"""
========================================
Release Highlights for scikit-learn 0.22
========================================

.. currentmodule:: sklearn

We are pleased to announce the release of scikit-learn 0.22, which comes
with many bug fixes and new 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 <changes_0_22>`.

To install the latest version (with pip)::

    pip install --upgrade scikit-learn

or with conda::

    conda install -c conda-forge scikit-learn
"""

# %%
# New plotting API
# ----------------
#
# A new plotting API is available for creating visualizations. This new API
# allows for quickly adjusting the visuals of a plot without involving any
# recomputation. It is also possible to add different plots to the same
# figure. The following example illustrates :class:`~metrics.plot_roc_curve`,
# but other plots utilities are supported like
# :class:`~inspection.plot_partial_dependence`,
# :class:`~metrics.plot_precision_recall_curve`, and
# :class:`~metrics.plot_confusion_matrix`. Read more about this new API in the
# :ref:`User Guide <visualizations>`.

from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import plot_roc_curve
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
import matplotlib.pyplot as plt

X, y = make_classification(random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

svc = SVC(random_state=42)
svc.fit(X_train, y_train)
rfc = RandomForestClassifier(random_state=42)
rfc.fit(X_train, y_train)

svc_disp = plot_roc_curve(svc, X_test, y_test)
rfc_disp = plot_roc_curve(rfc, X_test, y_test, ax=svc_disp.ax_)
rfc_disp.figure_.suptitle("ROC curve comparison")

plt.show()

# %%
# Stacking Classifier and Regressor
# ---------------------------------
# :class:`~ensemble.StackingClassifier` and
# :class:`~ensemble.StackingRegressor`
# allow you to have a stack of estimators with a final classifier or
# a regressor.
# Stacked generalization consists in stacking the output of individual
# estimators and use a classifier to compute the final prediction. Stacking
# allows to use the strength of each individual estimator by using their output
# as input of a final estimator.
# Base estimators are fitted on the full ``X`` while
# the final estimator is trained using cross-validated predictions of the
# base estimators using ``cross_val_predict``.
#
# Read more in the :ref:`User Guide <stacking>`.

from sklearn.datasets import load_iris
from sklearn.svm import LinearSVC
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.ensemble import StackingClassifier
from sklearn.model_selection import train_test_split

X, y = load_iris(return_X_y=True)
estimators = [
    ('rf', RandomForestClassifier(n_estimators=10, random_state=42)),
    ('svr', make_pipeline(StandardScaler(),
                          LinearSVC(random_state=42)))
]
clf = StackingClassifier(
    estimators=estimators, final_estimator=LogisticRegression()
)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, stratify=y, random_state=42
)
clf.fit(X_train, y_train).score(X_test, y_test)

# %%
# Permutation-based feature importance
# ------------------------------------
#
# The :func:`inspection.permutation_importance` can be used to get an
# estimate of the importance of each feature, for any fitted estimator:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.inspection import permutation_importance

X, y = make_classification(random_state=0, n_features=5, n_informative=3)
feature_names = np.array([f'x_{i}' for i in range(X.shape[1])])

rf = RandomForestClassifier(random_state=0).fit(X, y)
result = permutation_importance(rf, X, y, n_repeats=10, random_state=0,
fig, ax = plt.subplots()
sorted_idx = result.importances_mean.argsort()
ax.boxplot(result.importances[sorted_idx].T,
           vert=False, labels=feature_names[sorted_idx])
ax.set_title("Permutation Importance of each feature")
ax.set_ylabel("Features")
fig.tight_layout()
plt.show()

# %%
# Native support for missing values for gradient boosting
# -------------------------------------------------------
#
# The :class:`ensemble.HistGradientBoostingClassifier`
# and :class:`ensemble.HistGradientBoostingRegressor` now have native
# support for missing values (NaNs). This means that there is no need for
# imputing data when training or predicting.

from sklearn.ensemble import HistGradientBoostingClassifier

X = np.array([0, 1, 2, np.nan]).reshape(-1, 1)
y = [0, 0, 1, 1]

gbdt = HistGradientBoostingClassifier(min_samples_leaf=1).fit(X, y)
print(gbdt.predict(X))

# %%
# Precomputed sparse nearest neighbors graph
# ------------------------------------------
# Most estimators based on nearest neighbors graphs now accept precomputed
# sparse graphs as input, to reuse the same graph for multiple estimator fits.
# To use this feature in a pipeline, one can use the `memory` parameter, along
# with one of the two new transformers,
# :class:`neighbors.KNeighborsTransformer` and
# :class:`neighbors.RadiusNeighborsTransformer`. The precomputation
# can also be performed by custom estimators to use alternative
# implementations, such as approximate nearest neighbors methods.
# See more details in the :ref:`User Guide <neighbors_transformer>`.

