Visualization examples

GTM visualization examples from following datasets:

• S-curve (from sklearn s-curve example)
• Severed sphere (from sklearn severed sphere example)
• Hand-written digits (from sklearn hand-written digits example)

S-curve

from ugtm import eGTM,eGTR
import numpy as np
import altair as alt
import pandas as pd
from sklearn import datasets
from sklearn import metrics
from sklearn import model_selection
from sklearn import manifold

X,y = datasets.make_s_curve(n_samples=1000, random_state=0)
man = manifold.TSNE(n_components=2, init='pca', random_state=0)
tsne = man.fit_transform(X)
man = manifold.MDS(max_iter=100, n_init=1, random_state=0)
mds = man.fit_transform(X)
man = manifold.LocallyLinearEmbedding(n_neighbors=20, n_components=2,
                                      eigen_solver='auto',
                                      method="standard",
                                      random_state=0)
lle = man.fit_transform(X)

# Construct GTM
gtm = eGTM(m=2).fit(X)
gtm_means = gtm.transform(X,model="means")
gtm_modes = gtm.transform(X,model="modes")

dgtm_modes = pd.DataFrame(gtm_modes, columns=["x1", "x2"])
dgtm_modes["label"] = y

gtm_modes = alt.Chart(dgtm_modes).mark_circle().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:Q',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2','label:Q']
).properties(title = "GTM (modes)", width = 100, height = 100)

dgtm_means = pd.DataFrame(gtm_means, columns=["x1", "x2"])
dgtm_means["label"] = y

gtm_means = alt.Chart(dgtm_means).mark_circle().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:Q',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2','label:Q']
).properties(title = "GTM (means)", width = 100, height = 100)

#Construct activity landscape
gtr = eGTR(m=2)
gtr = gtr.fit(X,y)

dfclassmap = pd.DataFrame(gtr.optimizedModel.matX, columns=["x1", "x2"])
dfclassmap["label"] = gtr.node_label

# Classification map
gtr = alt.Chart(dfclassmap).mark_square().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:Q',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2', 'label:Q'],
    #opacity='density'
).properties(title = "GTM landscape",width = 100, height = 100)

dtsne = pd.DataFrame(tsne, columns=["x1", "x2"])
dmds = pd.DataFrame(mds, columns=["x1", "x2"])
dlle = pd.DataFrame(lle, columns=["x1", "x2"])
dtsne["label"] = y
dmds["label"] = y
dlle["label"] = y

tsne = alt.Chart(dtsne).mark_circle().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:Q',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2','label:Q']
).properties(title = "t-SNE", width = 100, height = 100)

mds = alt.Chart(dmds).mark_circle().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:Q',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2','label:Q']
).properties(title = "MDS", width = 100, height = 100)

lle = alt.Chart(dlle).mark_circle().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:Q',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2','label:Q']
).properties(title = "LLE", width = 100, height = 100)


gtm = gtm_means | gtm_modes | gtr
others = tsne | mds | lle

alt.vconcat(gtm, others)

Severed sphere

from ugtm import eGTM,eGTR
import numpy as np
import altair as alt
import pandas as pd
from sklearn import datasets
from sklearn import metrics
from sklearn import model_selection
from sklearn import manifold
from sklearn.utils import check_random_state

random_state = check_random_state(0)
p = random_state.rand(1000) * (2 * np.pi - 0.55)
t = random_state.rand(1000) * np.pi

# Sever the poles from the sphere.
indices = ((t < (np.pi - (np.pi / 8))) & (t > ((np.pi / 8))))
x, y, z = np.sin(t[indices]) * np.cos(p[indices]), \
    np.sin(t[indices]) * np.sin(p[indices]), \
    np.cos(t[indices])

X = np.array([x, y, z]).T

y = p[indices]

man = manifold.TSNE(n_components=2, init='pca', random_state=0)
tsne = man.fit_transform(X)
man = manifold.MDS(max_iter=100, n_init=1, random_state=0)
mds = man.fit_transform(X)
man = manifold.LocallyLinearEmbedding(n_neighbors=10, n_components=2,
                                      eigen_solver='auto',
                                      method="standard",
                                      random_state=0)
lle = man.fit_transform(X)

# Construct GTM
gtm = eGTM(m=2).fit(X)
gtm_means = gtm.transform(X,model="means")
gtm_modes = gtm.transform(X,model="modes")

dgtm_modes = pd.DataFrame(gtm_modes, columns=["x1", "x2"])
dgtm_modes["label"] = y

gtm_modes = alt.Chart(dgtm_modes).mark_circle().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:Q',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2','label:Q']
).properties(title = "GTM (modes)", width = 100, height = 100)

dgtm_means = pd.DataFrame(gtm_means, columns=["x1", "x2"])
dgtm_means["label"] = y

gtm_means = alt.Chart(dgtm_means).mark_circle().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:Q',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2','label:Q']
).properties(title = "GTM (means)", width = 100, height = 100)

