2.4 Select and Train#
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()
A = As[0]
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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)]
print("Purging {:d} nodes...".format((degree == 0).sum()))
return A_purged
class CleanData(BaseEstimator, TransformerMixin):
def fit(self, X):
return self
def transform(self, X):
print("Cleaning data...")
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):
print("Scaling edge-weights...")
A_scaled = pass_to_ranks(X)
return (A_scaled)
num_pipeline = Pipeline([
('cleaner', CleanData()),
('scaler', FeatureScaler()),
])
A_xfm = num_pipeline.fit_transform(A)
Cleaning data...
Purging 0 nodes...
Scaling edge-weights...
from graspologic.embed import AdjacencySpectralEmbed
embedding = AdjacencySpectralEmbed(n_components=3, svd_seed=0).fit_transform(A_xfm)
from graspologic.plot import pairplot
from matplotlib import pyplot as plt
fig = pairplot(embedding, title="(A) Spectral Embedding for connectome")
fig.tight_layout()
fname = "pairplots0"
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")
/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)
from sklearn.cluster import KMeans
labels = KMeans(n_clusters=2, random_state=0).fit_predict(embedding)
fig = pairplot(embedding, labels=labels, legend_name="Predicter Clusters",
title="(B) KMeans clustering")
fig.tight_layout()
fname = "pairplots1"
if mode != "png":
fig.savefig(f"Figures/{mode:s}/{fname:s}.{mode:s}")
fig.savefig(f"Figures/png/{fname:s}.png")
import matplotlib.image as mpimg
fig, axs = plt.subplots(1, 2, figsize=(20, 10), gridspec_kw={"width_ratios": [1, 1.39]})
axs[0].imshow(mpimg.imread('Figures/png/pairplots0.png'))
axs[1].imshow(mpimg.imread('Figures/png/pairplots1.png'))
# turn off x and y axis
[ax.set_axis_off() for ax in axs.ravel()];
fig.tight_layout()
fname = "pairplots"
if mode != "png":
fig.savefig(f"Figures/{mode:s}/{fname:s}.{mode:s}")
fig.savefig(f"Figures/png/{fname:s}.png")
from graspologic.cluster import KMeansCluster
labels = KMeansCluster(max_clusters=10, random_state=0).fit_predict(embedding)
fig = pairplot(embedding, labels=labels, title="(A) KMeans clustering, automatic selection",
legend_name="Predicted Clusters")
fig.tight_layout()
fname = "pairplot_impute0"
if mode != "png":
fig.savefig(f"Figures/{mode:s}/{fname:s}.{mode:s}")
fig.savefig(f"Figures/png/{fname:s}.png")
from graspologic.cluster import AutoGMMCluster
labels = AutoGMMCluster(max_components=10, random_state=0).fit_predict(embedding)
fig = pairplot(embedding, labels=labels, title="(B) AutoGMM Clustering, automatic selection",
legend_name="Predicted Clusters")
fig.tight_layout()
fname = "pairplot_impute1"
if mode != "png":
fig.savefig(f"Figures/{mode:s}/{fname:s}.{mode:s}")
fig.savefig(f"Figures/png/{fname:s}.png")
fig, axs = plt.subplots(1, 2, figsize=(20, 10), gridspec_kw={"width_ratios": [1, 1]})
axs[0].imshow(mpimg.imread('Figures/png/pairplot_impute0.png'))
axs[1].imshow(mpimg.imread('Figures/png/pairplot_impute1.png'))
# turn off x and y axis
[ax.set_axis_off() for ax in axs.ravel()]
fig.tight_layout()
fname = "pairplot_impute"
if mode != "png":
fig.savefig(f"Figures/{mode:s}/{fname:s}.{mode:s}")
fig.savefig(f"Figures/png/{fname:s}.png")
