"""
=============================================================================
Manifold learning on handwritten digits: Locally Linear Embedding, Isomap...
=============================================================================

We illustrate various embedding techniques on the digits dataset.

"""

# Authors: Fabian Pedregosa <fabian.pedregosa@inria.fr>
#          Olivier Grisel <olivier.grisel@ensta.org>
#          Mathieu Blondel <mathieu@mblondel.org>
#          Gael Varoquaux
#          Guillaume Lemaitre <g.lemaitre58@gmail.com>
# License: BSD 3 clause (C) INRIA 2011


# %%
# Load digits dataset
# -------------------
# We will load the digits dataset and only use six first of the ten available classes.
from sklearn.datasets import load_digits

digits = load_digits(n_class=6)
X, y = digits.data, digits.target
n_samples, n_features = X.shape
n_neighbors = 30

# %%
# We can plot the first hundred digits from this data set.
import matplotlib.pyplot as plt

fig, axs = plt.subplots(nrows=10, ncols=10, figsize=(6, 6))
for idx, ax in enumerate(axs.ravel()):
    ax.imshow(X[idx].reshape((8, 8)), cmap=plt.cm.binary)
    ax.axis("off")
_ = fig.suptitle("A selection from the 64-dimensional digits dataset", fontsize=16)

# %%
# Helper function to plot embedding
# ---------------------------------
# Below, we will use different techniques to embed the digits dataset. We will plot
# the projection of the original data onto each embedding. It will allow us to
# check whether or digits are grouped together in the embedding space, or
# scattered across it.
import numpy as np
from matplotlib import offsetbox

from sklearn.preprocessing import MinMaxScaler


def plot_embedding(X, title):
    _, ax = plt.subplots()
    X = MinMaxScaler().fit_transform(X)

    for digit in digits.target_names:
        ax.scatter(
            *X[y == digit].T,
            marker=f"${digit}$",
            s=60,
            color=plt.cm.Dark2(digit),
            alpha=0.425,
            zorder=2,
        )
    shown_images = np.array([[1.0, 1.0]])  # just something big
    for i in range(X.shape[0]):
        # plot every digit on the embedding
        # show an annotation box for a group of digits
        dist = np.sum((X[i] - shown_images) ** 2, 1)
        if np.min(dist) < 4e-3:
            # don't show points that are too close
            continue
        shown_images = np.concatenate([shown_images, [X[i]]], axis=0)
        imagebox = offsetbox.AnnotationBbox(
            offsetbox.OffsetImage(digits.images[i], cmap=plt.cm.gray_r), X[i]
        )
        imagebox.set(zorder=1)
        ax.add_artist(imagebox)

    ax.set_title(title)
    ax.axis("off")


# %%
# Embedding techniques comparison
# -------------------------------
#
# Below, we compare different techniques. However, there are a couple of things
# to note:
#
# * the :class:`~sklearn.ensemble.RandomTreesEmbedding` is not
#   technically a manifold embedding method, as it learn a high-dimensional
#   representation on which we apply a dimensionality reduction method.
#   However, it is often useful to cast a dataset into a representation in
#   which the classes are linearly-separable.
# * the :class:`~sklearn.discriminant_analysis.LinearDiscriminantAnalysis` and
#   the :class:`~sklearn.neighbors.NeighborhoodComponentsAnalysis`, are supervised
#   dimensionality reduction method, i.e. they make use of the provided labels,
#   contrary to other methods.
# * the :class:`~sklearn.manifold.TSNE` is initialized with the embedding that is
#   generated by PCA in this example. It ensures global stability  of the embedding,
#   i.e., the embedding does not depend on random initialization.
from sklearn.decomposition import TruncatedSVD
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.ensemble import RandomTreesEmbedding
from sklearn.manifold import (
    MDS,
    TSNE,
    Isomap,
    LocallyLinearEmbedding,
    SpectralEmbedding,
)
from sklearn.neighbors import NeighborhoodComponentsAnalysis
from sklearn.pipeline import make_pipeline
from sklearn.random_projection import SparseRandomProjection

embeddings = {
    "Random projection embedding": SparseRandomProjection(
        n_components=2, random_state=42
    ),
    "Truncated SVD embedding": TruncatedSVD(n_components=2),
    "Linear Discriminant Analysis embedding": LinearDiscriminantAnalysis(
        n_components=2
    ),
    "Isomap embedding": Isomap(n_neighbors=n_neighbors, n_components=2),
    "Standard LLE embedding": LocallyLinearEmbedding(
        n_neighbors=n_neighbors, n_components=2, method="standard"
    ),
    "Modified LLE embedding": LocallyLinearEmbedding(
        n_neighbors=n_neighbors, n_components=2, method="modified"
    ),
    "Hessian LLE embedding": LocallyLinearEmbedding(
        n_neighbors=n_neighbors, n_components=2, method="hessian"
    ),
    "LTSA LLE embedding": LocallyLinearEmbedding(
        n_neighbors=n_neighbors, n_components=2, method="ltsa"
    ),
    "MDS embedding": MDS(
        n_components=2, n_init=1, max_iter=120, n_jobs=2, normalized_stress="auto"
    ),
    "Random Trees embedding": make_pipeline(
        RandomTreesEmbedding(n_estimators=200, max_depth=5, random_state=0),
        TruncatedSVD(n_components=2),
    ),
    "Spectral embedding": SpectralEmbedding(
        n_components=2, random_state=0, eigen_solver="arpack"
    ),
    "t-SNE embeedding": TSNE(
        n_components=2,
        n_iter=500,
        n_iter_without_progress=150,
        n_jobs=2,
        random_state=0,
    ),
    "NCA embedding": NeighborhoodComponentsAnalysis(
        n_components=2, init="pca", random_state=0
    ),
}

# %%
# Once we declared all the methodes of interest, we can run and perform the projection
# of the original data. We will store the projected data as well as the computational
# time needed to perform each projection.
from time import time

projections, timing = {}, {}
for name, transformer in embeddings.items():
    if name.startswith("Linear Discriminant Analysis"):
        data = X.copy()
        data.flat[:: X.shape[1] + 1] += 0.01  # Make X invertible
    else:
        data = X

    print(f"Computing {name}...")
    start_time = time()
    projections[name] = transformer.fit_transform(data, y)
    timing[name] = time() - start_time

# %%
# Finally, we can plot the resulting projection given by each method.
for name in timing:
    title = f"{name} (time {timing[name]:.3f}s)"
    plot_embedding(projections[name], title)

plt.show()
