"""
Decoding with FREM: face vs house vs chair object recognition
=============================================================

This example uses fast ensembling of regularized models (FREM) to decode
a face vs house vs chair discrimination task
from :footcite:t:`Haxby2001` study.
:term:`FREM` uses an implicit spatial regularization
through fast clustering and aggregates a high number of estimators
trained on various splits of the training set,
thus returning a very robust decoder
at a lower computational cost than other spatially regularized methods.

To have more details, see: :ref:`frem`.
"""

# %%
# Load the Haxby dataset
# ----------------------
from nilearn.datasets import fetch_haxby

data_files = fetch_haxby()

# %%
# Load behavioral data
import pandas as pd

behavioral = pd.read_csv(data_files.session_target[0], sep=" ")

# %%
# Restrict to face, house, and chair conditions
conditions = behavioral["labels"]
condition_mask = conditions.isin(["face", "house", "chair"])

# %%
# Split data into train and test samples, using the chunks
condition_mask_train = (condition_mask) & (behavioral["chunks"] <= 6)
condition_mask_test = (condition_mask) & (behavioral["chunks"] > 6)

# %%
# Apply this sample mask to X (fMRI data) and y (behavioral labels)
# Because the data is in one single large 4D image, we need to use
# index_img to do the split easily
from nilearn.image import index_img

func_filenames = data_files.func[0]
X_train = index_img(func_filenames, condition_mask_train)
X_test = index_img(func_filenames, condition_mask_test)
y_train = conditions[condition_mask_train].to_numpy()
y_test = conditions[condition_mask_test].to_numpy()


# %%
# Compute the mean EPI to be used for the background of the plotting
from nilearn.image import mean_img

background_img = mean_img(func_filenames)

# %%
# Fit FREM
# --------
from nilearn.decoding import FREMClassifier

# %%
# Restrict analysis to within the brain mask
mask = data_files.mask

decoder = FREMClassifier(mask=mask, cv=10, n_jobs=2, verbose=1)

# %%
# Fit model on train data and predict on test data
decoder.fit(X_train, y_train)
y_pred = decoder.predict(X_test)
accuracy = (y_pred == y_test).mean() * 100.0
print(f"FREM classification accuracy : {accuracy:g}%")

# %%
# Plot confusion matrix
# ------------------------------------

import numpy as np
from sklearn.metrics import confusion_matrix

from nilearn.plotting import plot_matrix, plot_stat_map, show

# %%
# Calculate the confusion matrix
matrix = confusion_matrix(
    y_test,
    y_pred,
    normalize="true",
)

# %%
# Plot the confusion matrix
im = plot_matrix(
    matrix,
    labels=sorted(np.unique(y_test)),
    vmin=0,
    cmap="inferno",
)

# Add x/y-axis labels
ax = im.axes
ax.set_ylabel("True label")
ax.set_xlabel("Predicted label")

show()

# %%
# Visualization of :term:`FREM` weights
# -------------------------------------
plot_stat_map(
    decoder.coef_img_["face"],
    background_img,
    title=f"FREM: accuracy {accuracy:g}%, 'face coefs'",
    cut_coords=(-50, -4),
    display_mode="yz",
)
show()
# %%
# :term:`FREM` ensembling procedure
# yields an important improvement of decoding
# accuracy on this simple example compared to fitting only one model per
# fold and the clustering mechanism keeps its computational cost reasonable
# even on heavier examples. Here we ensembled several instances of l2-SVC,
# but FREMClassifier also works with ridge or logistic.
# FREMRegressor object is also available to solve regression problems.

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


# sphinx_gallery_dummy_images=1