is it possible to `predict recall` in pure sql?

[CrackedBeefcake]

the title says it all.
is it possible to predict recall in pure sql?

my usecase is that i'd like to maintain a column recallProbabilityNow and update that every few minutes.
of course, i can just load all the rows and do the compute in python/js, but it would be cool (and fast, i guess) to run this inside the db.

[fasiha]

@LazerJesus which version are we talking about? (I think the answer is going to be "not really" but depending on which version you have different options 😅)

[CrackedBeefcake]

i am on v2

[fasiha]

First, sorry for the delay! Life got busy (but happy busy 😁).

Second, to do this in SQL we have to use an approximation. We pretend that $E[p^\delta] \approx (E[p])^\delta$.

In words, we know that the expected value of a random variable $p$ (probability of recall) raised to a power is not equal to the expected value itself raised to the power. (There's a brief discussion of this at https://fasiha.github.io/ebisu/#why-we-work-with-random-variables.)

However, in practice, maybe the difference isn't that bad. Here's how to compute this approximation in both Python and SQL: I create a simple SQLite db that contains a timestamp (seconds in the Unix epoch) and the Ebisu model as JSON and then, given the current time, calculate the approximate recall probability entirely in SQLite:

import sqlite3
import time
import json
from math import exp
import ebisu  # type: ignore

HOURS_PER_SECOND = 1 / 3600
connection = sqlite3.connect(":memory:")
cursor = connection.cursor()

cursor.execute("""
CREATE TABLE IF NOT EXISTS data (
    quiz TEXT,
    timestamp FLOAT, -- seconds in Unix epoch
    ebisu TEXT
)
""")

quizName = 'foo'
model = ebisu.defaultModel(24, 2.5)  # 24 hours halflife, α = β = 2.5
t = time.time()  # seconds in Unix epoch

cursor.execute("INSERT INTO data (quiz, timestamp, ebisu) VALUES (?, ?, ?)",
               (quizName, t, json.dumps(model)))

elapsedHours = 10
newT = t + elapsedHours / HOURS_PER_SECOND  # some time in the future, Unix seconds

exact = ebisu.predictRecall(model, elapsedHours,
                            exact=True)  # full Bayesian expectation
approx = exp(-((newT - t) * HOURS_PER_SECOND) / model[-1])

# replace the first ? with `strftime('%s', 'now')` if you want SQL to compute the current
# time (seconds in Unix epoch) instead of passing it in
query = """
SELECT exp(-((? - timestamp) / (60 * 60)) / json_extract(ebisu, '$[2]')) AS approx
FROM data
WHERE quiz=?
"""
cursor.execute(query, (newT, quizName))
fromSql = cursor.fetchone()[0]

relativeError = abs(fromSql - approx) / approx
print(f'{approx=:0.6f}, {fromSql=:0.6f} ({relativeError=}; {exact=:0.2f})')

Output:

approx=0.659241, fromSql=0.659241 (relativeError=0.0; exact=0.73)

There's no error between Python and SQL calculating the exp, but you see there's quite a bit of difference between the approximation, 66%, and the exact prediction 73%. Probably in practice this difference isn't that big a deal, since Ebisu v2 is bad at the exact probability (though hopefully the relative ranking is acceptable).

Does this help?