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import io
import textwrap
import numpy as np
import pandas as pd
import streamlit as st
from sklearn.manifold import TSNE
import plotly.express as px
# -------------- Helper functions -------------------------------------------
EXAMPLE_SHAPES = {
"Cube (3-D, 8 vertices)": np.array([
[0, 0, 0], [0, 0, 1],
[0, 1, 0], [0, 1, 1],
[1, 0, 0], [1, 0, 1],
[1, 1, 0], [1, 1, 1]
]),
"Square pyramid (3-D, 5 vertices)": np.array([
[-1, -1, 0],
[ 1, -1, 0],
[ 1, 1, 0],
[-1, 1, 0],
[ 0, 0, 1]
])
}
def parse_text_points(text: str) -> np.ndarray:
"""
Parse a multiline string of comma- or whitespace-separated numbers
into an (n_points, n_dims) array.
"""
cleaned = textwrap.dedent(text.strip())
rows = [row for row in cleaned.splitlines() if row.strip()]
data = [list(map(float, row.replace(",", " ").split())) for row in rows]
return np.array(data, dtype=float)
def run_tsne(data: np.ndarray, perplexity: float, seed: int) -> np.ndarray:
tsne = TSNE(
n_components=2,
perplexity=perplexity,
random_state=seed,
init="pca"
)
return tsne.fit_transform(data)
# ---------------------------------------------------------------------------
st.title("🌀 t-SNE Explorer for n-D Point Clouds")
st.markdown(
"""
Upload or paste your points, choose parameters, and see how
**t-SNE** flattens them into 2-D.
*Example shapes* are provided for quick experimentation.
"""
)
# --- Sidebar controls -------------------------------------------------------
with st.sidebar:
st.header("1️⃣ Choose data source")
source = st.radio(
"Data input method",
["Example shape", "Upload CSV/TXT", "Paste raw text"]
)
if source == "Example shape":
shape_key = st.selectbox("Pick a shape", list(EXAMPLE_SHAPES.keys()))
data_raw = EXAMPLE_SHAPES[shape_key]
elif source == "Upload CSV/TXT":
file = st.file_uploader("Upload coordinates file (*.csv / *.txt)")
if file:
text = io.StringIO(file.getvalue().decode("utf-8")).read()
data_raw = parse_text_points(text)
else:
st.stop()
else: # Paste text
placeholder = "e.g.\n0,0,0\n0,0,1\n0,1,0\n..."
text = st.text_area("Paste coordinates (one point per line)", height=200, placeholder=placeholder)
if not text.strip():
st.stop()
data_raw = parse_text_points(text)
st.divider()
st.header("2️⃣ t-SNE parameters")
perplexity = st.slider("Perplexity", 5.0, 50.0, 30.0, 1.0)
seed = st.number_input("Random seed", value=42, step=1)
run_button = st.button("Run t-SNE 🚀")
# --- Main area --------------------------------------------------------------
if run_button:
if data_raw.ndim != 2 or data_raw.shape[0] < 2:
st.error("Need at least two points; check your input.")
st.stop()
if perplexity >= data_raw.shape[0]:
st.error("Perplexity must be less than the number of points.")
st.stop()
embedding = run_tsne(data_raw, perplexity, seed)
df_plot = pd.DataFrame(embedding, columns=["x", "y"])
st.subheader("2-D embedding")
fig = px.scatter(df_plot, x="x", y="y", width=700, height=500)
fig.update_traces(marker=dict(size=10))
fig.update_layout(margin=dict(l=20, r=20, t=30, b=20))
st.plotly_chart(fig, use_container_width=True)
with st.expander("Show raw data"):
st.write(pd.DataFrame(data_raw)) |