Note
Go to the end to download the full example code.
1. Computing RDMs#
As we did for classification, we will use an MEG dataset of humans performing a visual categorization task. Briefly, participants saw a list of 92 images. Effectively, these images are faces, not faces, human, not human, artificial, etc. For more information, consult MNE’s documentation or the original paper. Our goal here will be to create a neural RDM and a hypothesis RDM based on the neural data and the corresponding categories and see if we find some similarity between the two.
First, we will have to load the data—To do this, we use MNE’s sample code. Be aware that this will download roughly 6GB of data, which may take a while. As we did in the classification example, we will again be using only gradiometers (for convenience).
import matplotlib.pyplot as plt
import numpy as np
from pandas import read_csv
import mne
from mne.datasets import visual_92_categories
from mne.io import concatenate_raws, read_raw_fif
print(__doc__)
data_path = visual_92_categories.data_path()
# Define stimulus - trigger mapping
fname = data_path / "visual_stimuli.csv"
conds = read_csv(fname)
print(conds.head(5))
max_trigger = 92
conds = conds[:max_trigger] # take only the first 24 rows
conditions = []
for c in conds.values:
cond_tags = list(c[:2])
cond_tags += [
("not-" if i == 0 else "") + conds.columns[k] for k, i in enumerate(c[2:], 2)
]
conditions.append("/".join(map(str, cond_tags)))
print(conditions[:10])
event_id = dict(zip(conditions, conds.trigger + 1))
event_id["0/human bodypart/human/not-face/animal/natural"]
n_runs = 4 # 4 for full data (use less to speed up computations)
fnames = [data_path / f"sample_subject_{b}_tsss_mc.fif" for b in range(n_runs)]
raws = [
read_raw_fif(fname, verbose="error", on_split_missing="ignore") for fname in fnames
] # ignore filename warnings
raw = concatenate_raws(raws)
events = mne.find_events(raw, min_duration=0.002)
events = events[events[:, 2] <= max_trigger]
picks = mne.pick_types(raw.info, meg=True)
epochs = mne.Epochs(
raw,
events=events,
event_id=event_id,
baseline=None,
picks='grad',
tmin=-0.1,
tmax=0.500,
preload=True,
)
Using default location ~/mne_data for visual_92_categories...
Creating /home/runner/mne_data
Downloading file 'MNE-visual_92_categories-data-part1.tar.gz' from 'https://osf.io/8ejrs/download?version=1' to '/home/runner/mne_data'.
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43%|███████████████▊ | 1.59G/3.72G [00:21<00:28, 75.1MB/s]
43%|███████████████▉ | 1.60G/3.72G [00:21<00:28, 75.1MB/s]
43%|███████████████▉ | 1.61G/3.72G [00:21<00:28, 75.1MB/s]
43%|████████████████ | 1.61G/3.72G [00:21<00:28, 75.2MB/s]
44%|████████████████ | 1.62G/3.72G [00:21<00:27, 75.3MB/s]
44%|████████████████▏ | 1.63G/3.72G [00:21<00:28, 74.2MB/s]
44%|████████████████▎ | 1.64G/3.72G [00:21<00:27, 74.7MB/s]
44%|████████████████▎ | 1.64G/3.72G [00:22<00:27, 75.3MB/s]
