koaning / Memo
Licence: mit
Decorators that help you remember.
Stars: ✭ 34
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python
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Installation
pip install memo
Documentation
The documentation can be found here.
The quickstart guide is found here.
Usage
Here's an example of utility functions provided by our library.
import numpy as np
from memo import memlist, memfile, grid, time_taken
data = []
@memfile(filepath="results.jsonl")
@memlist(data=data)
@time_taken()
def birthday_experiment(class_size, n_sim):
"""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
proba = np.mean(n_uniq != class_size)
return {"est_proba": proba}
for settings in grid(class_size=[5, 10, 20, 30], n_sim=[1000, 1_000_000]):
birthday_experiment(**settings)
The decorators memlist
and memfile
are making sure that the keyword arugments and
dictionary output of the birthday_experiment
are logged. The contents of the results.jsonl
-file
and the data
variable looks like this;
{"class_size": 5, "n_sim": 1000, "est_proba": 0.024, "time_taken": 0.0004899501800537109}
{"class_size": 5, "n_sim": 1000000, "est_proba": 0.027178, "time_taken": 0.19407916069030762}
{"class_size": 10, "n_sim": 1000, "est_proba": 0.104, "time_taken": 0.000598907470703125}
{"class_size": 10, "n_sim": 1000000, "est_proba": 0.117062, "time_taken": 0.3751380443572998}
{"class_size": 20, "n_sim": 1000, "est_proba": 0.415, "time_taken": 0.0009679794311523438}
{"class_size": 20, "n_sim": 1000000, "est_proba": 0.411571, "time_taken": 0.7928380966186523}
{"class_size": 30, "n_sim": 1000, "est_proba": 0.703, "time_taken": 0.0018239021301269531}
{"class_size": 30, "n_sim": 1000000, "est_proba": 0.706033, "time_taken": 1.1375510692596436}
The nice thing about being able to log results to a file or to the web is that you can also more easily parallize your jobs!
Features
This library also offers decorators to pipe to other sources.
-
memlists
sends the json blobs to a list -
memfile
sends the json blobs to a file -
memweb
sends the json blobs to a server via http-post requests -
memfunc
sends the data to a callable that you supply, likeprint
-
grid
generates a convenient grid for your experiments -
random_grid
generates a randomized grid for your experiments -
time_taken
also logs the time the function takes to run
Check the API docs here for more information on how these work.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].