8.3.5. 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. Load the Haxby data dataset

from nilearn import datasets
import numpy as np
# By default 2nd subject from haxby datasets will be fetched.
haxby_dataset = datasets.fetch_haxby()

# Print basic information on the dataset
print('Mask nifti images are located at: %s' % haxby_dataset.mask)
print('Functional nifti images are located at: %s' % haxby_dataset.func[0])

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

# Load the behavioral data that we will predict
labels = np.recfromcsv(haxby_dataset.session_target[0], delimiter=" ")
y = labels['labels']
session = labels['chunks']

# Remove the rest condition, it is not very interesting
non_rest = y != b'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/kamalakar/nilearn_data/haxby2001/mask.nii.gz
Functional nifti images are located at: /home/kamalakar/nilearn_data/haxby2001/subj2/bold.nii.gz Prepare the fMRI data

from nilearn.input_data import NiftiMasker
# For decoding, standardizing is often very important
nifti_masker = NiftiMasker(mask_img=mask_filename, standardize=True,
                           sessions=session, smoothing_fwhm=4,
                           memory="nilearn_cache", memory_level=1)
X = nifti_masker.fit_transform(func_filename)

# Remove the "rest" condition
X = X[non_rest]
session = session[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.svm import SVC
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.multiclass import OneVsOneClassifier, OneVsRestClassifier
from sklearn.pipeline import Pipeline

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.cross_validation 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: 0.595292207792
OvA: 0.6704004329 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')
../../_images/sphx_glr_plot_haxby_multiclass_001.png Plot a confusion matrix

We fit on the the first 10 sessions and plot a confusion matrix on the last 2 sessions

from sklearn.metrics import confusion_matrix

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

plt.matshow(confusion_matrix(y_pred_ovo, y[session >= 10]))
plt.title('Confusion matrix: One vs One')
plt.xticks(np.arange(len(unique_conditions)), unique_conditions)
plt.yticks(np.arange(len(unique_conditions)), unique_conditions)

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

plt.matshow(confusion_matrix(y_pred_ova, y[session >= 10]))
plt.title('Confusion matrix: One vs All')
plt.xticks(np.arange(len(unique_conditions)), unique_conditions)
plt.yticks(np.arange(len(unique_conditions)), unique_conditions)

  • ../../_images/sphx_glr_plot_haxby_multiclass_002.png
  • ../../_images/sphx_glr_plot_haxby_multiclass_003.png

Total running time of the script: ( 0 minutes 38.210 seconds)

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