2.5 and 2.6 Fine tune, discover, and visualize#
mode = "svg" # output format for figs
import matplotlib
font = {'family' : 'Dejavu Sans',
'weight' : 'normal',
'size' : 20}
matplotlib.rc('font', **font)
import matplotlib
from matplotlib import pyplot as plt
import os
import urllib
import boto3
from botocore import UNSIGNED
from botocore.client import Config
from graspologic.utils import import_edgelist
import numpy as np
import glob
from tqdm import tqdm
# the AWS bucket the data is stored in
BUCKET_ROOT = "open-neurodata"
parcellation = "Schaefer400"
FMRI_PREFIX = "m2g/Functional/BNU1-11-12-20-m2g-func/Connectomes/" + parcellation + "_space-MNI152NLin6_res-2x2x2.nii.gz/"
FMRI_PATH = os.path.join("datasets", "fmri") # the output folder
DS_KEY = "abs_edgelist" # correlation matrices for the networks to exclude
def fetch_fmri_data(bucket=BUCKET_ROOT, fmri_prefix=FMRI_PREFIX,
output=FMRI_PATH, name=DS_KEY):
"""
A function to fetch fMRI connectomes from AWS S3.
"""
# check that output directory exists
if not os.path.isdir(FMRI_PATH):
os.makedirs(FMRI_PATH)
# start boto3 session anonymously
s3 = boto3.client('s3', config=Config(signature_version=UNSIGNED))
# obtain the filenames
bucket_conts = s3.list_objects(Bucket=bucket,
Prefix=fmri_prefix)["Contents"]
for s3_key in tqdm(bucket_conts):
# get the filename
s3_object = s3_key['Key']
# verify that we are grabbing the right file
if name not in s3_object:
op_fname = os.path.join(FMRI_PATH, str(s3_object.split('/')[-1]))
if not os.path.exists(op_fname):
s3.download_file(bucket, s3_object, op_fname)
def read_fmri_data(path=FMRI_PATH):
"""
A function which loads the connectomes as adjacency matrices.
"""
fnames = glob.glob(os.path.join(path, "*.csv"))
fnames.sort()
# import edgelists with graspologic
# edgelists will be all of the files that end in a csv
networks = [import_edgelist(fname) for fname in tqdm(fnames)]
return np.stack(networks, axis=0)
fetch_fmri_data()
As = read_fmri_data()
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from sklearn.base import TransformerMixin, BaseEstimator
from sklearn.pipeline import Pipeline
from graspologic.utils import pass_to_ranks
def remove_isolates(A):
"""
A function which removes isolated nodes from the
adjacency matrix A.
"""
degree = A.sum(axis=0) # sum along the rows to obtain the node degree
out_degree = A.sum(axis=1)
A_purged = A[~(degree == 0),:]
A_purged = A_purged[:,~(degree == 0)]
return A_purged
class CleanData(BaseEstimator, TransformerMixin):
def fit(self, X):
return self
def transform(self, X):
Acleaned = remove_isolates(X)
A_abs_cl = np.abs(Acleaned)
self.A_ = A_abs_cl
return self.A_
class FeatureScaler(BaseEstimator, TransformerMixin):
def fit(self, X):
return self
def transform(self, X):
A_scaled = pass_to_ranks(X)
return (A_scaled)
num_pipeline = Pipeline([
('cleaner', CleanData()),
('scaler', FeatureScaler()),
])
As_xfm = [num_pipeline.fit_transform(A) for A in As]
from graspologic.embed import MultipleASE
from graspologic.plot import pairplot
from matplotlib import pyplot as plt
embedding = MultipleASE(n_components=5, svd_seed=0).fit_transform(As_xfm)
fig = pairplot(embedding, title="(A) Multiple spectral embedding of all connectomes")
fig.tight_layout()
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1513: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=vector, **plot_kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1513: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=vector, **plot_kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1513: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=vector, **plot_kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1513: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=vector, **plot_kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1513: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=vector, **plot_kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/opt/hostedtoolcache/Python/3.12.5/x64/lib/python3.12/site-packages/seaborn/axisgrid.py:1615: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
