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