"""
The haxby dataset: different multi-class strategies
===================================================

We compare one vs all and one vs one multi-class strategies: the overall
cross-validated accuracy and the confusion matrix.

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

"""

import numpy as np
import pandas as pd

from nilearn import datasets
from nilearn.plotting import plot_matrix, show

# %%
# Load the Haxby data dataset
# ---------------------------


# By default 2nd subject from haxby datasets will be fetched.
haxby_dataset = datasets.fetch_haxby()

# Print basic information on the dataset
print(f"Mask nifti images are located at: {haxby_dataset.mask}")
print(f"Functional nifti images are located at: {haxby_dataset.func[0]}")

func_filename = haxby_dataset.func[0]
mask_filename = haxby_dataset.mask

# Load the behavioral data that we will predict
labels = pd.read_csv(haxby_dataset.session_target[0], sep=" ")
y = labels["labels"]
run = labels["chunks"]

# Remove the rest condition, it is not very interesting
non_rest = y != "rest"
y = y[non_rest]

# Get the labels of the numerical conditions represented by the vector y
unique_conditions, order = np.unique(y, return_index=True)
# Sort the conditions by the order of appearance
unique_conditions = unique_conditions[np.argsort(order)]

# %%
# Prepare the :term:`fMRI` data
# -----------------------------
from nilearn.maskers import NiftiMasker

# For decoding, standardizing is often very important
nifti_masker = NiftiMasker(
    mask_img=mask_filename,
    standardize="zscore_sample",
    runs=run,
    smoothing_fwhm=4,
    memory="nilearn_cache",
    memory_level=1,
    verbose=1,
)
X = nifti_masker.fit_transform(func_filename)

# Remove the "rest" condition
X = X[non_rest]
run = run[non_rest]

# %%
# Build the decoders, using scikit-learn
# --------------------------------------
# Here we use a Support Vector Classification, with a linear kernel,
# and a simple feature selection step

from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.multiclass import OneVsOneClassifier, OneVsRestClassifier
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC

svc_ovo = OneVsOneClassifier(
    Pipeline(
        [
            ("anova", SelectKBest(f_classif, k=500)),
            ("svc", SVC(kernel="linear")),
        ]
    )
)

svc_ova = OneVsRestClassifier(
    Pipeline(
        [
            ("anova", SelectKBest(f_classif, k=500)),
            ("svc", SVC(kernel="linear")),
        ]
    )
)

# %%
# Now we compute cross-validation scores
# --------------------------------------
from sklearn.model_selection import cross_val_score

cv_scores_ovo = cross_val_score(svc_ovo, X, y, cv=5, verbose=1)

cv_scores_ova = cross_val_score(svc_ova, X, y, cv=5, verbose=1)

print("OvO:", cv_scores_ovo.mean())
print("OvA:", cv_scores_ova.mean())

# %%
# Plot barplots of the prediction scores
# --------------------------------------
from matplotlib import pyplot as plt

plt.figure(figsize=(4, 3))
plt.boxplot([cv_scores_ova, cv_scores_ovo])
plt.xticks([1, 2], ["One vs All", "One vs One"])
plt.title("Prediction: accuracy score")

# %%
# Plot a confusion matrix
# -----------------------
# We fit on the first 10 runs and plot a confusion matrix on the
# last 2 runs
from sklearn.metrics import confusion_matrix

svc_ovo.fit(X[run < 10], y[run < 10])
y_pred_ovo = svc_ovo.predict(X[run >= 10])

plot_matrix(
    confusion_matrix(y_pred_ovo, y[run >= 10]),
    labels=unique_conditions,
    title="Confusion matrix: One vs One",
    cmap="inferno",
)

svc_ova.fit(X[run < 10], y[run < 10])
y_pred_ova = svc_ova.predict(X[run >= 10])

plot_matrix(
    confusion_matrix(y_pred_ova, y[run >= 10]),
    labels=unique_conditions,
    title="Confusion matrix: One vs All",
    cmap="inferno",
)

show()

# sphinx_gallery_dummy_images=3
