osmarks/meme-search-engine
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Code Issues Releases Wiki Activity

meme interpretability

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osmarks 2024-05-22 16:18:45 +01:00
parent bd426a30ba
commit 349fe802f7
2 changed files with 87 additions and 1 deletions
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25
clipfront2/src/App.svelte
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@ -95,6 +95,9 @@
{:else if term.type === "text"}
<input type="search" use:focusEl on:keydown={handleKey} bind:value={term.text} />
{/if}
{#if term.type === "embedding"}
<span>[embedding loaded from URL]</span>
{/if}
</li>
{/each}
</ul>
@ -148,6 +151,20 @@
let queryTerms = []
let queryCounter = 0
const decodeFloat16 = uint16 => {
const sign = (uint16 & 0x8000) ? -1 : 1
const exponent = (uint16 & 0x7C00) >> 10
const fraction = uint16 & 0x03FF
if (exponent === 0) {
return sign * Math.pow(2, -14) * (fraction / Math.pow(2, 10))
} else if (exponent === 0x1F) {
return fraction ? NaN : sign * Infinity
} else {
return sign * Math.pow(2, exponent - 15) * (1 + fraction / Math.pow(2, 10))
}
}
const focusEl = el => el.focus()
const newTextQuery = (content=null) => {
queryTerms.push({ type: "text", weight: 1, sign: "+", text: typeof content === "string" ? content : "" })
@ -183,7 +200,7 @@
let displayedResults = []
const runSearch = async () => {
if (!resultPromise) {
let args = {"terms": queryTerms.map(x => ({ image: x.imageData, text: x.text, weight: x.weight * { "+": 1, "-": -1 }[x.sign] }))}
let args = {"terms": queryTerms.map(x => ({ image: x.imageData, text: x.text, embedding: x.embedding, weight: x.weight * { "+": 1, "-": -1 }[x.sign] }))}
queryCounter += 1
resultPromise = util.doQuery(args).then(res => {
error = null
@ -252,4 +269,10 @@
newTextQuery(queryStringParams.get("q"))
runSearch()
}
if (queryStringParams.get("e")) {
const binaryData = atob(queryStringParams.get("e").replace(/\-/g, "+").replace(/_/g, "/"))
const uint16s = new Uint16Array(new Uint8Array(binaryData.split('').map(c => c.charCodeAt(0))).buffer)
queryTerms.push({ type: "embedding", weight: 1, sign: "+", embedding: Array.from(uint16s).map(decodeFloat16) })
runSearch()
}
</script>

63
meme-rater/pca.py Normal file
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@ -0,0 +1,63 @@
import sklearn.decomposition
import numpy as np
import sqlite3
import asyncio
import aiohttp
import base64
meme_search_backend = "http://localhost:1707/"
memes_url = "https://i.osmarks.net/memes-or-something/"
meme_search_url = "https://mse.osmarks.net/?e="
db = sqlite3.connect("/srv/mse/data.sqlite3")
db.row_factory = sqlite3.Row
def fetch_all_files():
csr = db.execute("SELECT embedding FROM files WHERE embedding IS NOT NULL")
x = [ np.frombuffer(row[0], dtype="float16").copy() for row in csr.fetchall() ]
csr.close()
return np.array(x)
embeddings = fetch_all_files()
print("loaded")
pca = sklearn.decomposition.PCA()
pca.fit(embeddings)
print(pca.explained_variance_ratio_)
print(pca.components_)
def emb_url(embedding):
return meme_search_url + base64.urlsafe_b64encode(embedding.astype(np.float16).tobytes()).decode("utf-8")
async def get_exemplars():
with open("components.html", "w") as f:
f.write("""<!DOCTYPE html>
<title>Embeddings PCA</title>
<style>
div img {
width: 20%
}
</style>
<body><h1>Embeddings PCA</h1>""")
async with aiohttp.ClientSession():
async def lookup(embedding):
async with aiohttp.request("POST", meme_search_backend, json={
"terms": [{ "embedding": list(float(x) for x in embedding) }], # sorry
"k": 10
}) as res:
return (await res.json())["matches"]
for i, (component, explained_variance_ratio) in enumerate(zip(pca.components_, pca.explained_variance_ratio_)):
f.write(f"""
<h2>Component {i}</h2>
<h3>Explained variance {explained_variance_ratio*100:0.2}%</h3>
<div>
<h4><a href="{emb_url(component)}">Max</a></h4>
""")
for match in await lookup(component):
f.write(f'<img loading="lazy" src="{memes_url+match[1]}">')
f.write(f'<h4><a href="{emb_url(-component)}">Min</a></h4>')
for match in await lookup(-component):
f.write(f'<img loading="lazy" src="{memes_url+match[1]}">')
f.write("</div>")
asyncio.run(get_exemplars())