44%|████████████████▍ | 1.65G/3.72G [00:22<00:27, 75.3MB/s]
45%|████████████████▍ | 1.66G/3.72G [00:22<00:27, 75.4MB/s]
45%|████████████████▌ | 1.67G/3.72G [00:22<00:27, 75.3MB/s]
45%|████████████████▋ | 1.68G/3.72G [00:22<00:27, 75.8MB/s]
45%|████████████████▋ | 1.68G/3.72G [00:22<00:26, 76.0MB/s]
45%|████████████████▊ | 1.69G/3.72G [00:22<00:27, 74.5MB/s]
46%|████████████████▊ | 1.70G/3.72G [00:22<00:27, 74.9MB/s]
46%|████████████████▉ | 1.71G/3.72G [00:22<00:26, 75.2MB/s]
46%|█████████████████ | 1.71G/3.72G [00:22<00:26, 75.2MB/s]
46%|█████████████████ | 1.72G/3.72G [00:23<00:26, 75.3MB/s]
46%|█████████████████▏ | 1.73G/3.72G [00:23<00:26, 75.5MB/s]
47%|█████████████████▏ | 1.74G/3.72G [00:23<00:26, 74.7MB/s]
47%|█████████████████▎ | 1.74G/3.72G [00:23<00:26, 74.9MB/s]
47%|█████████████████▍ | 1.75G/3.72G [00:23<00:26, 75.0MB/s]
47%|█████████████████▍ | 1.76G/3.72G [00:23<00:26, 74.9MB/s]
47%|█████████████████▌ | 1.77G/3.72G [00:23<00:26, 74.8MB/s]
48%|█████████████████▌ | 1.77G/3.72G [00:23<00:27, 72.0MB/s]
48%|█████████████████▋ | 1.78G/3.72G [00:23<00:27, 71.4MB/s]
48%|█████████████████▊ | 1.79G/3.72G [00:23<00:27, 71.0MB/s]
48%|█████████████████▊ | 1.79G/3.72G [00:24<00:27, 71.1MB/s]
48%|█████████████████▉ | 1.80G/3.72G [00:24<00:29, 65.4MB/s]
49%|█████████████████▉ | 1.81G/3.72G [00:24<00:29, 65.2MB/s]
49%|██████████████████ | 1.81G/3.72G [00:24<00:29, 64.0MB/s]
49%|██████████████████ | 1.82G/3.72G [00:24<00:31, 59.7MB/s]
49%|██████████████████▏ | 1.83G/3.72G [00:24<00:41, 45.9MB/s]
49%|██████████████████▏ | 1.83G/3.72G [00:25<00:56, 33.4MB/s]
49%|██████████████████▎ | 1.84G/3.72G [00:25<00:51, 36.8MB/s]
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50%|██████████████████▌ | 1.86G/3.72G [00:25<00:31, 58.6MB/s]
50%|██████████████████▌ | 1.87G/3.72G [00:25<00:29, 62.9MB/s]
50%|██████████████████▋ | 1.88G/3.72G [00:25<00:27, 66.4MB/s]
51%|██████████████████▋ | 1.89G/3.72G [00:25<00:26, 69.2MB/s]
51%|██████████████████▊ | 1.89G/3.72G [00:25<00:25, 70.9MB/s]
51%|██████████████████▉ | 1.90G/3.72G [00:26<00:25, 72.3MB/s]
51%|██████████████████▉ | 1.91G/3.72G [00:26<00:24, 74.1MB/s]
51%|███████████████████ | 1.92G/3.72G [00:26<00:24, 74.5MB/s]
52%|███████████████████ | 1.93G/3.72G [00:26<00:24, 74.6MB/s]
52%|███████████████████▏ | 1.93G/3.72G [00:26<00:23, 76.1MB/s]
52%|███████████████████▎ | 1.94G/3.72G [00:26<00:43, 41.3MB/s]
52%|███████████████████▎ | 1.95G/3.72G [00:26<00:41, 43.0MB/s]
52%|███████████████████▍ | 1.95G/3.72G [00:27<00:41, 42.8MB/s]
53%|███████████████████▍ | 1.96G/3.72G [00:27<00:38, 45.7MB/s]
53%|███████████████████▌ | 1.96G/3.72G [00:27<00:35, 49.1MB/s]
53%|███████████████████▌ | 1.97G/3.72G [00:27<00:35, 50.1MB/s]
53%|███████████████████▋ | 1.98G/3.72G [00:27<00:32, 54.2MB/s]
53%|███████████████████▋ | 1.98G/3.72G [00:27<00:34, 50.9MB/s]
53%|███████████████████▊ | 1.99G/3.72G [00:27<00:31, 55.4MB/s]
