MaxKorbmacher
commented
1 year ago Whole notebook:
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"source": [
"## **Setup**"
],
"metadata": {
"id": "25Zmh-iSX6jz"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"\n",
"```\n",
"# IMPORTANT NOTE:\n",
"# We work with a GPU here, which requires to change the Runtime settings\n",
"# go to Runtime >> Change runtime type >> select GPU under Hardware accelerator\n",
"```\n",
"\n"
],
"metadata": {
"id": "dkLtvJsb6Wgp"
}
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fR0PZAqD_moQ",
"outputId": "f6e247fa-8010-4263-f362-9f15d8e3ffae"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting nimare\n",
" Downloading NiMARE-0.1.1-py3-none-any.whl (13.3 MB)\n",
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"\u001b[?25hCollecting cognitiveatlas (from nimare)\n",
" Downloading cognitiveatlas-0.1.9.tar.gz (5.1 kB)\n",
" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Collecting fuzzywuzzy (from nimare)\n",
" Downloading fuzzywuzzy-0.18.0-py2.py3-none-any.whl (18 kB)\n",
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"Building wheels for collected packages: cognitiveatlas\n",
" Building wheel for cognitiveatlas (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for cognitiveatlas: filename=cognitiveatlas-0.1.9-py3-none-any.whl size=6376 sha256=d38afcb03ae02081f8101428d4b23675517511c3deb14ec2053c311bca668a73\n",
" Stored in directory: /root/.cache/pip/wheels/a3/6c/09/eff269417bf07149992261253dd9d11eae5c2a53bb53cdc7ed\n",
"Successfully built cognitiveatlas\n",
"Installing collected packages: fuzzywuzzy, tzdata, nibabel, llvmlite, pandas, numba, sparse, pymare, nilearn, cognitiveatlas, nimare\n",
" Attempting uninstall: nibabel\n",
" Found existing installation: nibabel 3.0.2\n",
" Uninstalling nibabel-3.0.2:\n",
" Successfully uninstalled nibabel-3.0.2\n",
" Attempting uninstall: llvmlite\n",
" Found existing installation: llvmlite 0.39.1\n",
" Uninstalling llvmlite-0.39.1:\n",
" Successfully uninstalled llvmlite-0.39.1\n",
" Attempting uninstall: pandas\n",
" Found existing installation: pandas 1.5.3\n",
" Uninstalling pandas-1.5.3:\n",
" Successfully uninstalled pandas-1.5.3\n",
" Attempting uninstall: numba\n",
" Found existing installation: numba 0.56.4\n",
" Uninstalling numba-0.56.4:\n",
" Successfully uninstalled numba-0.56.4\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"google-colab 1.0.0 requires pandas==1.5.3, but you have pandas 2.0.3 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed cognitiveatlas-0.1.9 fuzzywuzzy-0.18.0 llvmlite-0.40.1 nibabel-5.1.0 nilearn-0.10.1 nimare-0.1.1 numba-0.57.1 pandas-2.0.3 pymare-0.0.4rc2 sparse-0.14.0 tzdata-2023.3\n"
]
}
],
"source": [
"pip install nimare"
]
},
{
"cell_type": "code",
"source": [
"pip install biopython"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WCip6as1AYod",
"outputId": "d17f36a5-8d95-479e-8ea3-bf2cb90cf566"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting biopython\n",
" Downloading biopython-1.81-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m32.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from biopython) (1.22.4)\n",
"Installing collected packages: biopython\n",
"Successfully installed biopython-1.81\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# objective: testing whether meta-regression reanalysis produces same results as meta-analyses on neurosynth\n",
"\n",
"############ PREP\n",
"# dependencies which need prior installation: nimare & biopython\n",
"\n",
"import os\n",
"from pprint import pprint\n",
"\n",
"from nimare.extract import download_abstracts, fetch_neuroquery, fetch_neurosynth\n",
"from nimare.io import convert_neurosynth_to_dataset\n",
"import Bio\n",
"\n",
"########## DOWNLOAD NEUROSYNTH DATA\n",
"out_dir = os.path.abspath(\"../example_data/\")\n",
"os.makedirs(out_dir, exist_ok=True)\n",
"\n",
"files = fetch_neurosynth(\n",
" data_dir=out_dir,\n",
" version=\"7\",\n",
" overwrite=False,\n",
" source=\"abstract\",\n",
" vocab=\"terms\",\n",
")\n",
"# Note that the files are saved to a new folder within \"out_dir\" named \"neurosynth\".\n",
"pprint(files)\n",
"neurosynth_db = files[0]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gJYAYp3oAnx9",
