"""
=========================
Multi-dimensional scaling
=========================

An illustration of the metric and non-metric MDS on generated noisy data.

"""

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

# %%
# Dataset preparation
# -------------------
#
# We start by uniformly generating 20 points in a 2D space.

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.collections import LineCollection

from sklearn import manifold
from sklearn.decomposition import PCA
from sklearn.metrics import euclidean_distances

# Generate the data
EPSILON = np.finfo(np.float32).eps
n_samples = 20
rng = np.random.RandomState(seed=3)
X_true = rng.randint(0, 20, 2 * n_samples).astype(float)
X_true = X_true.reshape((n_samples, 2))

# Center the data
X_true -= X_true.mean()

# %%
# Now we compute pairwise distances between all points and add
# a small amount of noise to the distance matrix. We make sure
# to keep the noisy distance matrix symmetric.

# Compute pairwise Euclidean distances
distances = euclidean_distances(X_true)

# Add noise to the distances
noise = rng.rand(n_samples, n_samples)
noise = noise + noise.T
np.fill_diagonal(noise, 0)
distances += noise

# %%
# Here we compute metric, non-metric, and classical MDS of the noisy distance matrix.

mds = manifold.MDS(
    n_components=2,
    max_iter=3000,
    eps=1e-9,
    n_init=1,
    random_state=42,
    metric="precomputed",
    n_jobs=1,
    init="classical_mds",
)
X_mds = mds.fit(distances).embedding_

nmds = manifold.MDS(
    n_components=2,
    metric_mds=False,
    max_iter=3000,
    eps=1e-12,
    metric="precomputed",
    random_state=42,
    n_jobs=1,
    n_init=1,
    init="classical_mds",
)
X_nmds = nmds.fit_transform(distances)

cmds = manifold.ClassicalMDS(
    n_components=2,
    metric="precomputed",
)
X_cmds = cmds.fit_transform(distances)

# %%
# Rescaling the non-metric MDS solution to match the spread of the original data.

X_nmds *= np.sqrt((X_true**2).sum()) / np.sqrt((X_nmds**2).sum())

# %%
# To make the visual comparisons easier, we rotate the original data and all MDS
# solutions to their PCA axes. And flip horizontal and vertical MDS axes, if needed,
# to match the original data orientation.

# Rotate the data (CMDS does not need to be rotated, it is inherently PCA-aligned)
pca = PCA(n_components=2)
X_true = pca.fit_transform(X_true)
X_mds = pca.fit_transform(X_mds)
X_nmds = pca.fit_transform(X_nmds)

# Align the sign of PCs
for i in [0, 1]:
    if np.corrcoef(X_mds[:, i], X_true[:, i])[0, 1] < 0:
        X_mds[:, i] *= -1
    if np.corrcoef(X_nmds[:, i], X_true[:, i])[0, 1] < 0:
        X_nmds[:, i] *= -1
    if np.corrcoef(X_cmds[:, i], X_true[:, i])[0, 1] < 0:
        X_cmds[:, i] *= -1

# %%
# Finally, we plot the original data and all MDS reconstructions.

fig = plt.figure(1)
ax = plt.axes([0.0, 0.0, 1.0, 1.0])

s = 100
plt.scatter(X_true[:, 0], X_true[:, 1], color="navy", s=s, lw=0, label="True Position")
plt.scatter(X_mds[:, 0], X_mds[:, 1], color="turquoise", s=s, lw=0, label="MDS")
plt.scatter(
    X_nmds[:, 0], X_nmds[:, 1], color="darkorange", s=s, lw=0, label="Non-metric MDS"
)
plt.scatter(
    X_cmds[:, 0], X_cmds[:, 1], color="lightcoral", s=s, lw=0, label="Classical MDS"
)
plt.legend(scatterpoints=1, loc="best", shadow=False)

# Plot the edges
start_idx, end_idx = X_mds.nonzero()
# a sequence of (*line0*, *line1*, *line2*), where::
#            linen = (x0, y0), (x1, y1), ... (xm, ym)
segments = [
    [X_true[i, :], X_true[j, :]] for i in range(len(X_true)) for j in range(len(X_true))
]
edges = distances.max() / (distances + EPSILON) * 100
np.fill_diagonal(edges, 0)
edges = np.abs(edges)
lc = LineCollection(
    segments, zorder=0, cmap=plt.cm.Blues, norm=plt.Normalize(0, edges.max())
)
lc.set_array(edges.flatten())
lc.set_linewidths(np.full(len(segments), 0.5))
ax.add_collection(lc)

plt.show()