我正在尝试用 Python 开发 Bokeh 仪表板，我想向其中添加流程挖掘功能。我有使用 bupaR 的经验R 中的包，其中包含非常漂亮的动画流程图，这是我想要在我制作的仪表板中实现的内容。
我看过一些关于如何在 Python 中实现 R 代码的文档，即 r2py , 一些 ggplot在 Bokeh 仪表板等中，但我想要的似乎有点小众，我不确定是否可能。 Python 确实有一个 pm4py扩展到 bupaR但到目前为止，我还没有看到一种在 Bokeh 仪表板中实现类似于 R 中的动画流程图的方法。
只是为了提供某种示例(尽管不相关但仅用于演示目的)，这里是一些用于集群应用程序 Bokeh 仪表板的 Python 代码:-
#https://raw.githubusercontent.com/bokeh/bokeh/master/examples/app/clustering/main.py
import numpy as np
from sklearn import cluster, datasets
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from bokeh.io import curdoc
from bokeh.layouts import column, row
from bokeh.models import ColumnDataSource, Select, Slider
from bokeh.palettes import Spectral6
from bokeh.plotting import figure
np.random.seed(0)
# define some helper functions
def clustering(X, algorithm, n_clusters):
# normalize dataset for easier parameter selection
X = StandardScaler().fit_transform(X)
# estimate bandwidth for mean shift
bandwidth = cluster.estimate_bandwidth(X, quantile=0.3)
# connectivity matrix for structured Ward
connectivity = kneighbors_graph(X, n_neighbors=10, include_self=False)
# make connectivity symmetric
connectivity = 0.5 * (connectivity + connectivity.T)
# Generate the new colors:
if algorithm=='MiniBatchKMeans':
model = cluster.MiniBatchKMeans(n_clusters=n_clusters)
elif algorithm=='Birch':
model = cluster.Birch(n_clusters=n_clusters)
elif algorithm=='DBSCAN':
model = cluster.DBSCAN(eps=.2)
elif algorithm=='AffinityPropagation':
model = cluster.AffinityPropagation(damping=.9,
preference=-200)
elif algorithm=='MeanShift':
model = cluster.MeanShift(bandwidth=bandwidth,
bin_seeding=True)
elif algorithm=='SpectralClustering':
model = cluster.SpectralClustering(n_clusters=n_clusters,
eigen_solver='arpack',
affinity="nearest_neighbors")
elif algorithm=='Ward':
model = cluster.AgglomerativeClustering(n_clusters=n_clusters,
linkage='ward',
connectivity=connectivity)
elif algorithm=='AgglomerativeClustering':
model = cluster.AgglomerativeClustering(linkage="average",
affinity="cityblock",
n_clusters=n_clusters,
connectivity=connectivity)
elif algorithm=='KMeans':
model = cluster.KMeans(n_clusters= n_clusters)
model.fit(X)
if hasattr(model, 'labels_'):
y_pred = model.labels_.astype(int)
else:
y_pred = model.predict(X)
return X, y_pred
def get_dataset(dataset, n_samples):
if dataset == 'Noisy Circles':
return datasets.make_circles(n_samples=n_samples,
factor=0.5,
noise=0.05)
elif dataset == 'Noisy Moons':
return datasets.make_moons(n_samples=n_samples,
noise=0.05)
elif dataset == 'Blobs':
return datasets.make_blobs(n_samples=n_samples,
random_state=8)
elif dataset == "No Structure":
return np.random.rand(n_samples, 2), None
# set up initial data
n_samples = 1500
n_clusters = 2
algorithm = 'MiniBatchKMeans'
dataset = 'Noisy Circles'
X, y = get_dataset(dataset, n_samples)
X, y_pred = clustering(X, algorithm, n_clusters)
spectral = np.hstack([Spectral6] * 20)
colors = [spectral[i] for i in y]
# set up plot (styling in theme.yaml)
plot = figure(toolbar_location=None, title=algorithm)
source = ColumnDataSource(data=dict(x=X[:, 0], y=X[:, 1], colors=colors))
plot.circle('x', 'y', fill_color='colors', line_color=None, source=source)
# set up widgets
clustering_algorithms= [
'MiniBatchKMeans',
'AffinityPropagation',
'MeanShift',
'SpectralClustering',
'Ward',
'AgglomerativeClustering',
'DBSCAN',
'Birch',
'KMeans'
]
datasets_names = [
'Noisy Circles',
'Noisy Moons',
'Blobs',
'No Structure'
]
algorithm_select = Select(value='MiniBatchKMeans',
title='Select algorithm:',
width=200,
options=clustering_algorithms)
dataset_select = Select(value='Noisy Circles',
title='Select dataset:',
width=200,
options=datasets_names)
samples_slider = Slider(title="Number of samples",
value=1500.0,
start=1000.0,
end=3000.0,
step=100,
width=400)
clusters_slider = Slider(title="Number of clusters",
value=2.0,
start=2.0,
end=10.0,
step=1,
width=400)
# set up callbacks
def update_algorithm_or_clusters(attrname, old, new):
global X
algorithm = algorithm_select.value
n_clusters = int(clusters_slider.value)
X, y_pred = clustering(X, algorithm, n_clusters)
colors = [spectral[i] for i in y_pred]
source.data = dict(colors=colors, x=X[:, 0], y=X[:, 1])
plot.title.text = algorithm
def update_samples_or_dataset(attrname, old, new):
global X, y
dataset = dataset_select.value
algorithm = algorithm_select.value
n_clusters = int(clusters_slider.value)
n_samples = int(samples_slider.value)
X, y = get_dataset(dataset, n_samples)
X, y_pred = clustering(X, algorithm, n_clusters)
colors = [spectral[i] for i in y_pred]
source.data = dict(colors=colors, x=X[:, 0], y=X[:, 1])
algorithm_select.on_change('value', update_algorithm_or_clusters)
clusters_slider.on_change('value_throttled', update_algorithm_or_clusters)
dataset_select.on_change('value', update_samples_or_dataset)
samples_slider.on_change('value_throttled', update_samples_or_dataset)
# set up layout
selects = row(dataset_select, algorithm_select, width=420)
inputs = column(selects, samples_slider, clusters_slider)
# add to document
curdoc().add_root(row(inputs, plot))
curdoc().title = "Clustering"
在它下面，我想放来自
bupaR的过程挖掘动画。 :-library(bupaR)
library(processanimateR)
library(eventdataR)
animate_process(patients)
有没有办法在 Bokeh 仪表板中部署这两个功能？或者是否有更直接的替代方法来在更原生于 Python 的 Bokeh 仪表板中创建动画流程图？
最佳答案
是的，您可以在 Bokeh 中创建动画。这实际上就像与小部件的交互。可以使用自动更改值的 add_periodic_callback 功能。
对于您的示例，如果您想更改滑块:>
def animate_update(): # animation only change slider over and over here.
value= slider.value + 1
if value> valuesdict[-1]:
value= valuesdict[0]
slider.value = value
def animate():
global callback_id
callback_id = curdoc().add_periodic_callback(animate_update, 500)
callback_id = None
关于python - 将 bupaR 的动画流程图集成到 Python 中的 Bokeh 仪表板中；有办法吗？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/69250889/