"outputId": "a4909a23-9900-4893-adb7-8e3306e24220"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading data-neurosynth_version-7_coordinates.tsv.gz\n",
"Downloading data-neurosynth_version-7_metadata.tsv.gz\n",
"Downloading data-neurosynth_version-7_vocab-terms_source-abstract_type-tfidf_features.npz\n",
"Downloading data-neurosynth_version-7_vocab-terms_vocabulary.txt\n",
"[{'coordinates': '/example_data/neurosynth/data-neurosynth_version-7_coordinates.tsv.gz',\n",
" 'features': [{'features': '/example_data/neurosynth/data-neurosynth_version-7_vocab-terms_source-abstract_type-tfidf_features.npz',\n",
" 'vocabulary': '/example_data/neurosynth/data-neurosynth_version-7_vocab-terms_vocabulary.txt'}],\n",
" 'metadata': '/example_data/neurosynth/data-neurosynth_version-7_metadata.tsv.gz'}]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Convert Neurosynth database to NiMARE dataset file\n",
"neurosynth_dset = convert_neurosynth_to_dataset(\n",
" coordinates_file=neurosynth_db[\"coordinates\"],\n",
" metadata_file=neurosynth_db[\"metadata\"],\n",
" annotations_files=neurosynth_db[\"features\"],\n",
")\n",
"neurosynth_dset.save(os.path.join(out_dir, \"neurosynth_dataset.pkl.gz\"))\n",
"print(neurosynth_dset)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QZv6Nzk1BN9e",
"outputId": "c2ddcdfe-7068-4335-81e8-73c5bbecb227"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:nimare.utils:Not applying transforms to coordinates in unrecognized space 'UNKNOWN'\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Dataset(14371 experiments, space='mni152_2mm')\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Add article abstracts to dataset\n",
"neurosynth_dset = download_abstracts(neurosynth_dset, \"example@example.edu\")\n",
"neurosynth_dset.save(os.path.join(out_dir, \"neurosynth_dataset_with_abstracts.pkl.gz\"))"
],
"metadata": {
"id": "NDB2SjoaBQP-"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# # FOR NOW, THIS STEP OF **LOADING NEUROQUERY DATA** IS BEING **SKIPPED**!!\n",
"# # (too large RAM requirements for Google Colab)\n",
"#\n",
"#\n",
"# # Do the same with NeuroQuery\n",
"# # NeuroQuery’s data files are stored at https://github.com/neuroquery/neuroquery_data.\n",
"# files = fetch_neuroquery(\n",
"# data_dir=out_dir,\n",
"# version=\"1\",\n",
"# overwrite=False,\n",
"# source=\"combined\",\n",
"# vocab=\"neuroquery6308\",\n",
"# type=\"tfidf\",\n",
"# )\n",
"# # Note that the files are saved to a new folder within \"out_dir\" named \"neuroquery\".\n",
"# pprint(files)\n",
"# neuroquery_db = files[0]\n",
"\n",
"# # Note that the conversion function says \"neurosynth\".\n",
"# # This is just for backwards compatibility.\n",
"# neuroquery_dset = convert_neurosynth_to_dataset(\n",
"# coordinates_file=neuroquery_db[\"coordinates\"],\n",
"# metadata_file=neuroquery_db[\"metadata\"],\n",
"# annotations_files=neuroquery_db[\"features\"],\n",
"# )\n",
"# neuroquery_dset.save(os.path.join(out_dir, \"neuroquery_dataset.pkl.gz\"))\n",
"# print(neuroquery_dset)\n",
"\n",
"# # NeuroQuery also uses PMIDs as study IDs.\n",
"# neuroquery_dset = download_abstracts(neuroquery_dset, \"example@example.edu\")\n",
"# neuroquery_dset.save(os.path.join(out_dir, \"neuroquery_dataset_with_abstracts.pkl.gz\"))\n",
"#\n",
"# ######################################"
],
"metadata": {
"id": "MmK0B7NmBUcb"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## **Test 1: Motor Studies**"
],
"metadata": {
"id": "sZbbaTdaYLqL"
}
},
{
"cell_type": "code",
"source": [
"# Label data and test meta-regression on subsets\n",
"\n",
"# motor (2565 studies)\n",
"# compare results to https://neurosynth.org/analyses/terms/motor/\n",
"\n",
"# identify which studies examine motor functions\n",
"motor_study_id = neurosynth_dset.get_studies_by_label(labels=[\"terms_abstract_tfidf__motor\"])\n",
"# and those which do not (rest of the studies)\n",
"nonmotor_study_id = list(set(neurosynth_dset.ids) - set(motor_study_id))\n",
"# create a subset only containing motor studies\n",
"motor_dset = neurosynth_dset.slice(ids=motor_study_id)\n",
"# and a subset containing no motor studies\n",
"nonmotor_dset = neurosynth_dset.slice(ids=nonmotor_study_id)\n",
"\n",
"# we can now create a dummy variable indicating which study involved motor tasks and which did not.\n",
"neurosynth_dset.annotations[\"motor\"] = 'False'\n",
