from bokeh.plotting import figure, output_file, show
from bokeh.models import CategoricalColorMapper, ColumnDataSource
from bokeh.palettes import Category10

import umap
from sklearn.datasets import load_iris

iris = load_iris()
embedding = umap.UMAP(
    n_neighbors=50, learning_rate=0.5, init="random", min_dist=0.001
).fit_transform(iris.data)

output_file("iris.html")


targets = [str(d) for d in iris.target_names]

source = ColumnDataSource(
    dict(
        x=[e[0] for e in embedding],
        y=[e[1] for e in embedding],
        label=[targets[d] for d in iris.target],
    )
)

cmap = CategoricalColorMapper(factors=targets, palette=Category10[10])

p = figure(title="Test UMAP on Iris dataset")
p.circle(
    x="x",
    y="y",
    source=source,
    color={"field": "label", "transform": cmap},
    legend="label",
)

show(p)

e7513e6403d928953ae1b41f49a3e9e200d6730a
<mask token>

<mask token>
output_file('iris.html')
<mask token>
p.circle(x='x', y='y', source=source, color={'field': 'label', 'transform':
    cmap}, legend='label')
show(p)

<mask token>
iris = load_iris()
embedding = umap.UMAP(n_neighbors=50, learning_rate=0.5, init='random',
    min_dist=0.001).fit_transform(iris.data)
output_file('iris.html')
targets = [str(d) for d in iris.target_names]
source = ColumnDataSource(dict(x=[e[0] for e in embedding], y=[e[1] for e in
    embedding], label=[targets[d] for d in iris.target]))
cmap = CategoricalColorMapper(factors=targets, palette=Category10[10])
p = figure(title='Test UMAP on Iris dataset')
p.circle(x='x', y='y', source=source, color={'field': 'label', 'transform':
    cmap}, legend='label')
show(p)

from bokeh.plotting import figure, output_file, show
from bokeh.models import CategoricalColorMapper, ColumnDataSource
from bokeh.palettes import Category10
import umap
from sklearn.datasets import load_iris
iris = load_iris()
embedding = umap.UMAP(n_neighbors=50, learning_rate=0.5, init='random',
    min_dist=0.001).fit_transform(iris.data)
output_file('iris.html')
targets = [str(d) for d in iris.target_names]
source = ColumnDataSource(dict(x=[e[0] for e in embedding], y=[e[1] for e in
    embedding], label=[targets[d] for d in iris.target]))
cmap = CategoricalColorMapper(factors=targets, palette=Category10[10])
p = figure(title='Test UMAP on Iris dataset')
p.circle(x='x', y='y', source=source, color={'field': 'label', 'transform':
    cmap}, legend='label')
show(p)

from bokeh.plotting import figure, output_file, show
from bokeh.models import CategoricalColorMapper, ColumnDataSource
from bokeh.palettes import Category10

import umap
from sklearn.datasets import load_iris

iris = load_iris()
embedding = umap.UMAP(
    n_neighbors=50, learning_rate=0.5, init="random", min_dist=0.001
).fit_transform(iris.data)

output_file("iris.html")


targets = [str(d) for d in iris.target_names]

source = ColumnDataSource(
    dict(
        x=[e[0] for e in embedding],
        y=[e[1] for e in embedding],
        label=[targets[d] for d in iris.target],
    )
)

cmap = CategoricalColorMapper(factors=targets, palette=Category10[10])

p = figure(title="Test UMAP on Iris dataset")
p.circle(
    x="x",
    y="y",
    source=source,
    color={"field": "label", "transform": cmap},
    legend="label",
)

show(p)
