"""
=============================================
A demo of the Spectral Biclustering algorithm
=============================================

This example demonstrates how to generate a checkerboard dataset and bicluster
it using the :class:`~sklearn.cluster.SpectralBiclustering` algorithm. The
spectral biclustering algorithm is specifically designed to cluster data by
simultaneously considering both the rows (samples) and columns (features) of a
matrix. It aims to identify patterns not only between samples but also within
subsets of samples, allowing for the detection of localized structure within the
data. This makes spectral biclustering particularly well-suited for datasets
where the order or arrangement of features is fixed, such as in images, time
series, or genomes.

The data is generated, then shuffled and passed to the spectral biclustering
algorithm. The rows and columns of the shuffled matrix are then rearranged to
plot the biclusters found.
"""

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

# %%
# Generate sample data
# --------------------
# We generate the sample data using the
# :func:`~sklearn.datasets.make_checkerboard` function. Each pixel within
# `shape=(300, 300)` represents with its color a value from a uniform
# distribution. The noise is added from a normal distribution, where the value
# chosen for `noise` is the standard deviation.
#
# As you can see, the data is distributed over 12 cluster cells and is
# relatively well distinguishable.
from matplotlib import pyplot as plt

from sklearn.datasets import make_checkerboard

n_clusters = (4, 3)
data, rows, columns = make_checkerboard(
    shape=(300, 300), n_clusters=n_clusters, noise=10, shuffle=False, random_state=42
)

plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Original dataset")
plt.show()

# %%
# We shuffle the data and the goal is to reconstruct it afterwards using
# :class:`~sklearn.cluster.SpectralBiclustering`.
import numpy as np

# Creating lists of shuffled row and column indices
rng = np.random.RandomState(0)
row_idx_shuffled = rng.permutation(data.shape[0])
col_idx_shuffled = rng.permutation(data.shape[1])

# %%
# We redefine the shuffled data and plot it. We observe that we lost the
# structure of original data matrix.
data = data[row_idx_shuffled][:, col_idx_shuffled]

plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Shuffled dataset")
plt.show()

# %%
# Fitting `SpectralBiclustering`
# ------------------------------
# We fit the model and compare the obtained clusters with the ground truth. Note
# that when creating the model we specify the same number of clusters that we
# used to create the dataset (`n_clusters = (4, 3)`), which will contribute to
# obtain a good result.
from sklearn.cluster import SpectralBiclustering
from sklearn.metrics import consensus_score

model = SpectralBiclustering(n_clusters=n_clusters, method="log", random_state=0)
model.fit(data)

# Compute the similarity of two sets of biclusters
score = consensus_score(
    model.biclusters_, (rows[:, row_idx_shuffled], columns[:, col_idx_shuffled])
)
print(f"consensus score: {score:.1f}")

# %%
# The score is between 0 and 1, where 1 corresponds to a perfect matching. It
# shows the quality of the biclustering.

# %%
# Plotting results
# ----------------
# Now, we rearrange the data based on the row and column labels assigned by the
# :class:`~sklearn.cluster.SpectralBiclustering` model in ascending order and
# plot again. The `row_labels_` range from 0 to 3, while the `column_labels_`
# range from 0 to 2, representing a total of 4 clusters per row and 3 clusters
# per column.

# Reordering first the rows and then the columns.
reordered_rows = data[np.argsort(model.row_labels_)]
reordered_data = reordered_rows[:, np.argsort(model.column_labels_)]

plt.matshow(reordered_data, cmap=plt.cm.Blues)
plt.title("After biclustering; rearranged to show biclusters")
plt.show()

# %%
# As a last step, we want to demonstrate the relationships between the row
# and column labels assigned by the model. Therefore, we create a grid with
# :func:`numpy.outer`, which takes the sorted `row_labels_` and `column_labels_`
# and adds 1 to each to ensure that the labels start from 1 instead of 0 for
# better visualization.
plt.matshow(
    np.outer(np.sort(model.row_labels_) + 1, np.sort(model.column_labels_) + 1),
    cmap=plt.cm.Blues,
)
plt.title("Checkerboard structure of rearranged data")
plt.show()

# %%
# The outer product of the row and column label vectors shows a representation
# of the checkerboard structure, where different combinations of row and column
# labels are represented by different shades of blue.

