memo: conclusion

Sometimes you're experimenting away in a notebook but you want to have a stable method of collecting metrics for your experiments. Memo is a lightweight tool that might help out in these situations.

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Notes

We're using the same data collecting code.

import numpy as np
from memo import memlist, memfile, memfunc, memweb

data = []

@memlist(data=data)
def birthday_experiment(class_size, n_sim=1000):
    """Simulates the birthday paradox. Vectorized = Fast!"""
    sims = np.random.randint(1, 365 + 1, (n_sim, class_size))
    sort_sims = np.sort(sims, axis=1)
    n_uniq = (sort_sims[:, 1:] != sort_sims[:, :-1]).sum(axis = 1) + 1
    return {"est_prob": np.mean(n_uniq != class_size), "number": 42}

settings = grid(
    class_size=range(2, 100),
    n_sim=[1_000, 10_000, 100_000]
)

for setting in settings:
    birthday_experiment(**setting)

But now we're visualising it with hiplot.

import hiplot as hip
hip.Experiment.from_iterable(data).display()

For more information on hiplot you might enjoy our tutorial here.

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