"neurosynth_dset.annotations.loc[neurosynth_dset.annotations['id'].isin(motor_study_id), 'motor'] = 'True'\n",
"# and a dummy indicating the absence of moderators (all values = 1)\n",
"neurosynth_dset.annotations[\"moderator\"] = 1"
],
"metadata": {
"id": "11-DPKlYPYSG"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!nvidia-smi\n",
"import torch\n",
"torch.cuda.is_available()\n",
"import tensorflow as tf\n",
"tf.test.gpu_device_name()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "tD1g74HT-w3L",
"outputId": "e0e32c73-f19e-4d66-ab6c-8ff41335b066"
},
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'/device:GPU:0'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"source": [
"# set up the model\n",
"import numpy as np\n",
"import scipy\n",
"from nilearn.plotting import plot_stat_map\n",
"\n",
"from nimare.generate import create_coordinate_dataset\n",
"from nimare.meta import models\n",
"from nimare.transforms import StandardizeField\n",
"from nimare.meta.cbmr import CBMREstimator\n",
"\n",
"cbmr = CBMREstimator(\n",
" group_categories=[\"motor\"],\n",
" moderators=[\n",
" \"moderator\",\n",
" ],\n",
" spline_spacing=100, # a reasonable choice is 10 or 5, 100 is for speed\n",
" model=models.PoissonEstimator,\n",
" penalty=False,\n",
" lr=1e-1,\n",
" tol=1e3, # a reasonable choice is 1e-2, 1e3 is for speed\n",
" device=\"cuda\", # \"cuda\" if you have GPU or \"cpu\" for CPU\n",
")\n",
"results = cbmr.fit(dataset=neurosynth_dset)\n",
"\n",
"#############################"
],
"metadata": {
"id": "A0PIvYxy1QKh",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 417
},
"outputId": "064bd828-6461-42d9-f8dd-cc75cedc5ec0"
},
"execution_count": 9,
"outputs": [
{
"output_type": "error",
"ename": "RuntimeError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-ba45c24639e9>\u001b[0m in \u001b[0;36m<cell line: 23>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cuda\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# \"cuda\" if you have GPU or \"cpu\" for CPU\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m )\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbmr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mneurosynth_dset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;31m#############################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/nimare/estimator.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, dataset, drop_invalid)\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_collect_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_invalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop_invalid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_preprocess_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m \u001b[0mmaps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescription\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"masker\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmasker\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/nimare/meta/cbmr.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 395\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 396\u001b[0m \u001b[0mmoderators_by_group\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"moderators_by_group\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmoderators\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 397\u001b[0;31m self.model.fit(\n\u001b[0m\u001b[1;32m 398\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"coef_spline_bases\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 399\u001b[0m \u001b[0mmoderators_by_group\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/nimare/meta/models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study)\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoef_spline_bases\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoderators_by_group\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfoci_per_voxel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfoci_per_study\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 296\u001b[0m \u001b[0;34m\"\"\"Fit the model and estimate standard error of estimates.