#Construct activity landscape
gtr = eGTR(m=2)
gtr = gtr.fit(X,y)

dfclassmap = pd.DataFrame(gtr.optimizedModel.matX, columns=["x1", "x2"])
dfclassmap["label"] = gtr.node_label

# Classification map
gtr = alt.Chart(dfclassmap).mark_square().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:Q',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2', 'label:Q'],
    #opacity='density'
).properties(title = "GTM landscape",width = 100, height = 100)

dtsne = pd.DataFrame(tsne, columns=["x1", "x2"])
dmds = pd.DataFrame(mds, columns=["x1", "x2"])
dlle = pd.DataFrame(lle, columns=["x1", "x2"])
dtsne["label"] = y
dmds["label"] = y
dlle["label"] = y

tsne = alt.Chart(dtsne).mark_circle().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:Q',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2','label:Q']
).properties(title = "t-SNE", width = 100, height = 100)

mds = alt.Chart(dmds).mark_circle().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:Q',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2','label:Q']
).properties(title = "MDS", width = 100, height = 100)

lle = alt.Chart(dlle).mark_circle().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:Q',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2','label:Q']
).properties(title = "LLE", width = 100, height = 100)


gtm = gtm_means | gtm_modes | gtr
others = tsne | mds | lle

alt.vconcat(gtm, others)

Hand-written digits

from ugtm import eGTM,eGTC
import numpy as np
import altair as alt
import pandas as pd
from sklearn import datasets
from sklearn import metrics
from sklearn import model_selection
from sklearn import manifold
from sklearn.utils import check_random_state


digits = datasets.load_digits(n_class=6)
X = digits.data
y = digits.target

man = manifold.TSNE(n_components=2, init='pca', random_state=0)
tsne = man.fit_transform(X)
man = manifold.MDS(max_iter=100, n_init=1, random_state=0)
mds = man.fit_transform(X)
man = manifold.LocallyLinearEmbedding(n_neighbors=20, n_components=2,
                                      eigen_solver='auto',
                                      method="standard",
                                      random_state=0)
lle = man.fit_transform(X)

# Construct GTM
gtm = eGTM().fit(X)
gtm_means = gtm.transform(X,model="means")
gtm_modes = gtm.transform(X,model="modes")

dgtm_modes = pd.DataFrame(gtm_modes, columns=["x1", "x2"])
dgtm_modes["label"] = y

gtm_modes = alt.Chart(dgtm_modes).mark_circle().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:N',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2','label:N']
).properties(title = "GTM (modes)", width = 100, height = 100)

dgtm_means = pd.DataFrame(gtm_means, columns=["x1", "x2"])
dgtm_means["label"] = y

gtm_means = alt.Chart(dgtm_means).mark_circle().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:N',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2','label:N']
).properties(title = "GTM (means)", width = 100, height = 100)

#Construct activity landscape
gtc = eGTC()
gtc = gtc.fit(X,y)

dfclassmap = pd.DataFrame(gtc.optimizedModel.matX, columns=["x1", "x2"])
dfclassmap["label"] = gtc.node_label

# Classification map
gtc = alt.Chart(dfclassmap).mark_square().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:N',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2', 'label:N'],
    #opacity='density'
).properties(title = "GTM class map",width = 100, height = 100)

dtsne = pd.DataFrame(tsne, columns=["x1", "x2"])
dmds = pd.DataFrame(mds, columns=["x1", "x2"])
dlle = pd.DataFrame(lle, columns=["x1", "x2"])
dtsne["label"] = digits.target_names[y]
dmds["label"] = digits.target_names[y]
dlle["label"] = digits.target_names[y]

tsne = alt.Chart(dtsne).mark_circle().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:N',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2','label:N']
).properties(title = "t-SNE", width = 100, height = 100)

mds = alt.Chart(dmds).mark_circle().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:N',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2','label:N']
).properties(title = "MDS", width = 100, height = 100)

lle = alt.Chart(dlle).mark_circle().encode(
    x='x1',
    y='x2',
    color=alt.Color('label:N',
                    scale=alt.Scale(scheme='viridis')),
    size=alt.value(50),
    tooltip=['x1','x2','label:N']
).properties(title = "LLE", width = 100, height = 100)


gtm = gtm_means | gtm_modes | gtc
others = tsne | mds | lle

alt.vconcat(gtm, others)