 0) & (df['p_tests'] == 0)).astype(int) print(df)
So that is running fine.
My question now would be, how I can decide eg. the 1 and 0 of the infected group based on my probabilities.
The chance to be infected is 10%. How can I program the data, so that 10% of my set have 1s (show that 10% are infected) ?
The same is with the probability of TP (80%), TN(98%), FP(2%), FN(20%)
Does anyone have an Ideo on how to solve this?
To assign values at random with a set probability, e.g. P(infected) = 0.9, you can proceed like this:
Choose random floating-point values
rin the range
df['r'] = np.random.uniform(0, 1.0, size=df.shape)
Set the value of
infectedbased on the probability threshold:
df['infected'] = (df['r'] >= 0.9).astype(int)
Answered By – alexis
Answer Checked By – Marie Seifert (AngularFixing Admin)