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meme-search-engine/meme-rater/shared.py

53 lines
1.8 KiB
Python

import sqlite3
import hashlib
from collections import defaultdict
import numpy
import random
db = sqlite3.connect("data.sqlite3")
db.row_factory = sqlite3.Row
val_fraction = 0.2
def is_val_set(meme1, meme2):
def is_one_val(meme):
return hashlib.sha256(meme.encode("utf-8")).digest()[0] / 255 < (val_fraction / 2) # not strictly correct but good enough
return is_one_val(meme1) or is_one_val(meme2)
def fetch_embedding(filename):
csr = db.execute("SELECT embedding_vector FROM files WHERE filename = ?", (filename,))
x = numpy.frombuffer(csr.fetchone()[0], dtype="float16")
csr.close()
return x.copy() # PyTorch complains otherwise due to bad
def map_rating(rating, uncertainty=0.05):
match rating:
case "1": # meme 1 is better
return 1 - uncertainty
case "2":
return uncertainty
case _: raise ValueError("invalid rating, please fix")
def fetch_ratings():
trains = defaultdict(list)
validations = defaultdict(list)
csr = db.execute("SELECT meme1, meme2, rating, iteration FROM ratings")
for meme1, meme2, rating, iteration in csr.fetchall():
(validations if is_val_set(meme1, meme2) else trains)[int(iteration or "0")].append((fetch_embedding(meme1), fetch_embedding(meme2), map_rating(rating)))
csr.close()
return list(x[1] for x in sorted(trains.items())), list(x[1] for x in sorted(validations.items()))
def generate_random_permutations(x, n):
out = []
for _ in range(n):
random.shuffle(x)
out.append(x.copy())
return out
def fetch_all_files():
csr = db.execute("SELECT filename, embedding_vector FROM files")
x = [ (row[0], numpy.frombuffer(row[1], dtype="float16").copy()) for row in csr.fetchall() ]
csr.close()
return x
def checkpoint_for(steps):
return f"./ckpt/model-{steps}.pt", f"./ckpt/optim-{steps}.pt"