"""
Successive Halving Iterations
=============================

This example illustrates how a successive halving search
(:class:`~sklearn.model_selection.HalvingGridSearchCV` and
:class:`~sklearn.model_selection.HalvingRandomSearchCV`)
iteratively chooses the best parameter combination out of
multiple candidates.

"""

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

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.stats import randint

from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
from sklearn.experimental import enable_halving_search_cv  # noqa: F401
from sklearn.model_selection import HalvingRandomSearchCV

# %%
# We first define the parameter space and train a
# :class:`~sklearn.model_selection.HalvingRandomSearchCV` instance.

rng = np.random.RandomState(0)

X, y = datasets.make_classification(n_samples=400, n_features=12, random_state=rng)

clf = RandomForestClassifier(n_estimators=20, random_state=rng)

param_dist = {
    "max_depth": [3, None],
    "max_features": randint(1, 6),
    "min_samples_split": randint(2, 11),
    "bootstrap": [True, False],
    "criterion": ["gini", "entropy"],
}

rsh = HalvingRandomSearchCV(
    estimator=clf, param_distributions=param_dist, factor=2, random_state=rng
)
rsh.fit(X, y)

# %%
# We can now use the `cv_results_` attribute of the search estimator to inspect
# and plot the evolution of the search.

results = pd.DataFrame(rsh.cv_results_)
results["params_str"] = results.params.apply(str)
results.drop_duplicates(subset=("params_str", "iter"), inplace=True)
mean_scores = results.pivot(
    index="iter", columns="params_str", values="mean_test_score"
)
ax = mean_scores.plot(legend=False, alpha=0.6)

labels = [
    f"iter={i}\nn_samples={rsh.n_resources_[i]}\nn_candidates={rsh.n_candidates_[i]}"
    for i in range(rsh.n_iterations_)
]

ax.set_xticks(range(rsh.n_iterations_))
ax.set_xticklabels(labels, rotation=45, multialignment="left")
ax.set_title("Scores of candidates over iterations")
ax.set_ylabel("mean test score", fontsize=15)
ax.set_xlabel("iterations", fontsize=15)
plt.tight_layout()
plt.show()

# %%
# Number of candidates and amount of resource at each iteration
# -------------------------------------------------------------
#
# At the first iteration, a small amount of resources is used. The resource
# here is the number of samples that the estimators are trained on. All
# candidates are evaluated.
#
# At the second iteration, only the best half of the candidates is evaluated.
# The number of allocated resources is doubled: candidates are evaluated on
# twice as many samples.
#
# This process is repeated until the last iteration, where only 2 candidates
# are left. The best candidate is the candidate that has the best score at the
# last iteration.

"""
Successive Halving Iterations
=============================

This example illustrates how a successive halving search
(:class:`~sklearn.model_selection.HalvingGridSearchCV` and
:class:`~sklearn.model_selection.HalvingRandomSearchCV`)
iteratively chooses the best parameter combination out of
multiple candidates.

"""

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

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.stats import randint

from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
from sklearn.experimental import enable_halving_search_cv  # noqa
from sklearn.model_selection import HalvingRandomSearchCV

# %%
# We first define the parameter space and train a
# :class:`~sklearn.model_selection.HalvingRandomSearchCV` instance.

rng = np.random.RandomState(0)

X, y = datasets.make_classification(n_samples=400, n_features=12, random_state=rng)

clf = RandomForestClassifier(n_estimators=20, random_state=rng)

param_dist = {
    "max_depth": [3, None],
    "max_features": randint(1, 6),
    "min_samples_split": randint(2, 11),
    "bootstrap": [True, False],
    "criterion": ["gini", "entropy"],
}

rsh = HalvingRandomSearchCV(
    estimator=clf, param_distributions=param_dist, factor=2, random_state=rng
)
rsh.fit(X, y)

# %%
# We can now use the `cv_results_` attribute of the search estimator to inspect
# and plot the evolution of the search.

results = pd.DataFrame(rsh.cv_results_)
results["params_str"] = results.params.apply(str)
results.drop_duplicates(subset=("params_str", "iter"), inplace=True)
mean_scores = results.pivot(
    index="iter", columns="params_str", values="mean_test_score"
)
ax = mean_scores.plot(legend=False, alpha=0.6)

labels = [
    f"iter={i}\nn_samples={rsh.n_resources_[i]}\nn_candidates={rsh.n_candidates_[i]}"
    for i in range(rsh.n_iterations_)
]

ax.set_xticks(range(rsh.n_iterations_))
ax.set_xticklabels(labels, rotation=45, multialignment="left")
ax.set_title("Scores of candidates over iterations")
ax.set_ylabel("mean test score", fontsize=15)
ax.set_xlabel("iterations", fontsize=15)
plt.tight_layout()
plt.show()

# %%
# Number of candidates and amount of resource at each iteration
# -------------------------------------------------------------
#
# At the first iteration, a small amount of resources is used. The resource
# here is the number of samples that the estimators are trained on. All
# candidates are evaluated.
#
# At the second iteration, only the best half of the candidates is evaluated.
# The number of allocated resources is doubled: candidates are evaluated on
# twice as many samples.
#
# This process is repeated until the last iteration, where only 2 candidates
# are left. The best candidate is the candidate that has the best score at the
# last iteration.
