import io import textwrap import itertools import zipfile from typing import List, Tuple import numpy as np import pandas as pd import streamlit as st from sklearn.manifold import TSNE, trustworthiness from sklearn.decomposition import PCA from sklearn.cluster import KMeans, DBSCAN import umap.umap_ as umap import plotly.express as px from scipy.spatial.distance import cdist from sklearn.datasets import make_swiss_roll # ── Example shapes (some generated on demand) ──────────────────────────────── 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 # ── Streamlit UI ───────────────────────────────────────────────────────────── st.set_page_config(layout="wide") st.title("🌀 Dimensionality Reduction Explorer") st.write(""" Upload **one or many** CSV/TXT files *or* use the other sources, pick an algorithm, (optionally cluster), and explore the 2‑D embedding. Each result is downloadable with a full pair‑wise distance table. """) # 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 one **or many** files", 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() data_raw = parse_text_points(txt) datasets.append(("pasted_points", data_raw)) 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): if pts.ndim != 2 or pts.shape[0] < 2: st.error(f"Dataset **{name}** needs at least two points in an (n_pts × n_dims) array.") return None, None # Dimensionality reduction 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) # 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 # DataFrame for embedding df = pd.DataFrame(emb, columns=["x", "y"]) # Clustering 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) # Pair‑wise distances in embedding dist_matrix = cdist(emb, emb, metric="euclidean") dist_df = pd.DataFrame(dist_matrix, columns=[f"dist_{i}" for i in range(n_samples)]) out_df = pd.concat([df, dist_df], axis=1) return out_df, {"kl": kl, "tw": tw, "k_max": k_max} if run: results: List[Tuple[str, pd.DataFrame]] = [] for name, pts in datasets: st.subheader(f"📂 Dataset: {name}") out_df, stats = process_dataset(name, pts) if out_df is None: continue # Scatter plot color_arg = "cluster" if ("cluster" in out_df.columns) else None fig = px.scatter(out_df, x="x", y="y", color=color_arg, 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 (need ≥ 3 points).") if stats["kl"] is not None: st.markdown(f"**t‑SNE KL divergence:** {stats['kl']:.3f}") # Distance matrix preview with st.expander("🔍 Show pair‑wise distance matrix"): st.dataframe(out_df.filter(like="dist_")) # Download CSV for this dataset csv_bytes = out_df.to_csv(index=False).encode("utf‑8") st.download_button( f"Download embedding + distances ({name})", data=csv_bytes, file_name=f"{name}_embedding_with_distances.csv", mime="text/csv" ) # Keep for ZIP if batch results.append((name, csv_bytes)) # One‑click ZIP if multiple datasets if len(results) >= 2: zip_buf = io.BytesIO() with zipfile.ZipFile(zip_buf, "w", zipfile.ZIP_DEFLATED) as zf: for nm, csv_b in results: zf.writestr(f"{nm}_embedding_with_distances.csv", csv_b) st.download_button( "📦 Download **all** results as ZIP", data=zip_buf.getvalue(), file_name="all_embeddings_with_distances.zip", mime="application/zip" )