WIP add caching to .skb.apply

[jeromedockes]

this is very much a POC / draft. the calls to estimators used in .skb.apply() are cached with joblib

[jeromedockes]

example script

import time

import skrub
from sklearn.datasets import make_classification
from sklearn.ensemble import HistGradientBoostingClassifier

X_a, y_a = make_classification(n_samples=100_000, n_features=200)
pred = skrub.X(X_a).skb.apply(HistGradientBoostingClassifier(), y=skrub.y(y_a))
split = pred.skb.train_test_split()
learner = pred.skb.make_learner()

tic = time.perf_counter()
learner.fit(split["train"])
toc = time.perf_counter()
print('first fit', toc - tic)

tic = time.perf_counter()
learner.fit(split["train"])
toc = time.perf_counter()
print('second fit', toc - tic)

tic = time.perf_counter()
result = learner.predict_proba(split["test"])
toc = time.perf_counter()
print('first predict', toc - tic)

tic = time.perf_counter()
result = learner.predict_proba(split["test"])
toc = time.perf_counter()
print('second predict', toc - tic)

first fit 3.222974759002682
second fit 0.22381325099559035
first predict 0.13882737699896097
second predict 0.06695482600480318