54%|███████████████████▊ | 2.00G/3.72G [00:27<00:28, 60.1MB/s]
54%|███████████████████▉ | 2.00G/3.72G [00:27<00:27, 63.5MB/s]
54%|███████████████████▉ | 2.01G/3.72G [00:27<00:25, 66.0MB/s]
54%|████████████████████ | 2.02G/3.72G [00:28<00:25, 67.8MB/s]
54%|████████████████████ | 2.02G/3.72G [00:28<00:24, 68.9MB/s]
55%|████████████████████▏ | 2.03G/3.72G [00:28<00:24, 70.2MB/s]
55%|████████████████████▎ | 2.04G/3.72G [00:28<00:23, 71.4MB/s]
55%|████████████████████▎ | 2.05G/3.72G [00:28<00:24, 67.4MB/s]
55%|████████████████████▍ | 2.05G/3.72G [00:28<00:30, 54.0MB/s]
55%|████████████████████▍ | 2.06G/3.72G [00:28<00:29, 55.8MB/s]
55%|████████████████████▌ | 2.07G/3.72G [00:28<00:27, 59.6MB/s]
56%|████████████████████▌ | 2.07G/3.72G [00:29<00:27, 61.1MB/s]
56%|████████████████████▋ | 2.08G/3.72G [00:29<00:25, 64.5MB/s]
56%|████████████████████▋ | 2.09G/3.72G [00:29<00:24, 67.7MB/s]
56%|████████████████████▊ | 2.10G/3.72G [00:29<00:22, 71.0MB/s]
56%|████████████████████▉ | 2.10G/3.72G [00:29<00:27, 58.2MB/s]
57%|████████████████████▉ | 2.11G/3.72G [00:29<00:29, 54.4MB/s]
57%|█████████████████████ | 2.12G/3.72G [00:29<00:29, 53.8MB/s]
57%|█████████████████████ | 2.12G/3.72G [00:29<00:28, 57.0MB/s]
57%|█████████████████████▏ | 2.13G/3.72G [00:29<00:26, 59.1MB/s]
57%|█████████████████████▏ | 2.14G/3.72G [00:30<00:25, 61.6MB/s]
58%|█████████████████████▎ | 2.14G/3.72G [00:30<00:24, 63.7MB/s]
58%|█████████████████████▎ | 2.15G/3.72G [00:30<00:24, 65.4MB/s]
58%|█████████████████████▍ | 2.16G/3.72G [00:30<00:23, 65.9MB/s]
58%|█████████████████████▍ | 2.16G/3.72G [00:30<00:22, 68.3MB/s]
58%|█████████████████████▌ | 2.17G/3.72G [00:30<00:22, 70.3MB/s]
58%|█████████████████████▋ | 2.18G/3.72G [00:30<00:21, 72.1MB/s]
59%|█████████████████████▋ | 2.19G/3.72G [00:30<00:21, 72.5MB/s]
59%|█████████████████████▊ | 2.19G/3.72G [00:30<00:20, 73.6MB/s]
59%|█████████████████████▊ | 2.20G/3.72G [00:30<00:20, 74.3MB/s]
59%|█████████████████████▉ | 2.21G/3.72G [00:31<00:20, 74.3MB/s]
59%|██████████████████████ | 2.22G/3.72G [00:31<00:20, 74.5MB/s]
60%|██████████████████████ | 2.22G/3.72G [00:31<00:20, 74.8MB/s]
60%|██████████████████████▏ | 2.23G/3.72G [00:31<00:20, 74.6MB/s]
60%|██████████████████████▏ | 2.24G/3.72G [00:31<00:19, 74.8MB/s]
60%|██████████████████████▎ | 2.25G/3.72G [00:31<00:19, 74.9MB/s]
61%|██████████████████████▍ | 2.25G/3.72G [00:31<00:19, 74.9MB/s]
61%|██████████████████████▍ | 2.26G/3.72G [00:31<00:19, 74.9MB/s]
61%|██████████████████████▌ | 2.27G/3.72G [00:31<00:19, 75.1MB/s]
61%|██████████████████████▌ | 2.28G/3.72G [00:31<00:19, 75.1MB/s]
61%|██████████████████████▋ | 2.28G/3.72G [00:32<00:19, 75.0MB/s]
62%|██████████████████████▊ | 2.29G/3.72G [00:32<00:19, 74.9MB/s]
62%|██████████████████████▊ | 2.30G/3.72G [00:32<00:19, 74.6MB/s]
62%|██████████████████████▉ | 2.31G/3.72G [00:32<00:18, 75.2MB/s]
