# As a decoder, we use a Support Vector Classification, with a linear kernel. # We first create it using by using :class:`nilearn.decoding.Decoder`. from nilearn.decoding import Decoder decoder = Decoder(estimator='svc', mask=mask_filename, standardize=True) ########################################################################### # The svc object is an object that can be fit (or trained) on data with # labels, and then predict labels on data without. # # We first fit it on the data decoder.fit(fmri_niimgs, conditions) ########################################################################### # We can then predict the labels from the data prediction = decoder.predict(fmri_niimgs) print(prediction) ########################################################################### # Let's measure the prediction accuracy: print((prediction == conditions).sum() / float(len(conditions))) ########################################################################### # This prediction accuracy score is meaningless. Why? ########################################################################### # Measuring prediction scores using cross-validation # --------------------------------------------------- # # The proper way to measure error rates or prediction accuracy is via # cross-validation: leaving out some data and testing on it.
# -------------------------------------------------
# As a complete pipeline by itself, decoder will perform cross-validation
# for the estimator, in this case Support Vector Machine. We can output the
# best parameters selected for each cross-validation fold. See
# https://scikit-learn.org/stable/modules/cross_validation.html for an
# excellent explanation of how cross-validation works.
#
# First we fit the Decoder
decoder.fit(fmri_niimgs, y)
for i, (param, cv_score) in enumerate(
zip(decoder.cv_params_['shoe']['C'], decoder.cv_scores_['shoe'])):
print("Fold %d | Best SVM parameter: %.1f with score: %.3f" %
(i + 1, param, cv_score))
# Output the prediction with Decoder
y_pred = decoder.predict(fmri_niimgs)
###########################################################################
# Compute prediction scores with different values of screening percentile
# -----------------------------------------------------------------------
import numpy as np
screening_percentile_range = [2, 4, 8, 16, 32, 64]
cv_scores = []
val_scores = []
for sp in screening_percentile_range:
decoder = Decoder(estimator='svc',
mask=mask_img,
smoothing_fwhm=4,
cv=3,
standardize=True,
# on nested cross-validation.
from nilearn.decoding import Decoder
# Here screening_percentile is set to 5 percent
mask_img = haxby_dataset.mask
decoder = Decoder(estimator='svc',
mask=mask_img,
smoothing_fwhm=4,
standardize=True,
screening_percentile=5,
scoring='accuracy')
#############################################################################
# Fit the decoder and predict
# ----------------------------
decoder.fit(func_img, conditions)
y_pred = decoder.predict(func_img)
#############################################################################
# Obtain prediction scores via cross validation
# -----------------------------------------------
# Define the cross-validation scheme used for validation. Here we use a
# LeaveOneGroupOut cross-validation on the session group which corresponds to a
# leave a session out scheme, then pass the cross-validator object to the cv
# parameter of decoder.leave-one-session-out For more details please take a
# look at:
# <https://nilearn.github.io/stable/auto_examples/plot_decoding_tutorial.html#measuring-prediction-scores-using-cross-validation>
from sklearn.model_selection import LeaveOneGroupOut
cv = LeaveOneGroupOut()
decoder = Decoder(estimator='svc',
mask=mask_img,
# ---------------------------------
#
# Note that for this classification task both classes contain the same number
# of samples (the problem is balanced). Then, we can use accuracy to measure
# the performance of the decoder. This is done by defining accuracy as the
# `scoring`.
from nilearn.decoding import Decoder
decoder = Decoder(estimator='svc', mask_strategy='background',
smoothing_fwhm=4, scoring='accuracy')
decoder.fit(X_train, y_train)
accuracy = np.mean(decoder.cv_scores_[b"house"]) * 100
print("Decoder cross-validation accuracy : %f%%" % accuracy)
# Testing on out-of-sample data
y_pred = decoder.predict(X_test)
accuracy = (y_pred == y_test).mean() * 100.
print("Decoder classification accuracy : %f%%" % accuracy)
######################################################################
# Visualization
# --------------
weight_img = decoder.coef_img_[b"face"]
from nilearn.image import mean_img
background_img = mean_img(func_filenames)
from nilearn.plotting import plot_stat_map, show
plot_stat_map(weight_img, background_img, cut_coords=[-52, -5],
display_mode="yz",