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_optimizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoef_spline_bases\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoderators_by_group\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfoci_per_voxel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfoci_per_study\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 298\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextract_optimized_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoef_spline_bases\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoderators_by_group\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 299\u001b[0m self.standard_error_estimation(\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/nimare/meta/models.py\u001b[0m in \u001b[0;36m_optimizer\u001b[0;34m(self, coef_spline_bases, moderators_by_group, foci_per_voxel, foci_per_study)\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 279\u001b[0;31m loss = self._update(\n\u001b[0m\u001b[1;32m 280\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[0mcoef_spline_bases\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/nimare/meta/models.py\u001b[0m in \u001b[0;36m_update\u001b[0;34m(self, optimizer, coef_spline_bases, moderators, foci_per_voxel, foci_per_study, prev_loss)\u001b[0m\n\u001b[1;32m 188\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 190\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclosure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 191\u001b[0m \u001b[0mscheduler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_step_count\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0mwrapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;31m# Note that the returned function here is no longer a bound method,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 278\u001b[0m f\"but got {result}.\")\n\u001b[1;32m 279\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 281\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_optimizer_step_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 282\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/lbfgs.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[0;31m# evaluate initial f(x) and df/dx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 312\u001b[0;31m \u001b[0morig_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclosure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 313\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 314\u001b[0m \u001b[0mcurrent_evals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/nimare/meta/models.py\u001b[0m in \u001b[0;36mclosure\u001b[0;34m()\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclosure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoef_spline_bases\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoderators\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfoci_per_voxel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfoci_per_study\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 187\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 188\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/nimare/meta/models.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, coef_spline_bases, moderators, foci_per_voxel, foci_per_study)\u001b[0m\n\u001b[1;32m 866\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 867\u001b[0m \u001b[0mmoderators_coef\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_moderators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 868\u001b[0;31m group_log_l = self._log_likelihood_single_group(\n\u001b[0m\u001b[1;32m 869\u001b[0m \u001b[0mgroup_spatial_coef\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 870\u001b[0m \u001b[0mmoderators_coef\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/nimare/meta/models.py\u001b[0m in \u001b[0;36m_log_likelihood_single_group\u001b[0;34m(self, group_spatial_coef, moderators_coef, coef_spline_bases, group_moderators, group_foci_per_voxel, group_foci_per_study, device)\u001b[0m\n\u001b[1;32m 782\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 783\u001b[0m ):\n\u001b[0;32m--> 784\u001b[0;31m \u001b[0mlog_mu_spatial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoef_spline_bases\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_spatial_coef\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 785\u001b[0m \u001b[0mmu_spatial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_mu_spatial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 786\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmoderators_coef\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRuntimeError\u001b[0m: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument mat2 in method wrapper_CUDA_mm)"