62%|██████████████████████▉ | 2.31G/3.72G [00:32<00:19, 71.6MB/s]
62%|███████████████████████ | 2.32G/3.72G [00:32<00:20, 70.2MB/s]
63%|███████████████████████▏ | 2.33G/3.72G [00:32<00:19, 70.9MB/s]
63%|███████████████████████▏ | 2.34G/3.72G [00:32<00:19, 72.1MB/s]
63%|███████████████████████▎ | 2.34G/3.72G [00:32<00:18, 73.1MB/s]
63%|███████████████████████▎ | 2.35G/3.72G [00:32<00:18, 74.0MB/s]
63%|███████████████████████▍ | 2.36G/3.72G [00:33<00:18, 74.4MB/s]
64%|███████████████████████▌ | 2.37G/3.72G [00:33<00:18, 74.5MB/s]
64%|███████████████████████▌ | 2.37G/3.72G [00:33<00:18, 74.6MB/s]
64%|███████████████████████▋ | 2.38G/3.72G [00:33<00:18, 74.6MB/s]
64%|███████████████████████▋ | 2.39G/3.72G [00:33<00:17, 74.8MB/s]
64%|███████████████████████▊ | 2.40G/3.72G [00:33<00:17, 74.9MB/s]
65%|███████████████████████▉ | 2.40G/3.72G [00:33<00:17, 75.2MB/s]
65%|███████████████████████▉ | 2.41G/3.72G [00:33<00:17, 74.9MB/s]
65%|████████████████████████ | 2.42G/3.72G [00:33<00:17, 75.4MB/s]
65%|████████████████████████ | 2.43G/3.72G [00:33<00:17, 74.9MB/s]
65%|████████████████████████▏ | 2.43G/3.72G [00:34<00:17, 75.0MB/s]
66%|████████████████████████▎ | 2.44G/3.72G [00:34<00:17, 74.7MB/s]
66%|████████████████████████▎ | 2.45G/3.72G [00:34<00:17, 74.8MB/s]
66%|████████████████████████▍ | 2.46G/3.72G [00:34<00:17, 74.4MB/s]
66%|████████████████████████▍ | 2.46G/3.72G [00:34<00:16, 74.6MB/s]
66%|████████████████████████▌ | 2.47G/3.72G [00:34<00:16, 74.7MB/s]
67%|████████████████████████▌ | 2.48G/3.72G [00:34<00:16, 75.1MB/s]
67%|████████████████████████▋ | 2.49G/3.72G [00:34<00:16, 75.2MB/s]
67%|████████████████████████▊ | 2.49G/3.72G [00:34<00:16, 75.3MB/s]
67%|████████████████████████▊ | 2.50G/3.72G [00:34<00:16, 75.3MB/s]
67%|████████████████████████▉ | 2.51G/3.72G [00:35<00:16, 75.0MB/s]
68%|████████████████████████▉ | 2.52G/3.72G [00:35<00:16, 75.3MB/s]
68%|█████████████████████████ | 2.52G/3.72G [00:35<00:16, 74.8MB/s]
68%|█████████████████████████▏ | 2.53G/3.72G [00:35<00:15, 75.0MB/s]
68%|█████████████████████████▏ | 2.54G/3.72G [00:35<00:15, 74.8MB/s]
68%|█████████████████████████▎ | 2.55G/3.72G [00:35<00:15, 73.7MB/s]
69%|█████████████████████████▎ | 2.55G/3.72G [00:35<00:15, 74.2MB/s]
69%|█████████████████████████▍ | 2.56G/3.72G [00:35<00:15, 73.3MB/s]
69%|█████████████████████████▌ | 2.57G/3.72G [00:35<00:15, 73.7MB/s]
69%|█████████████████████████▌ | 2.58G/3.72G [00:35<00:15, 74.1MB/s]
69%|█████████████████████████▋ | 2.58G/3.72G [00:36<00:15, 74.4MB/s]
70%|█████████████████████████▋ | 2.59G/3.72G [00:36<00:15, 74.6MB/s]
70%|█████████████████████████▊ | 2.60G/3.72G [00:36<00:14, 75.1MB/s]
70%|█████████████████████████▉ | 2.61G/3.72G [00:36<00:14, 75.3MB/s]
70%|█████████████████████████▉ | 2.61G/3.72G [00:36<00:14, 76.1MB/s]
70%|██████████████████████████ | 2.62G/3.72G [00:36<00:14, 74.5MB/s]
71%|██████████████████████████ | 2.63G/3.72G [00:36<00:14, 74.5MB/s]
