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5eace46 ea61d54 5eace46 1b8a737 5eace46 1b8a737 5eace46 1b8a737 5eace46 1b8a737 5eace46 ea61d54 5eace46 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | # t-SNE page: optional helper embedding + scatter (StreamlitApp also runs t-SNE inline with Plotly).
# Kept for reuse; main app path uses the same encode_sequence + MLP hidden features.
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import streamlit as st
import torch
import numpy as np
from utils.predict import encode_sequence, get_embedding_extractor
def tsne_visualization(sequences, model):
# Project model embeddings into 2D and render a quick scatter plot.
st.info("Generating embeddings... this may take a moment.")
embeddings = []
embedding_extractor = get_embedding_extractor(model)
for seq in sequences:
x = torch.tensor(encode_sequence(seq, model), dtype=torch.float32).unsqueeze(0)
with torch.no_grad():
# Use an early hidden layer as a compact learned representation.
emb = embedding_extractor(x)
embeddings.append(emb.numpy().flatten())
embeddings = np.vstack(embeddings)
# Perplexity must be < n_samples; cap at 30 for stability on small sets.
perplexity = min(30, len(sequences) - 1)
if perplexity < 2:
st.warning("Need at least 2 sequences for visualization.")
return
tsne = TSNE(n_components=2, random_state=42, perplexity=perplexity)
reduced = tsne.fit_transform(embeddings)
df = pd.DataFrame(reduced, columns=["x", "y"])
st.success("t-SNE visualization complete.")
st.scatter_chart(df) |