# ruff: noqa
"""
=======================================
Release Highlights for scikit-learn 1.1
=======================================

.. currentmodule:: sklearn

We are pleased to announce the release of scikit-learn 1.1! Many bug fixes
and improvements were added, as well as some new key features. We detail
below a few of the major features of this release. **For an exhaustive list of
all the changes**, please refer to the :ref:`release notes <release_notes_1_1>`.

To install the latest version (with pip)::

    pip install --upgrade scikit-learn

or with conda::

    conda install -c conda-forge scikit-learn

"""

# %%
# .. _quantile_support_hgbdt:
#
# Quantile loss in :class:`~ensemble.HistGradientBoostingRegressor`
# -----------------------------------------------------------------
# :class:`~ensemble.HistGradientBoostingRegressor` can model quantiles with
# `loss="quantile"` and the new parameter `quantile`.
from sklearn.ensemble import HistGradientBoostingRegressor
import numpy as np
import matplotlib.pyplot as plt

# Simple regression function for X * cos(X)
rng = np.random.RandomState(42)
X_1d = np.linspace(0, 10, num=2000)
X = X_1d.reshape(-1, 1)
y = X_1d * np.cos(X_1d) + rng.normal(scale=X_1d / 3)

quantiles = [0.95, 0.5, 0.05]
parameters = dict(loss="quantile", max_bins=32, max_iter=50)
hist_quantiles = {
    f"quantile={quantile:.2f}": HistGradientBoostingRegressor(
        **parameters, quantile=quantile
    ).fit(X, y)
    for quantile in quantiles
}

fig, ax = plt.subplots()
ax.plot(X_1d, y, "o", alpha=0.5, markersize=1)
for quantile, hist in hist_quantiles.items():
    ax.plot(X_1d, hist.predict(X), label=quantile)
_ = ax.legend(loc="lower left")

# %%
# For a usecase example, see
# :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py`

# %%
# `get_feature_names_out` Available in all Transformers
# -----------------------------------------------------
# :term:`get_feature_names_out` is now available in all Transformers. This enables
# :class:`~pipeline.Pipeline` to construct the output feature names for more complex
# pipelines:
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.impute import SimpleImputer
from sklearn.feature_selection import SelectKBest
from sklearn.datasets import fetch_openml
from sklearn.linear_model import LogisticRegression

X, y = fetch_openml(
    "titanic", version=1, as_frame=True, return_X_y=True, parser="pandas"
)
numeric_features = ["age", "fare"]
numeric_transformer = make_pipeline(SimpleImputer(strategy="median"), StandardScaler())
categorical_features = ["embarked", "pclass"]

preprocessor = ColumnTransformer(
    [
        ("num", numeric_transformer, numeric_features),
        (
            "cat",
            OneHotEncoder(handle_unknown="ignore", sparse_output=False),
            categorical_features,
        ),
    ],
    verbose_feature_names_out=False,
)
log_reg = make_pipeline(preprocessor, SelectKBest(k=7), LogisticRegression())
log_reg.fit(X, y)


# %%
# Here we slice the pipeline to include all the steps but the last one. The output
# feature names of this pipeline slice are the features put into logistic
# regression. These names correspond directly to the coefficients in the logistic
# regression:
import pandas as pd

log_reg_input_features = log_reg[:-1].get_feature_names_out()
pd.Series(log_reg[-1].coef_.ravel(), index=log_reg_input_features).plot.bar()
plt.tight_layout()


# %%
# Grouping infrequent categories in :class:`~preprocessing.OneHotEncoder`
# -----------------------------------------------------------------------
# :class:`~preprocessing.OneHotEncoder` supports aggregating infrequent
# categories into a single output for each feature. The parameters to enable
# the gathering of infrequent categories are `min_frequency` and
# `max_categories`. See the :ref:`User Guide <encoder_infrequent_categories>`
# for more details.
from sklearn.preprocessing import OneHotEncoder
import numpy as np

X = np.array(
    [["dog"] * 5 + ["cat"] * 20 + ["rabbit"] * 10 + ["snake"] * 3], dtype=object
).T
enc = OneHotEncoder(min_frequency=6, sparse_output=False).fit(X)
enc.infrequent_categories_

# %%
# Since dog and snake are infrequent categories, they are grouped together when
# transformed:
encoded = enc.transform(np.array([["dog"], ["snake"], ["cat"], ["rabbit"]]))
pd.DataFrame(encoded, columns=enc.get_feature_names_out())

