# ruff: noqa
"""
=======================================
Release Highlights for scikit-learn 1.4
=======================================

.. currentmodule:: sklearn

We are pleased to announce the release of scikit-learn 1.4! 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_4>`.

To install the latest version (with pip)::

    pip install --upgrade scikit-learn

or with conda::

    conda install -c conda-forge scikit-learn

"""

# %%
# HistGradientBoosting Natively Supports Categorical DTypes in DataFrames
# -----------------------------------------------------------------------
# :class:`ensemble.HistGradientBoostingClassifier` and
# :class:`ensemble.HistGradientBoostingRegressor` now directly supports dataframes with
# categorical features.  Here we have a dataset with a mixture of
# categorical and numerical features:
from sklearn.datasets import fetch_openml

X_adult, y_adult = fetch_openml("adult", version=2, return_X_y=True)

# Remove redundant and non-feature columns
X_adult = X_adult.drop(["education-num", "fnlwgt"], axis="columns")
X_adult.dtypes

# %%
# By setting `categorical_features="from_dtype"`, the gradient boosting classifier
# treats the columns with categorical dtypes as categorical features in the
# algorithm:
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score

X_train, X_test, y_train, y_test = train_test_split(X_adult, y_adult, random_state=0)
hist = HistGradientBoostingClassifier(categorical_features="from_dtype")

hist.fit(X_train, y_train)
y_decision = hist.decision_function(X_test)
print(f"ROC AUC score is {roc_auc_score(y_test, y_decision)}")

# %%
# Polars output in `set_output`
# -----------------------------
# scikit-learn's transformers now support polars output with the `set_output` API.
import polars as pl
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer

df = pl.DataFrame(
    {"height": [120, 140, 150, 110, 100], "pet": ["dog", "cat", "dog", "cat", "cat"]}
)
preprocessor = ColumnTransformer(
    [
        ("numerical", StandardScaler(), ["height"]),
        ("categorical", OneHotEncoder(sparse_output=False), ["pet"]),
    ],
    verbose_feature_names_out=False,
)
preprocessor.set_output(transform="polars")

df_out = preprocessor.fit_transform(df)
df_out

# %%
print(f"Output type: {type(df_out)}")

# %%
# Missing value support for Random Forest
# ---------------------------------------
# The classes :class:`ensemble.RandomForestClassifier` and
# :class:`ensemble.RandomForestRegressor` now support missing values. When training
# every individual tree, the splitter evaluates each potential threshold with the
# missing values going to the left and right nodes. More details in the
# :ref:`User Guide <tree_missing_value_support>`.
import numpy as np
from sklearn.ensemble import RandomForestClassifier

X = np.array([0, 1, 6, np.nan]).reshape(-1, 1)
y = [0, 0, 1, 1]

forest = RandomForestClassifier(random_state=0).fit(X, y)
forest.predict(X)

# %%
# Add support for monotonic constraints in tree-based models
# ----------------------------------------------------------
# While we added support for monotonic constraints in histogram-based gradient boosting
# in scikit-learn 0.23, we now support this feature for all other tree-based models as
# trees, random forests, extra-trees, and exact gradient boosting. Here, we show this
# feature for random forest on a regression problem.
import matplotlib.pyplot as plt
from sklearn.inspection import PartialDependenceDisplay
from sklearn.ensemble import RandomForestRegressor

n_samples = 500
rng = np.random.RandomState(0)
X = rng.randn(n_samples, 2)
noise = rng.normal(loc=0.0, scale=0.01, size=n_samples)
y = 5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise

rf_no_cst = RandomForestRegressor().fit(X, y)
rf_cst = RandomForestRegressor(monotonic_cst=[1, 0]).fit(X, y)

disp = PartialDependenceDisplay.from_estimator(
    rf_no_cst,
    X,
    features=[0],
    feature_names=["feature 0"],
    line_kw={"linewidth": 4, "label": "unconstrained", "color": "tab:blue"},
)
PartialDependenceDisplay.from_estimator(
    rf_cst,
    X,
    features=[0],
    line_kw={"linewidth": 4, "label": "constrained", "color": "tab:orange"},
    ax=disp.axes_,
)
disp.axes_[0, 0].plot(
    X[:, 0], y, "o", alpha=0.5, zorder=-1, label="samples", color="tab:green"
)
disp.axes_[0, 0].set_ylim(-3, 3)
disp.axes_[0, 0].set_xlim(-1, 1)
disp.axes_[0, 0].legend()
plt.show()

# %%
# Enriched estimator displays
# ---------------------------
# Estimators displays have been enriched: if we look at `forest`, defined above:
forest

# %%
# One can access the documentation of the estimator by clicking on the icon "?" on
# the top right corner of the diagram.
#
# In addition, the display changes color, from orange to blue, when the estimator is
# fitted. You can also get this information by hovering on the icon "i".
from sklearn.base import clone

clone(forest)  # the clone is not fitted

# %%
# Metadata Routing Support
# ------------------------
# Many meta-estimators and cross-validation routines now support metadata
# routing, which are listed in the :ref:`user guide
# <metadata_routing_models>`. For instance, this is how you can do a nested
# cross-validation with sample weights and :class:`~model_selection.GroupKFold`:
import sklearn
from sklearn.metrics import get_scorer
from sklearn.datasets import make_regression
from sklearn.linear_model import Lasso
from sklearn.model_selection import GridSearchCV, cross_validate, GroupKFold

# For now by default metadata routing is disabled, and need to be explicitly
# enabled.
sklearn.set_config(enable_metadata_routing=True)

n_samples = 100
X, y = make_regression(n_samples=n_samples, n_features=5, noise=0.5)
rng = np.random.RandomState(7)
groups = rng.randint(0, 10, size=n_samples)
sample_weights = rng.rand(n_samples)
estimator = Lasso().set_fit_request(sample_weight=True)
hyperparameter_grid = {"alpha": [0.1, 0.5, 1.0, 2.0]}
scoring_inner_cv = get_scorer("neg_mean_squared_error").set_score_request(
    sample_weight=True
)
inner_cv = GroupKFold(n_splits=5)

grid_search = GridSearchCV(
    estimator=estimator,
    param_grid=hyperparameter_grid,
    cv=inner_cv,
    scoring=scoring_inner_cv,
)

outer_cv = GroupKFold(n_splits=5)
scorers = {
    "mse": get_scorer("neg_mean_squared_error").set_score_request(sample_weight=True)
}
results = cross_validate(
    grid_search,
    X,
    y,
    cv=outer_cv,
    scoring=scorers,
    return_estimator=True,
    params={"sample_weight": sample_weights, "groups": groups},
)
print("cv error on test sets:", results["test_mse"])

# Setting the flag to the default `False` to avoid interference with other
# scripts.
sklearn.set_config(enable_metadata_routing=False)

# %%
# Improved memory and runtime efficiency for PCA on sparse data
# -------------------------------------------------------------
# PCA is now able to handle sparse matrices natively for the `arpack`
# solver by levaraging `scipy.sparse.linalg.LinearOperator` to avoid
# materializing large sparse matrices when performing the
# eigenvalue decomposition of the data set covariance matrix.
#
from sklearn.decomposition import PCA
import scipy.sparse as sp
from time import time

X_sparse = sp.random(m=1000, n=1000, random_state=0)
X_dense = X_sparse.toarray()

t0 = time()
PCA(n_components=10, svd_solver="arpack").fit(X_sparse)
time_sparse = time() - t0

t0 = time()
PCA(n_components=10, svd_solver="arpack").fit(X_dense)
time_dense = time() - t0

print(f"Speedup: {time_dense / time_sparse:.1f}x")