我正在开发一个应用程序，它接受 2 个输入来更新显示聚类分析结果的散点图。第一个输入通过时间范围滑块过滤图表上的点。第二个输入是一个下拉框，用于突出显示图形上感兴趣类别的颜色。下拉框中可用的类别是对数据的时间范围进行聚类分析后产生的不同聚类。根据选择的时间范围，可能会有不同数量的集群可用。
我希望能够在各种连续的灰色阴影中为未选择的类别着色，并将选定的类别着色为绿色或突出的充满活力的东西。截至目前，我有
的组合color_discrete_map
和
color_continuous_scale
满足这个要求，但是好像不行。 color_discrete_map 参数似乎被 color_continuous_scale 参数否决了。
到目前为止，这是我的代码：
#Import packages
import pandas as pd
import numpy as np
import os
import plotly.express as px
import dash
from dash import dcc, html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
from sklearn.cluster import KMeans
from sklearn.impute import KNNImputer
from kneed import KneeLocator
from sklearn.preprocessing import StandardScaler
import random
cluster_choices = ["Cluster 1","Cluster 2","Cluster 3","Cluster 4","Cluster 5"]
options1 = ['Alabama','Wyoming','California','Tennessee','Texas']
options2 = random.sample(range(1991,2020),20)
options3 = random.sample(range(1000),20)
options4 = random.sample(range(750),20)
options5 = random.sample(range(500),20)
list1 = np.random.choice(options1, size=250, replace=True).tolist()
list2 = np.random.choice(options2, size=250, replace=True).tolist()
list3 = np.random.choice(options3, size=250, replace=True).tolist()
list4 = np.random.choice(options4, size=250, replace=True).tolist()
list5 = np.random.choice(options5, size=250, replace=True).tolist()
df = pd.DataFrame(list(zip(list1, list2, list3, list4, list5)),
columns =['State', 'Year','Metric1','Metric2','Metric3'])
app = dash.Dash(__name__,assets_folder=os.path.join(os.curdir,"assets"))
server = app.server
app.layout = html.Div([
dcc.Tabs([
dcc.Tab(label='Dashboard',
children=[
dbc.Row([
dbc.Col([
dcc.RangeSlider(
id='range_slider',
min=1991,
max=2020,
step=1,
value=[1991, 2020],
allowCross=False,
pushable=2,
tooltip={"placement": "bottom", "always_visible": True}
)
],width=6),
dbc.Col([
dcc.Dropdown(
id='dropdown1',
options=[{'label': i, 'value': i} for i in cluster_choices],
value=cluster_choices[0],
)
],width=6)
]),
dbc.Row([
dbc.Col([
dcc.Graph(id='cluster_graph')
],width=12)
])
]
)
])
])
#Configure reactivity of cluster map controlled by range slider
@app.callback(
Output('cluster_graph', 'figure'),
Output('dropdown1', 'options'),
Input('range_slider', 'value'),
Input('dropdown1','value')
)
def update_cluster_graph(slider_range_values,dd1):
filtered = df[(df['Year']>=slider_range_values[0]) & (df['Year']<=slider_range_values[1])]
X = filtered
#Step 1.) Break out into state and non-state dfs
states = pd.DataFrame(X[['State']])
not_states = X.loc[:, ~X.columns.isin(['State'])]
#Step 2.) Impute the non-text columns
imputer = KNNImputer(n_neighbors=5)
not_states_fixed = pd.DataFrame(imputer.fit_transform(not_states),columns=not_states.columns)
#Step 3.) Perform clustering
scaler = StandardScaler()
data_scaled = scaler.fit_transform(not_states_fixed)
#Step 4.) Define the kmeans function with initialization as k-means++
kmeans = KMeans(n_clusters=3, init='k-means++')
#Step 5.) Fit the k means algorithm on scaled data
kmeans.fit(data_scaled)
SSE = []
for cluster in range(1,10):
kmeans = KMeans(n_clusters = cluster, init='k-means++')
kmeans.fit(data_scaled)
SSE.append(kmeans.inertia_)
kl = KneeLocator(
range(1, 10), SSE, curve="convex", direction="decreasing"
)
#Step 6.) Identify # of clusters
elbow = kl.elbow
kmeans = KMeans(n_clusters = elbow, init='k-means++')
kmeans.fit(data_scaled)
pred = kmeans.predict(data_scaled)
#Step 7.) Get clusters back in original df
frame = pd.DataFrame(data_scaled)
frame['cluster'] = pred
frame['cluster'].value_counts()
clusters = frame['cluster'] +1
not_states_fixed = not_states_fixed.dropna()
not_states_fixed['cluster'] = clusters.values
not_states_fixed['cluster'] = not_states_fixed['cluster'].astype('str')
state_list = states['State'].values.tolist()
not_states_fixed['State'] = state_list
X = not_states_fixed
#This is the filtered list that gets populated in the dropdown box
cluster_list = X['cluster'].unique().tolist()
cluster_list.sort()
label = 'Cluster '
new_cluster_list = [label + x for x in cluster_list]
sortedX = X.sort_values(by='cluster',ascending=True)
sortedX['cluster_num'] = sortedX['cluster'].astype(int)
sortedX['cluster'] = 'Cluster '+sortedX['cluster']
fig = px.scatter(
sortedX,
x="Metric1",
y="Metric2",
color="cluster_num",
color_discrete_map={
f"{dd1}": "green"
},
color_continuous_scale="Greys",
hover_data = {
"State":True,
"Year":True,
"Metric1":True,
"Metric2":True,
"Metric3":True
},
template='plotly_dark'
)
fig.update_traces(marker=dict(size=10,
line=dict(width=0.5,
color='white')),
selector=dict(mode='markers'))
return fig, [{'label':i,'value':i} for i in new_cluster_list]
if __name__=='__main__':
app.run_server()
如何解决上述问题？
您可以通过三种不同的方式更改颜色：
color="cluster_num",
color_discrete_map={
f"{dd1}": "green"
},
color_continuous_scale="Greys",
他们互相矛盾。首先完全删除“color_continuous_scale”和“color_discrete_map”。您似乎想要自定义颜色，而不是预定义颜色。
根据我的经验，最好的方法是添加一个单独的列，为每一行数据定义颜色。这样您就可以完全控制颜色逻辑。
创建一个列来保存每个数据点的颜色值。这可以是十六进制值或 RGBA（如果您还想控制不透明度）。 pandas lambda 函数是执行此操作的简单方法：
# adjust color threshold as needed
def assign_color(value):
float_value = float(value)
if float_value > 2:
return "rgba(250, 0.0, 0.0, 1.0)"
elif float_value > 0 and float_value < 2:
return "rgba(250, 138, 5, 1.0)"
else:
return "rgba(236, 236, 236, 0.6)" # "lightgray"
sortedX['color'] = result.apply(lambda x: assign_color(x["data_column"]), axis = 1)
然后，您应该能够将颜色分配给列的值：
fig = px.scatter(
sortedX,
x="Metric1",
y="Metric2",
color="color",
...
)
编辑： 如果这不起作用，则可能是因为您的标记定义。从上面删除
color="color"marker=dict(color = sortedX["color"],
size = 4)