"""
Functional connectivity predicts age group
==========================================

This example compares different kinds of :term:`functional connectivity`
between regions of interest : correlation, partial correlation,
and tangent space embedding.

The resulting connectivity coefficients can be used to
discriminate children from adults. In general, the tangent space embedding
**outperforms** the standard correlations:
see :footcite:t:`Dadi2019` for a careful study.

.. include:: ../../../examples/masker_note.rst

"""

try:
    import matplotlib.pyplot as plt
except ImportError:
    raise RuntimeError("This script needs the matplotlib library")

# %%
# Load brain development :term:`fMRI` dataset and MSDL atlas
# ----------------------------------------------------------
# We study only 60 subjects from the dataset, to save computation time.
from nilearn.datasets import fetch_atlas_msdl, fetch_development_fmri

development_dataset = fetch_development_fmri(n_subjects=60)

# %%
# We use probabilistic regions of interest (ROIs) from the MSDL atlas.

from nilearn.maskers import NiftiMapsMasker

msdl_data = fetch_atlas_msdl()
msdl_coords = msdl_data.region_coords

masker = NiftiMapsMasker(
    msdl_data.maps,
    resampling_target="data",
    t_r=2,
    detrend=True,
    low_pass=0.1,
    high_pass=0.01,
    memory="nilearn_cache",
    memory_level=1,
    standardize="zscore_sample",
    standardize_confounds=True,
)

masked_data = list(
    map(
        masker.fit_transform,
        development_dataset.func,
        development_dataset.confounds,
    )
)

# %%
# What kind of connectivity is most powerful for classification?
# --------------------------------------------------------------
# we will use connectivity matrices as features to distinguish children from
# adults. We use cross-validation and measure classification accuracy to
# compare the different kinds of connectivity matrices.

# prepare the classification pipeline
from sklearn.dummy import DummyClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.svm import LinearSVC

from nilearn.connectome import ConnectivityMeasure

kinds = ["correlation", "partial correlation", "tangent"]

pipe = Pipeline(
    [
        (
            "connectivity",
            ConnectivityMeasure(
                vectorize=True,
                standardize="zscore_sample",
            ),
        ),
        (
            "classifier",
            GridSearchCV(LinearSVC(dual=True), {"C": [0.1, 1.0, 10.0]}, cv=5),
        ),
    ]
)

param_grid = [
    {"classifier": [DummyClassifier(strategy="most_frequent")]},
    {"connectivity__kind": kinds},
]

# %%
# We use random splits of the subjects into training/testing sets.
# StratifiedShuffleSplit allows preserving the proportion of children in the
# test set.
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.preprocessing import LabelEncoder

groups = development_dataset.phenotypic["Child_Adult"].to_list()
classes = LabelEncoder().fit_transform(groups)

cv = StratifiedShuffleSplit(n_splits=30, random_state=0, test_size=10)
gs = GridSearchCV(
    pipe,
    param_grid,
    scoring="accuracy",
    cv=cv,
    verbose=1,
    refit=False,
    n_jobs=2,
)
gs.fit(masked_data, classes)
mean_scores = gs.cv_results_["mean_test_score"]
scores_std = gs.cv_results_["std_test_score"]

# %%
# display the results
plt.figure(figsize=(6, 4), constrained_layout=True)

positions = [0.1, 0.2, 0.3, 0.4]
plt.barh(positions, mean_scores, align="center", height=0.05, xerr=scores_std)
yticks = ["dummy", *list(gs.cv_results_["param_connectivity__kind"].data[1:])]
yticks = [t.replace(" ", "\n") for t in yticks]
plt.yticks(positions, yticks)
plt.xlabel("Classification accuracy")
plt.gca().grid(True)
plt.gca().set_axisbelow(True)

# %%
# This is a small example to showcase nilearn features. In practice such
# comparisons need to be performed on much larger cohorts and several
# datasets.
# :footcite:t:`Dadi2019` showed
# that across many cohorts and clinical questions,
# the tangent kind should be preferred.

plt.show()

# %%
# References
# ----------
#
# .. footbibliography::
