"""
================================
Image denoising using kernel PCA
================================

This example shows how to use :class:`~sklearn.decomposition.KernelPCA` to
denoise images. In short, we take advantage of the approximation function
learned during `fit` to reconstruct the original image.

We will compare the results with an exact reconstruction using
:class:`~sklearn.decomposition.PCA`.

We will use USPS digits dataset to reproduce presented in Sect. 4 of [1]_.

.. topic:: References

   .. [1] `Bakır, Gökhan H., Jason Weston, and Bernhard Schölkopf.
      "Learning to find pre-images."
      Advances in neural information processing systems 16 (2004): 449-456.
      <https://papers.nips.cc/paper/2003/file/ac1ad983e08ad3304a97e147f522747e-Paper.pdf>`_

"""

# Authors: Guillaume Lemaitre <guillaume.lemaitre@inria.fr>
# Licence: BSD 3 clause

# %%
# Load the dataset via OpenML
# ---------------------------
#
# The USPS digits datasets is available in OpenML. We use
# :func:`~sklearn.datasets.fetch_openml` to get this dataset. In addition, we
# normalize the dataset such that all pixel values are in the range (0, 1).
import numpy as np

from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler

X, y = fetch_openml(data_id=41082, as_frame=False, return_X_y=True, parser="pandas")
X = MinMaxScaler().fit_transform(X)

# %%
# The idea will be to learn a PCA basis (with and without a kernel) on
# noisy images and then use these models to reconstruct and denoise these
# images.
#
# Thus, we split our dataset into a training and testing set composed of 1,000
# samples for the training and 100 samples for testing. These images are
# noise-free and we will use them to evaluate the efficiency of the denoising
# approaches. In addition, we create a copy of the original dataset and add a
# Gaussian noise.
#
# The idea of this application, is to show that we can denoise corrupted images
# by learning a PCA basis on some uncorrupted images. We will use both a PCA
# and a kernel-based PCA to solve this problem.
X_train, X_test, y_train, y_test = train_test_split(
    X, y, stratify=y, random_state=0, train_size=1_000, test_size=100
)

rng = np.random.RandomState(0)
noise = rng.normal(scale=0.25, size=X_test.shape)
X_test_noisy = X_test + noise

noise = rng.normal(scale=0.25, size=X_train.shape)
X_train_noisy = X_train + noise

# %%
# In addition, we will create a helper function to qualitatively assess the
# image reconstruction by plotting the test images.
import matplotlib.pyplot as plt


def plot_digits(X, title):
    """Small helper function to plot 100 digits."""
    fig, axs = plt.subplots(nrows=10, ncols=10, figsize=(8, 8))
    for img, ax in zip(X, axs.ravel()):
        ax.imshow(img.reshape((16, 16)), cmap="Greys")
        ax.axis("off")
    fig.suptitle(title, fontsize=24)


# %%
# In addition, we will use the mean squared error (MSE) to quantitatively
# assess the image reconstruction.
#
# Let's first have a look to see the difference between noise-free and noisy
# images. We will check the test set in this regard.
plot_digits(X_test, "Uncorrupted test images")
plot_digits(
    X_test_noisy, f"Noisy test images\nMSE: {np.mean((X_test - X_test_noisy) ** 2):.2f}"
)

# %%
# Learn the `PCA` basis
# ---------------------
#
# We can now learn our PCA basis using both a linear PCA and a kernel PCA that
# uses a radial basis function (RBF) kernel.
from sklearn.decomposition import PCA, KernelPCA

pca = PCA(n_components=32, random_state=42)
kernel_pca = KernelPCA(
    n_components=400,
    kernel="rbf",
    gamma=1e-3,
    fit_inverse_transform=True,
    alpha=5e-3,
    random_state=42,
)

pca.fit(X_train_noisy)
_ = kernel_pca.fit(X_train_noisy)

# %%
# Reconstruct and denoise test images
# -----------------------------------
#
# Now, we can transform and reconstruct the noisy test set. Since we used less
# components than the number of original features, we will get an approximation
# of the original set. Indeed, by dropping the components explaining variance
# in PCA the least, we hope to remove noise. Similar thinking happens in kernel
# PCA; however, we expect a better reconstruction because we use a non-linear
# kernel to learn the PCA basis and a kernel ridge to learn the mapping
# function.
X_reconstructed_kernel_pca = kernel_pca.inverse_transform(
    kernel_pca.transform(X_test_noisy)
)
X_reconstructed_pca = pca.inverse_transform(pca.transform(X_test_noisy))

# %%
plot_digits(X_test, "Uncorrupted test images")
plot_digits(
    X_reconstructed_pca,
    f"PCA reconstruction\nMSE: {np.mean((X_test - X_reconstructed_pca) ** 2):.2f}",
)
plot_digits(
    X_reconstructed_kernel_pca,
    (
        "Kernel PCA reconstruction\n"
        f"MSE: {np.mean((X_test - X_reconstructed_kernel_pca) ** 2):.2f}"
    ),
)

# %%
# PCA has a lower MSE than kernel PCA. However, the qualitative analysis might
# not favor PCA instead of kernel PCA. We observe that kernel PCA is able to
# remove background noise and provide a smoother image.
#
# However, it should be noted that the results of the denoising with kernel PCA
# will depend of the parameters `n_components`, `gamma`, and `alpha`.