from tempfile import TemporaryDirectory
from sklearn.neighbors import KNeighborsTransformer
from sklearn.manifold import Isomap
from sklearn.pipeline import make_pipeline

X, y = make_classification(random_state=0)

with TemporaryDirectory(prefix="sklearn_cache_") as tmpdir:
    estimator = make_pipeline(
        KNeighborsTransformer(n_neighbors=10, mode='distance'),
        Isomap(n_neighbors=10, metric='precomputed'),
        memory=tmpdir)
    estimator.fit(X)

    # We can decrease the number of neighbors and the graph will not be
    # recomputed.
    estimator.set_params(isomap__n_neighbors=5)
    estimator.fit(X)

# %%
# KNN Based Imputation
# ------------------------------------
# We now support imputation for completing missing values using k-Nearest
# Neighbors.
#
# Each sample's missing values are imputed using the mean value from
# ``n_neighbors`` nearest neighbors found in the training set. Two samples are
# close if the features that neither is missing are close.
# By default, a euclidean distance metric
# that supports missing values,
# :func:`~metrics.nan_euclidean_distances`, is used to find the nearest
# neighbors.
#
# Read more in the :ref:`User Guide <knnimpute>`.

from sklearn.impute import KNNImputer

X = [[1, 2, np.nan], [3, 4, 3], [np.nan, 6, 5], [8, 8, 7]]
imputer = KNNImputer(n_neighbors=2)
print(imputer.fit_transform(X))

# %%
# Tree pruning
# ------------
#
# It is now possible to prune most tree-based estimators once the trees are
# built. The pruning is based on minimal cost-complexity. Read more in the
# :ref:`User Guide <minimal_cost_complexity_pruning>` for details.

X, y = make_classification(random_state=0)

rf = RandomForestClassifier(random_state=0, ccp_alpha=0).fit(X, y)
print("Average number of nodes without pruning {:.1f}".format(
    np.mean([e.tree_.node_count for e in rf.estimators_])))

rf = RandomForestClassifier(random_state=0, ccp_alpha=0.05).fit(X, y)
print("Average number of nodes with pruning {:.1f}".format(
    np.mean([e.tree_.node_count for e in rf.estimators_])))

# %%
# Retrieve dataframes from OpenML
# -------------------------------
# :func:`datasets.fetch_openml` can now return pandas dataframe and thus
# properly handle datasets with heterogeneous data:

from sklearn.datasets import fetch_openml

titanic = fetch_openml('titanic', version=1, as_frame=True)
print(titanic.data.head()[['pclass', 'embarked']])

# %%
# Checking scikit-learn compatibility of an estimator
# ---------------------------------------------------
# Developers can check the compatibility of their scikit-learn compatible
# estimators using :func:`~utils.estimator_checks.check_estimator`. For
# instance, the ``check_estimator(LinearSVC())`` passes.
#
# We now provide a ``pytest`` specific decorator which allows ``pytest``
# to run all checks independently and report the checks that are failing.
#
# ..note::
#   This entry was slightly updated in version 0.24, where passing classes
#   isn't supported anymore: pass instances instead.

from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.utils.estimator_checks import parametrize_with_checks


@parametrize_with_checks([LogisticRegression(), DecisionTreeRegressor()])
def test_sklearn_compatible_estimator(estimator, check):
    check(estimator)

# %%
# ROC AUC now supports multiclass classification
# ----------------------------------------------
# The :func:`roc_auc_score` function can also be used in multi-class
# classification. Two averaging strategies are currently supported: the
# one-vs-one algorithm computes the average of the pairwise ROC AUC scores, and
# the one-vs-rest algorithm computes the average of the ROC AUC scores for each
# class against all other classes. In both cases, the multiclass ROC AUC scores
# are computed from the probability estimates that a sample belongs to a
# particular class according to the model. The OvO and OvR algorithms support
# weighting uniformly (``average='macro'``) and weighting by the prevalence
# (``average='weighted'``).
#
# Read more in the :ref:`User Guide <roc_metrics>`.


from sklearn.datasets import make_classification
from sklearn.svm import SVC
from sklearn.metrics import roc_auc_score

X, y = make_classification(n_classes=4, n_informative=16)
clf = SVC(decision_function_shape='ovo', probability=True).fit(X, y)
print(roc_auc_score(y, clf.predict_proba(X), multi_class='ovo'))
                                n_jobs=-1)