# %%
# Performance improvements
# ------------------------
# Reductions on pairwise distances for dense float64 datasets has been refactored
# to better take advantage of non-blocking thread parallelism. For example,
# :meth:`neighbors.NearestNeighbors.kneighbors` and
# :meth:`neighbors.NearestNeighbors.radius_neighbors` can respectively be up to ×20 and
# ×5 faster than previously. In summary, the following functions and estimators
# now benefit from improved performance:
#
# - :func:`metrics.pairwise_distances_argmin`
# - :func:`metrics.pairwise_distances_argmin_min`
# - :class:`cluster.AffinityPropagation`
# - :class:`cluster.Birch`
# - :class:`cluster.MeanShift`
# - :class:`cluster.OPTICS`
# - :class:`cluster.SpectralClustering`
# - :func:`feature_selection.mutual_info_regression`
# - :class:`neighbors.KNeighborsClassifier`
# - :class:`neighbors.KNeighborsRegressor`
# - :class:`neighbors.RadiusNeighborsClassifier`
# - :class:`neighbors.RadiusNeighborsRegressor`
# - :class:`neighbors.LocalOutlierFactor`
# - :class:`neighbors.NearestNeighbors`
# - :class:`manifold.Isomap`
# - :class:`manifold.LocallyLinearEmbedding`
# - :class:`manifold.TSNE`
# - :func:`manifold.trustworthiness`
# - :class:`semi_supervised.LabelPropagation`
# - :class:`semi_supervised.LabelSpreading`
#
# To know more about the technical details of this work, you can read
# `this suite of blog posts <https://blog.scikit-learn.org/technical/performances/>`_.
#
# Moreover, the computation of loss functions has been refactored using
# Cython resulting in performance improvements for the following estimators:
#
# - :class:`linear_model.LogisticRegression`
# - :class:`linear_model.GammaRegressor`
# - :class:`linear_model.PoissonRegressor`
# - :class:`linear_model.TweedieRegressor`

# %%
# :class:`~decomposition.MiniBatchNMF`: an online version of NMF
# --------------------------------------------------------------
# The new class :class:`~decomposition.MiniBatchNMF` implements a faster but
# less accurate version of non-negative matrix factorization
# (:class:`~decomposition.NMF`). :class:`~decomposition.MiniBatchNMF` divides the
# data into mini-batches and optimizes the NMF model in an online manner by
# cycling over the mini-batches, making it better suited for large datasets. In
# particular, it implements `partial_fit`, which can be used for online
# learning when the data is not readily available from the start, or when the
# data does not fit into memory.
import numpy as np
from sklearn.decomposition import MiniBatchNMF

rng = np.random.RandomState(0)
n_samples, n_features, n_components = 10, 10, 5
true_W = rng.uniform(size=(n_samples, n_components))
true_H = rng.uniform(size=(n_components, n_features))
X = true_W @ true_H

nmf = MiniBatchNMF(n_components=n_components, random_state=0)

for _ in range(10):
    nmf.partial_fit(X)

W = nmf.transform(X)
H = nmf.components_
X_reconstructed = W @ H

print(
    f"relative reconstruction error: ",
    f"{np.sum((X - X_reconstructed) ** 2) / np.sum(X**2):.5f}",
)

# %%
# :class:`~cluster.BisectingKMeans`: divide and cluster
# -----------------------------------------------------
# The new class :class:`~cluster.BisectingKMeans` is a variant of
# :class:`~cluster.KMeans`, using divisive hierarchical clustering. Instead of
# creating all centroids at once, centroids are picked progressively based on a
# previous clustering: a cluster is split into two new clusters repeatedly
# until the target number of clusters is reached, giving a hierarchical
# structure to the clustering.
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans, BisectingKMeans
import matplotlib.pyplot as plt

X, _ = make_blobs(n_samples=1000, centers=2, random_state=0)

km = KMeans(n_clusters=5, random_state=0, n_init="auto").fit(X)
bisect_km = BisectingKMeans(n_clusters=5, random_state=0).fit(X)

fig, ax = plt.subplots(1, 2, figsize=(10, 5))
ax[0].scatter(X[:, 0], X[:, 1], s=10, c=km.labels_)
ax[0].scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1], s=20, c="r")
ax[0].set_title("KMeans")

ax[1].scatter(X[:, 0], X[:, 1], s=10, c=bisect_km.labels_)
ax[1].scatter(
    bisect_km.cluster_centers_[:, 0], bisect_km.cluster_centers_[:, 1], s=20, c="r"
)
_ = ax[1].set_title("BisectingKMeans")