import io import itertools import textwrap import zipfile from typing import List, Tuple import numpy as np import pandas as pd import plotly.express as px import streamlit as st from scipy.spatial.distance import cdist from sklearn.cluster import DBSCAN, KMeans from sklearn.decomposition import PCA from sklearn.datasets import make_swiss_roll from sklearn.manifold import TSNE, trustworthiness import umap.umap_ as umap # ── Example shapes ─────────────────────────────────────────────────────────── def generate_hypercube(n=4): return np.array(list(itertools.product([0, 1], repeat=n)), dtype=float) def generate_simplex(n=3): eye = np.eye(n, dtype=float) origin = np.zeros((1, n), dtype=float) return np.vstack([eye, origin]) def generate_swiss_roll(n_samples=500, noise=0.05): X, _ = make_swiss_roll(n_samples=n_samples, noise=noise) return X EXAMPLE_SHAPES = { "Cube (3-D, 8 pts)": 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] ], dtype=float), "Square pyramid (3-D, 5 pts)": np.array([ [-1,-1,0],[1,-1,0],[1,1,0],[-1,1,0],[0,0,1] ], dtype=float), "4-D hypercube (16 pts)": generate_hypercube(4), "3-simplex (4 pts in 3-D)": generate_simplex(3), "Swiss roll (500 pts, 3-D)": generate_swiss_roll, } # ── Helpers ────────────────────────────────────────────────────────────────── def parse_text_points(text: str) -> np.ndarray: txt = textwrap.dedent(text.strip()) rows = [r for r in txt.splitlines() if r.strip()] data = [list(map(float, r.replace(",", " ").split())) for r in rows] return np.array(data, dtype=float) def run_tsne(data, perp, seed): ts = TSNE(n_components=2, perplexity=perp, random_state=seed, init="pca") emb = ts.fit_transform(data) return emb, ts.kl_divergence_ def run_pca(data): pca = PCA(n_components=2) return pca.fit_transform(data), None def run_umap(data, n_neighbors, min_dist, seed): um = umap.UMAP(n_components=2, n_neighbors=n_neighbors, min_dist=min_dist, random_state=seed) return um.fit_transform(data), None def distinct_count(dist_row: np.ndarray, tol: float = 1e-3) -> int: """Count unique non-zero distances in a row after rounding to 3 decimals.""" nz = dist_row[dist_row > tol] rounded = (nz * 1000).round().astype(int) # rounding to 3 d.p. return len(np.unique(rounded)) # ── Streamlit UI ───────────────────────────────────────────────────────────── st.set_page_config(layout="wide") st.title("🌀 Dimensionality Reduction Explorer") st.write(""" Upload **one or many** CSV/TXT files *or* use an example shape, pick an algorithm, (optionally cluster), and explore the 2-D embedding. Every output CSV now contains the embedding, the original point coordinates, all pair-wise distances, **and** the number of distinct distances per point. """) # ── Sidebar ────────────────────────────────────────────────────────────────── with st.sidebar: st.header("1️⃣ Data Input") mode = st.radio("Source", ["Example shape", "Upload CSV/TXT", "Paste text"]) datasets: List[Tuple[str, np.ndarray]] = [] if mode == "Example shape": key = st.selectbox("Choose example", list(EXAMPLE_SHAPES.keys())) src = EXAMPLE_SHAPES[key] data_raw = src() if callable(src) else src datasets.append((key.replace(" ", "_"), data_raw)) elif mode == "Upload CSV/TXT": uploads = st.file_uploader( "Upload file(s)", type=["csv", "txt"], accept_multiple_files=True ) if not uploads: st.stop() for up in uploads: txt = io.StringIO(up.getvalue().decode("utf-8")).read() pts = parse_text_points(txt) datasets.append((up.name.rsplit(".", 1)[0], pts)) else: # Paste text placeholder = "e.g.