]
}
]
},
{
"cell_type": "code",
"source": [
"plot_stat_map(\n",
" results.get_map(\"spatialIntensity_group-True\"),\n",
" cut_coords=[0, 0, -8],\n",
" draw_cross=False,\n",
" cmap=\"RdBu_r\",\n",
" title=\"Motor\",\n",
" threshold=0.0005, # can be adapted for visualisation purpose\n",
" vmax=1e-3,\n",
")\n",
"plot_stat_map(\n",
" results.get_map(\"spatialIntensity_group-False\"),\n",
" cut_coords=[0, 0, -8],\n",
" draw_cross=False,\n",
" cmap=\"RdBu_r\",\n",
" title=\"Non-Motor\",\n",
" threshold=0.0005, # can be adapted for visualisation purpose\n",
" vmax=1e-3,\n",
")"
],
"metadata": {
"id": "c-nlS1Fqbtmk"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from nimare.meta.cbmr import CBMRInference\n",
"\n",
"inference = CBMRInference(device=\"cuda\")\n",
"inference.fit(result=results)\n",
"t_con_groups = inference.create_contrast(\n",
" [\"True\", \"False\"], source=\"motor\"\n",
")\n",
"contrast_result = inference.transform(t_con_groups=t_con_groups)"
],
"metadata": {
"id": "VsOJtFFDcraI"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Test 2: Language Studies"
],
"metadata": {
"id": "hMpIxVOlYylw"
}
},
{
"cell_type": "code",
"source": [
"# # test 2: language (1101 studies)\n",
"# # compare results to https://neurosynth.org/analyses/terms/language/\n",
"\n",
"# # identify which studies examine langage (functions)\n",
"# language_study_id = neurosynth_dset.get_studies_by_label(labels=[\"terms_abstract_tfidf__language\"])\n",
"# # and those which do not (rest of the studies)\n",
"# nonlanguage_study_id = list(set(neurosynth_dset.ids) - set(language_study_id))\n",
"# # create a subset only containing langage studies\n",
"# language_dset = neurosynth_dset.slice(ids=language_study_id)\n",
"# # and a subset containing no langage studies\n",
"# nonlanguage_dset = neurosynth_dset.slice(ids=nonlanguage_study_id)\n",
"\n",
"# # we can now create dummy variables indicating which study involved language tasks and which did not.\n",
"# neurosynth_dset.annotations[\"language\"] = 'False'\n",
"# neurosynth_dset.annotations.loc[neurosynth_dset.annotations['id'].isin(language_study_id), 'language'] = 'True'"
],
"metadata": {
"id": "XbBr-2hyZKiF"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# # test 2: language (1101 studies)\n",
"# # compare results to https://neurosynth.org/analyses/terms/language/\n",
"# language_study_id = neurosynth_dset.get_studies_by_label(labels=[\"terms_abstract_tfidf__language\"])\n",
"# nonlanguage_study_id = list(set(neurosynth_dset.ids) - set(language_study_id))\n",
"# language_dset = neurosynth_dset.slice(ids=language_study_id)\n",
"# nonlanguage_dset = neurosynth_dset.slice(ids=nonlanguage_study_id)"
],
"metadata": {
"id": "RUfSGzeGCll8"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# # test 3: addiction (135 studies)\n",
"# # compare results to https://neurosynth.org/analyses/terms/addiction/\n",
"# addiction_study_id = neurosynth_dset.get_studies_by_label(labels=[\"terms_abstract_tfidf__addiction\"])\n",
"# nonaddiction_study_id = list(set(neurosynth_dset.ids) - set(addiction_study_id))\n",
"# addiction_dset = neurosynth_dset.slice(ids=addiction_study_id)\n",
"# nonaddiction_dset = neurosynth_dset.slice(ids=nonaddiction_study_id)"
],
"metadata": {
"id": "HxWjNlhhQF7G"
},
"execution_count": null,
"outputs": []
}
]
}
Summary
When fitting a coordinate-based meta-regression model (Google Collab, with correct GPU settings), GPUs are not recruited appropriately. Instead CPUs are used.
Here is the error message: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument mat2 in method wrapper_CUDA_mm)
Additional details
What were you trying to do?
Here is the code:
What did you expect to happen?
GPU usage
What actually happened?
(mainly) CPU usage + error message Yet, GPU availability confirmed with
Reproducing the bug
Some code above, rest of the code is here: https://colab.research.google.com/drive/1EVngYoYlryl-YcjmIEOjm0VBBplc-Qyw?usp=sharing