"""
===============================================
Feature transformations with ensembles of trees
===============================================

Transform your features into a higher dimensional, sparse space. Then train a
linear model on these features.

First fit an ensemble of trees (totally random trees, a random forest, or
gradient boosted trees) on the training set. Then each leaf of each tree in the
ensemble is assigned a fixed arbitrary feature index in a new feature space.
These leaf indices are then encoded in a one-hot fashion.

Each sample goes through the decisions of each tree of the ensemble and ends up
in one leaf per tree. The sample is encoded by setting feature values for these
leaves to 1 and the other feature values to 0.

The resulting transformer has then learned a supervised, sparse,
high-dimensional categorical embedding of the data.

"""

# Authors: The jax-sklearn developers
# SPDX-License-Identifier: BSD-3-Clause

# %%
# First, we will create a large dataset and split it into three sets:
#
# - a set to train the ensemble methods which are later used to as a feature
#   engineering transformer;
# - a set to train the linear model;
# - a set to test the linear model.
#
# It is important to split the data in such way to avoid overfitting by leaking
# data.

from xlearn.datasets import make_classification
from xlearn.model_selection import train_test_split

X, y = make_classification(n_samples=80_000, random_state=10)

X_full_train, X_test, y_full_train, y_test = train_test_split(
    X, y, test_size=0.5, random_state=10
)
X_train_ensemble, X_train_linear, y_train_ensemble, y_train_linear = train_test_split(
    X_full_train, y_full_train, test_size=0.5, random_state=10
)

# %%
# For each of the ensemble methods, we will use 10 estimators and a maximum
# depth of 3 levels.

n_estimators = 10
max_depth = 3

# %%
# First, we will start by training the random forest and gradient boosting on
# the separated training set

from xlearn.ensemble import GradientBoostingClassifier, RandomForestClassifier

random_forest = RandomForestClassifier(
    n_estimators=n_estimators, max_depth=max_depth, random_state=10
)
random_forest.fit(X_train_ensemble, y_train_ensemble)

gradient_boosting = GradientBoostingClassifier(
    n_estimators=n_estimators, max_depth=max_depth, random_state=10
)
_ = gradient_boosting.fit(X_train_ensemble, y_train_ensemble)

# %%
# Notice that :class:`~xlearn.ensemble.HistGradientBoostingClassifier` is much
# faster than :class:`~xlearn.ensemble.GradientBoostingClassifier` starting
# with intermediate datasets (`n_samples >= 10_000`), which is not the case of
# the present example.
#
# The :class:`~xlearn.ensemble.RandomTreesEmbedding` is an unsupervised method
# and thus does not required to be trained independently.

from xlearn.ensemble import RandomTreesEmbedding

random_tree_embedding = RandomTreesEmbedding(
    n_estimators=n_estimators, max_depth=max_depth, random_state=0
)

# %%
# Now, we will create three pipelines that will use the above embedding as
# a preprocessing stage.
#
# The random trees embedding can be directly pipelined with the logistic
# regression because it is a standard jax-sklearn transformer.

from xlearn.linear_model import LogisticRegression
from xlearn.pipeline import make_pipeline

rt_model = make_pipeline(random_tree_embedding, LogisticRegression(max_iter=1000))
rt_model.fit(X_train_linear, y_train_linear)

# %%
# Then, we can pipeline random forest or gradient boosting with a logistic
# regression. However, the feature transformation will happen by calling the
# method `apply`. The pipeline in jax-sklearn expects a call to `transform`.
# Therefore, we wrapped the call to `apply` within a `FunctionTransformer`.

from xlearn.preprocessing import FunctionTransformer, OneHotEncoder


def rf_apply(X, model):
    return model.apply(X)


rf_leaves_yielder = FunctionTransformer(rf_apply, kw_args={"model": random_forest})

rf_model = make_pipeline(
    rf_leaves_yielder,
    OneHotEncoder(handle_unknown="ignore"),
    LogisticRegression(max_iter=1000),
)
rf_model.fit(X_train_linear, y_train_linear)


# %%
def gbdt_apply(X, model):
    return model.apply(X)[:, :, 0]


gbdt_leaves_yielder = FunctionTransformer(
    gbdt_apply, kw_args={"model": gradient_boosting}
)

gbdt_model = make_pipeline(
    gbdt_leaves_yielder,
    OneHotEncoder(handle_unknown="ignore"),
    LogisticRegression(max_iter=1000),
)
gbdt_model.fit(X_train_linear, y_train_linear)

# %%
# We can finally show the different ROC curves for all the models.

import matplotlib.pyplot as plt

from xlearn.metrics import RocCurveDisplay

_, ax = plt.subplots()

models = [
    ("RT embedding -> LR", rt_model),
    ("RF", random_forest),
    ("RF embedding -> LR", rf_model),
    ("GBDT", gradient_boosting),
    ("GBDT embedding -> LR", gbdt_model),
]

model_displays = {}
for name, pipeline in models:
    model_displays[name] = RocCurveDisplay.from_estimator(
        pipeline, X_test, y_test, ax=ax, name=name
    )
_ = ax.set_title("ROC curve")

# %%
_, ax = plt.subplots()
for name, pipeline in models:
    model_displays[name].plot(ax=ax)

ax.set_xlim(0, 0.2)
ax.set_ylim(0.8, 1)
_ = ax.set_title("ROC curve (zoomed in at top left)")
