from dyneusr import DyNeuGraph from dyneusr.datasets import make_trefoil from kmapper import KeplerMapper # Generate synthetic dataset dataset = make_trefoil(size=100) X = dataset.data y = dataset.target # Generate shape graph using KeplerMapper mapper = KeplerMapper(verbose=1) lens = mapper.fit_transform(X, projection=[0]) graph = mapper.map(lens, X, nr_cubes=6, overlap_perc=0.2) # Convert to a DyNeuGraph dG = DyNeuGraph(G=graph, y=y) # Define some custom_layouts dG.add_custom_layout(lens, name='lens') dG.add_custom_layout(nx.spring_layout, name='nx.spring') dG.add_custom_layout(nx.kamada_kawai_layout, name='nx.kamada_kawai') dG.add_custom_layout(nx.spectral_layout, name='nx.spectral') dG.add_custom_layout(nx.circular_layout, name='nx.circular') # Configure some projections pca = PCA(2, random_state=1) tsne = TSNE(2, init='pca', random_state=1) umap = UMAP(n_components=2, init=pca.fit_transform(X)) # Add projections as custom_layouts dG.add_custom_layout(pca.fit_transform(X), name='PCA')
X = masker.fit_transform(dataset.func[0])
# Encode labels as integers
df = pd.read_csv(dataset.session_target[0], sep=" ")
target, labels = pd.factorize(df.labels.values)
y = pd.DataFrame({l: (target == i).astype(int) for i, l in enumerate(labels)})
# Generate shape graph using KeplerMapper
mapper = KeplerMapper(verbose=1)
lens = mapper.fit_transform(X, projection=TSNE(2, random_state=1))
graph = mapper.map(lens, X=X, cover=Cover(20, 0.5), clusterer=DBSCAN(eps=20.))
# Visualize the stages of Mapper
fig, axes = visualize_mapper_stages(dataset,
y=target,
lens=lens,
graph=graph,
cover=mapper.cover,
node_size=20,
edge_size=0.5,
edge_color='gray',
layout="kamada_kawai",
figsize=(16, 4))
plt.savefig("mapper_stages.png", dpi=600, background='transparent')
plt.show()
# Visualize the shape graph using DyNeuSR's DyNeuGraph
dG = DyNeuGraph(G=graph, y=y)
dG.visualize('dyneusr_haxby_decoding.html', port=8800)
webbrowser.open(dG.HTTP.url)
import numpy as np
from dyneusr import DyNeuGraph
from dyneusr.datasets import make_trefoil
from kmapper import KeplerMapper
from sklearn.decomposition import PCA
# Generate synthetic dataset
import tadasets
X = tadasets.sphere(n=500, r=1)
# Sort by first column
inds = np.argsort(X[:, 0])
X = X[inds].copy()
y = np.arange(X.shape[0])
# Generate shape graph using KeplerMapper
mapper = KeplerMapper(verbose=1)
lens = mapper.fit_transform(X, projection=PCA(2))
graph = mapper.map(lens, X, nr_cubes=6, overlap_perc=0.5)
# Visualize the shape graph using DyNeuSR's DyNeuGraph
dG = DyNeuGraph(G=graph, y=y)
dG.visualize('dyneusr2D_sphere.html', template='2D', static=True, show=True)
X = masker.fit_transform(dataset.func[0])
# Encode labels as integers
df = pd.read_csv(dataset.session_target[0], sep=" ")
target, labels = pd.factorize(df.labels.values)
y = pd.DataFrame({l:(target==i).astype(int) for i,l in enumerate(labels)})
# Generate shape graph using KeplerMapper
mapper = KeplerMapper(verbose=1)
lens = mapper.fit_transform(X, projection=TSNE(2, random_state=1))
graph = mapper.map(
lens, X=X,
cover=Cover(20, 0.5),
clusterer=DBSCAN(eps=20.)
)
dG = DyNeuGraph(G=graph, y=y)
# Define some custom_layouts
import networkx as nx
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
dG.add_custom_layout(nx.spring_layout, name='nx.spring')
dG.add_custom_layout(nx.kamada_kawai_layout, name='nx.kamada_kawai')
dG.add_custom_layout(nx.spectral_layout, name='nx.spectral')
dG.add_custom_layout(nx.circular_layout, name='nx.circular')
dG.add_custom_layout(PCA(2).fit_transform(X), name='PCA')
dG.add_custom_layout(TSNE(2).fit_transform(X), name='TSNE')
# Visualize
dG.visualize('dyneusr_custom_layouts.html', static=True, show=True)
memory="nilearn_cache"
)
X = masker.fit_transform(dataset.func[0])
# Encode labels as integers
df = pd.read_csv(dataset.session_target[0], sep=" ")
target, labels = pd.factorize(df.labels.values)
y = pd.DataFrame({l:(target==i).astype(int) for i,l in enumerate(labels)})
# Generate shape graph using KeplerMapper
mapper = KeplerMapper(verbose=1)
lens = mapper.fit_transform(X, projection=TSNE(2, random_state=1))
graph = mapper.map(
lens, X=X,
cover=Cover(20, 0.5),
clusterer=DBSCAN(eps=20.)