<seaborn.axisgrid.PairGrid at 0x7f2654ccd040>
from graspologic.cluster import AutoGMMCluster
labels = AutoGMMCluster(max_components=10, random_state=0).fit_predict(embedding)
fig = pairplot(embedding, labels=labels,
title="Multiple spectral embedding of all connectomes",
legend_name="Predicted Clusters")
fig.tight_layout()
fname = "mase"
if mode != "png":
os.makedirs(f"Figures/{mode:s}", exist_ok=True)
fig.savefig(f"Figures/{mode:s}/{fname:s}.{mode:s}")
os.makedirs("Figures/png", exist_ok=True)
fig.savefig(f"Figures/png/{fname:s}.png")
from urllib import request
import json
import pandas as pd
from pathlib import Path
coord_dest = os.path.join(FMRI_PATH, "coordinates.json")
request.urlretrieve("https://github.com/neurodata/neuroparc/" + "raw/master/atlases/label/Human/Metadata-json/" + parcellation + "_space-MNI152NLin6_res-2x2x2.json", coord_dest)
with open(coord_dest) as coord_f:
coords = []
for roiname, contents in json.load(coord_f)["rois"].items():
try:
if roiname != "0":
coord_roi = {"x" : contents["center"][0], "y" : contents["center"][1], "z" : contents["center"][2]}
coords.append(coord_roi)
except:
continue
Path("./Images/").mkdir(parents=True, exist_ok=True)
request.urlretrieve("https://github.com/neurodata/graph-stats-book/" +
"blob/master/network_machine_learning_in_python/" +
"foundations/ch2/Images/lobes.png?raw=true", "./Images/lobes.png")
coords_df = pd.DataFrame(coords)
import matplotlib.image as mpimg
import seaborn as sns
coords_df["Community"] = labels
coords_df['Community'] = coords_df['Community'].astype('category')
fig, axs = plt.subplots(1, 2, figsize=(18, 6))
axs[0].imshow(mpimg.imread('./Images/lobes.png'))
axs[0].set_axis_off()
sns.scatterplot(x="y", y="z", data=coords_df, hue="Community", ax=axs[1])
axs[1].set_ylabel("Bottom to Top (mm)")
axs[1].set_xlabel("Back to Front (mm)")
axs[1].legend(title="Predicted Community", bbox_to_anchor=(1, 1))
fig.tight_layout()
fname = "brain_preds"
if mode != "png":
fig.savefig(f"Figures/{mode:s}/{fname:s}.{mode:s}")
fig.savefig(f"Figures/png/{fname:s}.png")
import contextlib
import datasets.dice as dice
from sklearn.metrics import confusion_matrix
from graphbook_code import cmaps
group_dest = os.path.join("./datasets/", "Yeo-7_space-MNI152NLin6_res-2x2x2.nii.gz")
request.urlretrieve("https://github.com/neurodata/neuroparc/" + "blob/master/atlases/label/Human/" +
"Yeo-7_space-MNI152NLin6_res-2x2x2.nii.gz?raw=true", group_dest)
roi_dest = os.path.join("./datasets/", parcellation + "_space-MNI152NLin6_res-2x2x2.nii.gz")
request.urlretrieve("https://github.com/neurodata/neuroparc/" + "blob/master/atlases/label/Human/" +
parcellation + "_space-MNI152NLin6_res-2x2x2.nii.gz?raw=true", roi_dest)
dicemap, _, _ = dice.dice_roi("./datasets/", "./datasets",
"Yeo-7_space-MNI152NLin6_res-2x2x2.nii.gz",
parcellation + "_space-MNI152NLin6_res-2x2x2.nii.gz",
verbose=False)
actual_cluster = np.argmax(dicemap, axis=0)[1:] - 1
# make confusion matrix
cf_matrix = confusion_matrix(actual_cluster, labels)
# and plot it
fig, ax = plt.subplots(1,1, figsize=(5, 4))
sns.heatmap(cf_matrix, cmap=cmaps["sequential"], ax=ax)
ax.set_title("Confusion matrix")
ax.set_ylabel("True Parcel")
ax.set_xlabel("Predicted Community")
ax.set_xlim((0, 4))
fig.tight_layout()
fname = "cf_mtx"
if mode != "png":
fig.savefig(f"Figures/{mode:s}/{fname:s}.{mode:s}")
fig.savefig(f"Figures/png/{fname:s}.png")
