A simple start (on the command line)ΒΆ

This script is the exact equivalent of the A simple start example, but using the command line interface. First we load an fMRI dataset with some attributes for each volume, only considering voxels that are non-zero in a mask image.

pymvpa2 mkds --mri-data "$dataroot"/bold.nii.gz \
            --add-sa-attr "$dataroot"/attributes_literal.txt  \
            --mask "$dataroot"/mask.nii.gz \
            -o "$outdir"/bold_ds.hdf5

Next we remove linear trends by polynomial regression for each voxel and each chunk (recording run) of the dataset individually.

pymvpa2 preproc --chunks chunks \
                --poly-detrend 1 \
                -i "$outdir"/bold_ds.hdf5 \
                -o "$outdir"/preproced.hdf5

For this example we are only interested in data samples that correspond to the face or to the house condition.

pymvpa2 select --samples-by-attr targets eq face or targets eq house \
               -i "$outdir"/preproced.hdf5 \
               -o "$outdir"/facehouse.hdf5

The setup for our cross-validation analysis include the selection of a classifier from the “warehouse”, a partitioning scheme, and an error function to convert literal predictions into a quantitative performance metric.

pymvpa2 crossval --learner 'SMLR(lm=1.0)' \
                 --partitioner oddeven:chunks \
                 --errorfx mean_mismatch_error \
                 --avg-datafold-results \
                 -i "$outdir"/facehouse.hdf5 \
                 -o "$outdir"/crossval_results.hdf5

The resulting dataset contains the computed accuracy.

echo -n "Error for cross-validation problem: "
pymvpa2 dump -s -i "$outdir"/crossval_results.hdf5

See also

The full source code of this example is included in the PyMVPA source distribution (doc/examples/cmdline/start_easy.sh).