\n0,0,0\n0,0,1\n0,1,0\n..." txt = st.text_area("Paste coordinates", height=200, placeholder=placeholder) if not txt.strip(): st.stop() datasets.append(("pasted_points", parse_text_points(txt))) st.header("2️⃣ Algorithm & Params") algo = st.selectbox("Method", ["t-SNE", "PCA", "UMAP"]) seed = st.number_input("Random seed", value=42, step=1) if algo == "t-SNE": perp = st.slider("Perplexity", 1.0, 50.0, 30.0, 1.0) elif algo == "UMAP": neighbors = st.slider("n_neighbors", 5, 200, 15, 5) min_dist = st.slider("min_dist", 0.0, 0.99, 0.1, 0.01) st.header("3️⃣ Clustering (optional)") do_cluster = st.checkbox("Cluster embedding") if do_cluster: cluster_algo = st.selectbox("Algorithm", ["KMeans", "DBSCAN"]) if cluster_algo == "KMeans": n_clusters = st.slider("n_clusters", 2, 10, 3, 1) else: eps = st.slider("DBSCAN eps", 0.1, 5.0, 0.5, 0.1) st.markdown("---") run = st.button("Run & Visualize 🚀") # ── Main processing ───────────────────────────────────────────────────────── def process_dataset(name: str, pts: np.ndarray) -> Tuple[pd.DataFrame, dict]: if pts.ndim != 2 or pts.shape[0] < 2: st.error(f"Dataset **{name}** needs at least two points (rows).") return None, None # 1. Reduce dimensionality if algo == "t-SNE": emb, kl = run_tsne(pts, perp, seed) elif algo == "PCA": emb, kl = run_pca(pts) else: emb, kl = run_umap(pts, neighbors, min_dist, seed) # 2. Trustworthiness n_samples = pts.shape[0] k_max = (n_samples - 1) // 2 tw = trustworthiness(pts, emb, n_neighbors=k_max) if k_max >= 1 else None # 3. Build DataFrame in requested column order df_emb = pd.DataFrame(emb, columns=["x", "y"]) df_pts = pd.DataFrame(pts, columns=[f"p{i}" for i in range(pts.shape[1])]) df = pd.concat([df_emb, df_pts], axis=1) # 4. Clustering (optional) if do_cluster: if cluster_algo == "KMeans": labels = KMeans(n_clusters=n_clusters, random_state=seed).fit_predict(emb) else: labels = DBSCAN(eps=eps).fit_predict(emb) df["cluster"] = labels.astype(str) # 5. Pair-wise distances (in embedding space) dists = cdist(emb, emb, metric="euclidean") dist_df = pd.DataFrame(dists, columns=[f"dist_{i}" for i in range(n_samples)]) df = pd.concat([df, dist_df], axis=1) # 6. Distinct-distance count per point df["distinct_count"] = [distinct_count(row) for row in dists] return df, {"kl": kl, "tw": tw, "k_max": k_max} if run: zip_buffer = io.BytesIO() with zipfile.ZipFile(zip_buffer, "w", zipfile.ZIP_DEFLATED) as zf: for name, pts in datasets: st.subheader(f"📂 Dataset: {name}") out_df, stats = process_dataset(name, pts) if out_df is None: continue # Plot color_col = "cluster" if "cluster" in out_df.columns else None fig = px.scatter(out_df, x="x", y="y", color=color_col, title=f"{algo} embedding ({name})", width=700, height=500) fig.update_traces(marker=dict(size=8)) fig.update_layout(margin=dict(l=20, r=20, t=40, b=20)) st.plotly_chart(fig, use_container_width=True) # Stats if stats["tw"] is not None: st.markdown(f"**Trustworthiness (k={stats['k_max']}):** {stats['tw']:.3f}") else: st.markdown("**Trustworthiness:** Not enough samples to compute.") if stats["kl"] is not None: st.markdown(f"**t-SNE KL divergence:** {stats['kl']:.3f}") # Data preview with st.expander("Preview first 10 rows"): st.dataframe(out_df.head(10)) # Individual CSV download csv_bytes = out_df.to_csv(index=False).encode("utf-8") st.download_button( f"Download CSV ({name})", data=csv_bytes, file_name=f"{name}_embedding_with_distances.csv", mime="text/csv" ) # Add to ZIP zf.writestr(f"{name}_embedding_with_distances.csv", csv_bytes) # ZIP download (always available once run) st.download_button( "📦 Download ALL results as ZIP", data=zip_buffer.getvalue(), file_name="all_embeddings_with_distances.zip", mime="application/zip" )