A/B test with python
This is just going to make our generated datas as well as is able to emulate a real data collection surface.
class DataGenerator: def __init__(self, p1, p2): self.p1 = p1 self.p2 = p2 def next(self): if np.random.random() < self.p1: click1 = 1 else: click1 = 0 if np.random.random() < self.p2: click2 = 1 else: click2 = 0 return click1, click2
p1 and p2 are probability of click for group 1 and group 2.
Next, I will write a function for obtaining the p-value.
def get_p_value(T): det = T[0,0]*T[1,1] - T[0,1]*T[1,0] c2 = float(det) / T[0].sum() * det / T[1].sum() * T.sum() / T[:,0].sum() / T[:,1].sum() p = 1 - chi2.cdf(x=c2, df=1) return p
I am going to explain p-value later in the other article.
Next, I will write a function for running a experiment.
That is going to include the probability of click for group1 and group2 and the number of samples.
In this case, I am going to take 2500 samples.
def run_experiment(p1, p2, N): data = DataGenerator(p1, p2) p_values = np.empty(N) T = np.zeros((2,2)).astype(np.float32) for i in range(N): c1, c2 = data.next() T[0,c1] += 1 T[1,c2] += 1 if i < 10: p_values[i] = None else: p_values[i] = get_p_value(T) plt.plot(p_values) plt.plot(np.ones(N)*0.05) plt.show() run_experiment(0.1, 0.11, 2500)
data = DataGenerator(p1, p2)
The data written above is to create an instance of data generator.
if i < 10: p_values[i] = None else: p_values[i] = get_p_value(T)
We have to ignore the first a few datas in terms of taking into account p-value.
Because if we try to calculate the p-value too early, the formula might be broken.
c2 = float(det) / T[0].sum() * det / T[1].sum() * T.sum() / T[:,0].sum() / T[:,1].sum()
I divided by row sums and column sums, so if any of those are 0, I cannot calculate it.