71%|██████████████████████████▏ | 2.64G/3.72G [00:36<00:14, 75.7MB/s]
71%|██████████████████████████▎ | 2.64G/3.72G [00:36<00:14, 72.0MB/s]
71%|██████████████████████████▎ | 2.65G/3.72G [00:37<00:15, 70.4MB/s]
71%|██████████████████████████▍ | 2.66G/3.72G [00:37<00:30, 34.9MB/s]
72%|██████████████████████████▍ | 2.67G/3.72G [00:37<00:26, 40.4MB/s]
72%|██████████████████████████▌ | 2.67G/3.72G [00:37<00:22, 46.4MB/s]
72%|██████████████████████████▋ | 2.68G/3.72G [00:37<00:19, 52.8MB/s]
72%|██████████████████████████▋ | 2.69G/3.72G [00:38<00:24, 41.7MB/s]
72%|██████████████████████████▊ | 2.69G/3.72G [00:38<00:27, 37.8MB/s]
72%|██████████████████████████▊ | 2.70G/3.72G [00:38<00:24, 41.1MB/s]
73%|██████████████████████████▊ | 2.70G/3.72G [00:38<00:21, 46.3MB/s]
73%|██████████████████████████▉ | 2.71G/3.72G [00:38<00:23, 42.4MB/s]
73%|██████████████████████████▉ | 2.72G/3.72G [00:38<00:22, 44.6MB/s]
73%|███████████████████████████ | 2.72G/3.72G [00:38<00:20, 49.4MB/s]
73%|███████████████████████████ | 2.73G/3.72G [00:38<00:18, 54.2MB/s]
73%|███████████████████████████▏ | 2.74G/3.72G [00:38<00:17, 57.1MB/s]
74%|███████████████████████████▏ | 2.74G/3.72G [00:39<00:16, 57.9MB/s]
74%|███████████████████████████▎ | 2.75G/3.72G [00:39<00:16, 59.9MB/s]
74%|███████████████████████████▎ | 2.76G/3.72G [00:39<00:15, 63.3MB/s]
74%|███████████████████████████▍ | 2.76G/3.72G [00:39<00:14, 65.7MB/s]
74%|███████████████████████████▌ | 2.77G/3.72G [00:39<00:14, 64.6MB/s]
75%|███████████████████████████▌ | 2.78G/3.72G [00:39<00:14, 65.6MB/s]
75%|███████████████████████████▋ | 2.78G/3.72G [00:39<00:14, 67.1MB/s]
75%|███████████████████████████▋ | 2.79G/3.72G [00:39<00:13, 68.5MB/s]
75%|███████████████████████████▊ | 2.80G/3.72G [00:39<00:13, 70.3MB/s]
75%|███████████████████████████▊ | 2.80G/3.72G [00:39<00:13, 68.9MB/s]
75%|███████████████████████████▉ | 2.81G/3.72G [00:40<00:18, 48.3MB/s]
76%|███████████████████████████▉ | 2.82G/3.72G [00:40<00:19, 47.2MB/s]
76%|████████████████████████████ | 2.82G/3.72G [00:40<00:17, 50.9MB/s]
76%|████████████████████████████ | 2.83G/3.72G [00:40<00:16, 55.8MB/s]
76%|████████████████████████████▏ | 2.84G/3.72G [00:40<00:14, 60.5MB/s]
76%|████████████████████████████▎ | 2.85G/3.72G [00:40<00:13, 64.5MB/s]
77%|████████████████████████████▎ | 2.85G/3.72G [00:40<00:12, 67.5MB/s]
77%|████████████████████████████▍ | 2.86G/3.72G [00:40<00:12, 70.0MB/s]
77%|████████████████████████████▍ | 2.87G/3.72G [00:41<00:11, 71.5MB/s]
77%|████████████████████████████▌ | 2.88G/3.72G [00:41<00:11, 72.7MB/s]
77%|████████████████████████████▋ | 2.88G/3.72G [00:41<00:11, 73.7MB/s]
78%|████████████████████████████▋ | 2.89G/3.72G [00:41<00:11, 74.3MB/s]
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Downloading file 'MNE-visual_92_categories-data-part2.tar.gz' from 'https://osf.io/t4yjp/download?version=1' to '/home/runner/mne_data'.