)
# Visualize the shape graph using DyNeuSR's DyNeuGraph
dG = DyNeuGraph(G=graph, y=y)
dG.visualize('dyneusr_haxby_decoding.html', static=True, show=True)
# Visualize the stages of Mapper
fig, axes = visualize_mapper_stages(
dataset, y=target, lens=lens, graph=graph, cover=mapper.cover,
node_size=20, edge_size=0.5, edge_color='gray',
layout="kamada_kawai", figsize=(16, 4)
)
plt.savefig("mapper_stages.png", dpi=600, background='transparent')
plt.show()
from dyneusr import DyNeuGraph
from dyneusr.datasets import make_trefoil
from kmapper import KeplerMapper
# Generate synthetic dataset
dataset = make_trefoil(size=100)
X = dataset.data
y = dataset.target
# Generate shape graph using KeplerMapper
mapper = KeplerMapper(verbose=1)
lens = mapper.fit_transform(X, projection=[0])
graph = mapper.map(lens, X, nr_cubes=6, overlap_perc=0.2)
# Visualize the shape graph using DyNeuSR's DyNeuGraph
dG = DyNeuGraph(G=graph, y=y)
dG.visualize('dyneusr_trefoil_knot.html', static=True, show=True)
import webbrowser
from dyneusr import DyNeuGraph
from dyneusr.datasets import make_trefoil
from kmapper import KeplerMapper
# Generate synthetic dataset
dataset = make_trefoil(size=100)
X = dataset.data
y = dataset.target
# Generate shape graph using KeplerMapper
mapper = KeplerMapper(verbose=1)
lens = mapper.fit_transform(X, projection=[0])
graph = mapper.map(lens, X, nr_cubes=6, overlap_perc=0.2)
# Visualize the shape graph using DyNeuSR's DyNeuGraph
dG = DyNeuGraph(G=graph, y=y)
dG.visualize('dyneusr_trefoil_knot.html')
# Explore/interact with the visualization in your browser
webbrowser.open(dG.HTTP.url)
from dyneusr import DyNeuGraph
from nilearn.datasets import fetch_haxby
from nilearn.input_data import NiftiMasker
from kmapper import KeplerMapper, Cover
from sklearn.manifold import TSNE
from sklearn.cluster import DBSCAN
# Fetch dataset, extract time-series from ventral temporal (VT) mask
dataset = fetch_haxby()
masker = NiftiMasker(
dataset.mask_vt[0],
standardize=True, detrend=True, smoothing_fwhm=4.0,
low_pass=0.09, high_pass=0.008, t_r=2.5,
memory="nilearn_cache")
X = masker.fit_transform(dataset.func[0])
# Encode labels as integers
df = pd.read_csv(dataset.session_target[0], sep=" ")
target, labels = pd.factorize(df.labels.values)
y = pd.DataFrame({l:(target==i).astype(int) for i,l in enumerate(labels)})
# Generate shape graph using KeplerMapper
mapper = KeplerMapper(verbose=1)
lens = mapper.fit_transform(X, projection=TSNE(2))
graph = mapper.map(lens, X, cover=Cover(20, 0.5), clusterer=DBSCAN(eps=20.))
# Visualize the shape graph using DyNeuSR's DyNeuGraph
dG = DyNeuGraph(G=graph, y=y)
dG.visualize('haxby/haxby_output.html')
from dyneusr import DyNeuGraph from dyneusr.datasets import make_trefoil from kmapper import KeplerMapper # Generate synthetic dataset dataset = make_trefoil(size=100) X = dataset.data y = dataset.target # Generate shape graph using KeplerMapper mapper = KeplerMapper(verbose=1) lens = mapper.fit_transform(X, projection=[0]) graph = mapper.map(lens, X, nr_cubes=6, overlap_perc=0.2) # Convert to a DyNeuGraph, visualize dG = DyNeuGraph(G=graph, y=y) dG.visualize(static=True, show=True)
import pandas as pd
from dyneusr import DyNeuGraph
from nilearn.datasets import fetch_miyawaki2008
from nilearn.input_data import MultiNiftiMasker
from kmapper import KeplerMapper, Cover
from sklearn.manifold import TSNE
from sklearn.cluster import DBSCAN
# Fetch dataset, extract time-series from ventral temporal (VT) mask
dataset = fetch_miyawaki2008()
masker = MultiNiftiMasker(mask_img=dataset.mask,
detrend=True,
standardize=False)
X = masker.fit_transform(dataset.func[0])
# Encode labels as integers
#df = pd.read_csv(dataset.session_target[0], sep=" ")
#target, labels = pd.factorize(df.labels.values)
#y = pd.DataFrame({l:(target==i).astype(int) for i,l in enumerate(labels)})
# Generate shape graph using KeplerMapper
mapper = KeplerMapper(verbose=1)
lens = mapper.fit_transform(X, projection=TSNE(2))
graph = mapper.map(lens, X, cover=Cover(20, 0.5), clusterer=DBSCAN(eps=20.))
# Visualize the shape graph using DyNeuSR's DyNeuGraph
dG = DyNeuGraph(G=graph)
dG.visualize('output.html')
