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.


If you are using Nilearn with a version older than 0.9.0, then you should either upgrade your version or import maskers from the input_data module instead of the maskers module.

That is, you should manually replace in the following example all occurrences of:

from nilearn.maskers import NiftiMasker


from nilearn.input_data import NiftiMasker
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)]
Mask nifti images are located at: /home/himanshu/nilearn_data/haxby2001/mask.nii.gz
Functional nifti images are located at: /home/himanshu/nilearn_data/haxby2001/subj2/bold.nii.gz

Prepare the fMRI data#

from nilearn.maskers import NiftiMasker

# For decoding, standardizing is often very important
nifti_masker = NiftiMasker(
X = nifti_masker.fit_transform(func_filename)

# Remove the "rest" condition
X = X[non_rest]
run = run[non_rest]
/home/himanshu/.local/miniconda3/envs/nilearnpy/lib/python3.12/site-packages/nilearn/image/ UserWarning:

The provided image has no sform in its header. Please check the provided file. Results may not be as expected.

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(
            ("anova", SelectKBest(f_classif, k=500)),
            ("svc", SVC(kernel="linear")),

svc_ova = OneVsRestClassifier(
            ("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())
OvO: 0.601855088049469
OvA: 0.6712058072321548

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")
Prediction: accuracy score
Text(0.5, 1.0, '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[run < 10], y[run < 10])
y_pred_ovo = svc_ovo.predict(X[run >= 10])

    confusion_matrix(y_pred_ovo, y[run >= 10]),
    title="Confusion matrix: One vs One",
)[run < 10], y[run < 10])
y_pred_ova = svc_ova.predict(X[run >= 10])

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

  • Confusion matrix: One vs One
  • Confusion matrix: One vs All

Total running time of the script: (0 minutes 36.784 seconds)

Estimated memory usage: 3156 MB

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