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Untarring contents of '/home/runner/mne_data/MNE-visual_92_categories-data-part2.tar.gz' to '/home/runner/mne_data'
Attempting to create new mne-python configuration file:
/home/runner/.mne/mne-python.json
Could not read the /home/runner/.mne/mne-python.json json file during the writing. Assuming it is empty. Got: Expecting value: line 1 column 1 (char 0)
Download complete in 02m44s (5468.7 MB)
trigger condition human face animal natural
0 0 human bodypart 1 0 1 1
1 1 human bodypart 1 0 1 1
2 2 human bodypart 1 0 1 1
3 3 human bodypart 1 0 1 1
4 4 human bodypart 1 0 1 1
['0/human bodypart/human/not-face/animal/natural', '1/human bodypart/human/not-face/animal/natural', '2/human bodypart/human/not-face/animal/natural', '3/human bodypart/human/not-face/animal/natural', '4/human bodypart/human/not-face/animal/natural', '5/human bodypart/human/not-face/animal/natural', '6/human bodypart/human/not-face/animal/natural', '7/human bodypart/human/not-face/animal/natural', '8/human bodypart/human/not-face/animal/natural', '9/human bodypart/human/not-face/animal/natural']
Finding events on: STI101
4142 events found on stim channel STI101
Event IDs: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
91 92 93 200 222 244]
Not setting metadata
2760 matching events found
No baseline correction applied
0 projection items activated
Loading data for 2760 events and 601 original time points ...
2 bad epochs dropped
Before we can compute RDMs, we would like to also get some data to base our hypothesis RDMs on. Specifically, we will use the epoch categories (human/nonhuman/natural/artificial, bodypart/face/inanimate, human/not-human, face/not-face, natural/not-natural) to build features vectors of our images, like so:
# generate some vectors for conditions based on labels
condition_types = {}
for i, cond in enumerate(conditions):
items = '/'.join(cond.split(' ')).split('/')[1:]
for j, item in enumerate(items):
if j not in condition_types:
condition_types[j] = []
if item not in condition_types[j]:
condition_types[j].append(item)
N = np.array([len(condition_types[i]) for i in range(len(condition_types))]).sum()
condition_vecs = np.zeros((len(conditions), N))
for i, cond in enumerate(conditions):
items = '/'.join(cond.split(' ')).split('/')[1:]
indx_i = 0
for j, item in enumerate(items):
indx_j = np.where(np.array(condition_types[j]) == item)[0]
condition_vecs[i,indx_i+indx_j[0]] = 1
indx_i += len(condition_types[j])
# generate vectors for trials based on condition labels
epochs, indc = epochs.equalize_event_counts()
X = epochs.get_data()
y = np.zeros((X.shape[0], N, X.shape[-1]))
L = np.zeros((X.shape[0],))
for i, event in enumerate(epochs.events):
y[i,:,:] = condition_vecs[event[2] - 1,:,None]
L[i] = event[2] - 1
# group by condition
unq, counts = np.unique(L, return_counts = True)
X_g = np.zeros((counts[0], len(unq), X.shape[1], X.shape[2]))
y_g = np.zeros((counts[0], len(unq), y.shape[1], y.shape[2]))
for i, unq_i in enumerate(unq):
indc = np.where(L == unq_i)[0]
X_g[:,i] = X[indc]
y_g[:,i] = y[indc]
Dropped 90 epochs: 930, 1478, 1479, 1480, 1481, 1482, 1486, 1492, 1493, 1499, 1504, 1505, 1518, 1520, 1521, 1525, 1539, 1657, 1659, 1669, 1709, 1713, 1730, 1742, 1743, 1745, 1749, 1752, 1754, 1757, 1758, 1761, 1763, 1768, 1770, 1771, 1772, 1774, 1777, 1778, 1779, 1780, 1781, 1782, 1784, 1785, 1787, 1788, 1790, 1791, 1792, 1793, 1794, 1795, 1796, 1798, 1800, 1803, 1805, 1806, 1807, 1808, 1810, 1812, 1814, 1815, 1817, 1818, 1819, 1820, 1821, 1823, 1824, 1825, 1826, 1827, 1830, 1831, 1832, 1833, 1836, 1837, 1838, 1946, 1959, 2001, 2007, 2011, 2013, 2016
Now that we have our data, let’s move everything to torch. Note that, by default, this will try to look for a GPU.