"""
=============================================
A demo of the Spectral Biclustering algorithm
=============================================

This example demonstrates how to generate a checkerboard dataset and bicluster
it using the :class:`~sklearn.cluster.SpectralBiclustering` algorithm. The
spectral biclustering algorithm is specifically designed to cluster data by
simultaneously considering both the rows (samples) and columns (features) of a
matrix. It aims to identify patterns not only between samples but also within
subsets of samples, allowing for the detection of localized structure within the
data. This makes spectral biclustering particularly well-suited for datasets
where the order or arrangement of features is fixed, such as in images, time
series, or genomes.

The data is generated, then shuffled and passed to the spectral biclustering
algorithm. The rows and columns of the shuffled matrix are then rearranged to
plot the biclusters found.
"""

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

# %%
# Generate sample data
# --------------------
# We generate the sample data using the
# :func:`~sklearn.datasets.make_checkerboard` function. Each pixel within
# `shape=(300, 300)` represents with its color a value from a uniform
# distribution. The noise is added from a normal distribution, where the value
# chosen for `noise` is the standard deviation.
#
# As you can see, the data is distributed over 12 cluster cells and is
# relatively well distinguishable.
from matplotlib import pyplot as plt

from sklearn.datasets import make_checkerboard

n_clusters = (4, 3)
data, rows, columns = make_checkerboard(
    shape=(300, 300), n_clusters=n_clusters, noise=10, shuffle=False, random_state=42
)

plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Original dataset")
_ = plt.show()

# %%
# We shuffle the data and the goal is to reconstruct it afterwards using
# :class:`~sklearn.cluster.SpectralBiclustering`.
import numpy as np

# Creating lists of shuffled row and column indices
rng = np.random.RandomState(0)
row_idx_shuffled = rng.permutation(data.shape[0])
col_idx_shuffled = rng.permutation(data.shape[1])

# %%
# We redefine the shuffled data and plot it. We observe that we lost the
# structure of original data matrix.
data = data[row_idx_shuffled][:, col_idx_shuffled]

plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Shuffled dataset")
_ = plt.show()

# %%
# Fitting `SpectralBiclustering`
# ------------------------------
# We fit the model and compare the obtained clusters with the ground truth. Note
# that when creating the model we specify the same number of clusters that we
# used to create the dataset (`n_clusters = (4, 3)`), which will contribute to
# obtain a good result.
from sklearn.cluster import SpectralBiclustering
from sklearn.metrics import consensus_score

model = SpectralBiclustering(n_clusters=n_clusters, method="log", random_state=0)
model.fit(data)

# Compute the similarity of two sets of biclusters
score = consensus_score(
    model.biclusters_, (rows[:, row_idx_shuffled], columns[:, col_idx_shuffled])
)
print(f"consensus score: {score:.1f}")

# %%
# The score is between 0 and 1, where 1 corresponds to a perfect matching. It
# shows the quality of the biclustering.

# %%
# Plotting results
# ----------------
# Now, we rearrange the data based on the row and column labels assigned by the
# :class:`~sklearn.cluster.SpectralBiclustering` model in ascending order and
# plot again. The `row_labels_` range from 0 to 3, while the `column_labels_`
# range from 0 to 2, representing a total of 4 clusters per row and 3 clusters
# per column.

# Reordering first the rows and then the columns.
reordered_rows = data[np.argsort(model.row_labels_)]
reordered_data = reordered_rows[:, np.argsort(model.column_labels_)]

plt.matshow(reordered_data, cmap=plt.cm.Blues)
plt.title("After biclustering; rearranged to show biclusters")
_ = plt.show()

# %%
# As a last step, we want to demonstrate the relationships between the row
# and column labels assigned by the model. Therefore, we create a grid with
# :func:`numpy.outer`, which takes the sorted `row_labels_` and `column_labels_`
# and adds 1 to each to ensure that the labels start from 1 instead of 0 for
# better visualization.
plt.matshow(
    np.outer(np.sort(model.row_labels_) + 1, np.sort(model.column_labels_) + 1),
    cmap=plt.cm.Blues,
)
plt.title("Checkerboard structure of rearranged data")
plt.show()

# %%
# The outer product of the row and column label vectors shows a representation
# of the checkerboard structure, where different combinations of row and column
# labels are represented by different shades of blue.