import torch
# convert data
device = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
X_g, y_g = torch.from_numpy(X_g).to(torch.float32).to(device), torch.from_numpy(y_g).to(torch.float32).to(device)
With the setup out of the way, let’s create a quick pipeline to compute our neural and hypothesis RDMs:
from mvpy.estimators import Scaler, RSA
from mvpy.math import *
from sklearn.pipeline import make_pipeline
# compute the neural RDM with the following pipeline:
# 1. Scale the data (zero mean and unit variance)
# 2. Compute the neural RDM using the Pearson correlation as our similarity measure
n_rsa = make_pipeline(Scaler().to_torch(),
RSA(estimator = pearsonr,
verbose = True,
n_jobs = None).to_torch())
n_rsa.fit(X_g.mean(0))
n_rsa.transform(X_g.mean(0))
# compute the hypothesis RDM; pipeline is same as neural RDM
h_rsa = make_pipeline(Scaler().to_torch(),
RSA(estimator = pearsonr,
verbose = True,
n_jobs = None).to_torch())
h_rsa.fit(y_g.mean(0))
h_rsa.transform(y_g.mean(0))
# Now, let's compare the two RDMs we have so far by computing their spearman correlations
rho_pr = spearmanr(n_rsa[1].rdm_.T, h_rsa[1].rdm_.T)
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With our result in hand, we may find ourselves wanting to look at the results and some of the raw RSMs we computed. Let’s do that now:
fig, ax = plt.subplots(nrows = 1, ncols = 3, figsize = (9.0, 4.0))
max_tp = rho_pr.argmax().cpu().numpy()
t = np.arange(-0.1, 0.5+1e-3, 1e-3) # time points
ax[0].plot(t, 0*t, color = 'red') # plot chance level
ax[0].plot(t, rho_pr.cpu().numpy()) # plot spearman correlation
ax[0].set_xlabel(r'Time ($s$)')
ax[0].set_ylabel(r'Spearman $\rho$')
ax[1].imshow(n_rsa[1].full_rdm().cpu().numpy()[:,:,max_tp], vmin = -1, vmax = 1, cmap = 'RdBu_r') # obtain and plot the full neural RDM from our class
ax[1].set_title(fr'nRDM at $t={np.round(max_tp*1e-3 - 0.1, 3)}s$')
ax[1].set_ylabel('Categories')
ax[1].set_xlabel('Categories')
ax[2].imshow(h_rsa[1].full_rdm().cpu().numpy()[:,:,max_tp], vmin = -1, vmax = 1, cmap = 'RdBu_r') # obtain and plot the full hypothesis RDM from our class
ax[2].set_title(fr'hRDM at $t={np.round(max_tp*1e-3 - 0.1, 3)}s$')
ax[2].set_ylabel('Categories')
ax[2].set_xlabel('Categories')
plt.tight_layout()
Total running time of the script: (3 minutes 27.923 seconds)