[英]python bokeh, how to make a correlation plot?
如何在散景中制作相关热图?
import pandas as pd
import bokeh.charts
df = pd.util.testing.makeTimeDataFrame(1000)
c = df.corr()
p = bokeh.charts.HeatMap(c) # not right
# try to make it a long form
# (and it's ugly in pandas to use 'index' in melt)
c['x'] = c.index
c = pd.melt(c, 'x', ['A','B','C','D'])
# this shows the right 4x4 matrix, but values are still wrong
p = bokeh.charts.HeatMap(c, x = 'x', y = 'variable', values = 'value')
顺便说一句,我可以在侧面制作一个颜色条,而不是情节中的图例吗? 以及如何选择颜色范围/映射,例如深蓝色(-1)到白色(0)到深红色(+1)?
所以我想我可以提供一个基线代码来帮助你使用上面的答案和一些额外的预处理的组合来完成你的要求。
假设您已经加载了一个数据帧df (在本例中为UCI Adult Data )并计算了相关系数( p_corr )。
import bisect
#
from math import pi
from numpy import arange
from itertools import chain
from collections import OrderedDict
#
from bokeh.palettes import RdBu as colors # just make sure to import a palette that centers on white (-ish)
from bokeh.models import ColorBar, LinearColorMapper
colors = list(reversed(colors[9])) # we want an odd number to ensure 0 correlation is a distinct color
labels = df.columns
nlabels = len(labels)
def get_bounds(n):
"""Gets bounds for quads with n features"""
bottom = list(chain.from_iterable([[ii]*nlabels for ii in range(nlabels)]))
top = list(chain.from_iterable([[ii+1]*nlabels for ii in range(nlabels)]))
left = list(chain.from_iterable([list(range(nlabels)) for ii in range(nlabels)]))
right = list(chain.from_iterable([list(range(1,nlabels+1)) for ii in range(nlabels)]))
return top, bottom, left, right
def get_colors(corr_array, colors):
"""Aligns color values from palette with the correlation coefficient values"""
ccorr = arange(-1, 1, 1/(len(colors)/2))
color = []
for value in corr_array:
ind = bisect.bisect_left(ccorr, value)
color.append(colors[ind-1])
return color
p = figure(plot_width=600, plot_height=600,
x_range=(0,nlabels), y_range=(0,nlabels),
title="Correlation Coefficient Heatmap (lighter is worse)",
toolbar_location=None, tools='')
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.xaxis.major_label_orientation = pi/4
p.yaxis.major_label_orientation = pi/4
top, bottom, left, right = get_bounds(nlabels) # creates sqaures for plot
color_list = get_colors(p_corr.values.flatten(), colors)
p.quad(top=top, bottom=bottom, left=left,
right=right, line_color='white',
color=color_list)
# Set ticks with labels
ticks = [tick+0.5 for tick in list(range(nlabels))]
tick_dict = OrderedDict([[tick, labels[ii]] for ii, tick in enumerate(ticks)])
# Create the correct number of ticks for each axis
p.xaxis.ticker = ticks
p.yaxis.ticker = ticks
# Override the labels
p.xaxis.major_label_overrides = tick_dict
p.yaxis.major_label_overrides = tick_dict
# Setup color bar
mapper = LinearColorMapper(palette=colors, low=-1, high=1)
color_bar = ColorBar(color_mapper=mapper, location=(0, 0))
p.add_layout(color_bar, 'right')
show(p)
如果类别是整数编码的,这将导致以下图(这是一个可怕的数据示例):
在现代散景中,您应该使用bokeh.plotting
界面。 您可以在图库中看到使用此界面生成的分类热图示例:
http://docs.bokeh.org/en/latest/docs/gallery/categorical.html
关于图例,对于这样的颜色图,您实际上需要一个离散的ColorBar
而不是Legend
。 这是一项新功能,将出现在本周晚些时候(今天的日期:2016-08-28)即将发布的0.12.2
版本中。 这些新的颜色条注释可以位于主绘图区域之外。
GitHub repo 中还有一个示例:
https://github.com/bokeh/bokeh/blob/master/examples/plotting/file/color_data_map.py
请注意,最后一个示例还使用另一个新功能在浏览器中进行颜色映射,而不必在 python 中预先计算颜色。 基本上所有在一起看起来像:
# create a color mapper with your palette - can be any list of colors
mapper = LinearColorMapper(palette=Viridis3, low=0, high=100)
p = figure(toolbar_location=None, tools='', title=title)
p.circle(
x='x', y='y', source=source
# use the mapper to colormap according to the 'z' column (in the browser)
fill_color={'field': 'z', 'transform': mapper},
)
# create a ColorBar and addit to the side of the plot
color_bar = ColorBar(color_mapper=mapper, location=(0, 0))
p.add_layout(color_bar, 'right')
还有更复杂的选项,例如,如果您想更仔细地控制颜色栏上的刻度,您可以像在普通Axis
上一样添加自定义代码或刻度格式,以实现以下目的:
目前尚不清楚您的实际需求是什么,所以我只是提到这一点,以防万一它是有用的。
最后,Bokeh 是一个大型项目,找到最好的方法通常需要询问更多信息和背景,一般来说,进行讨论。 这种协作帮助似乎对 SO 不屑一顾(它们“不是真正的答案”),因此我鼓励您也随时查看项目 Discourse以获得帮助。
我尝试使用 Bokeh 库创建交互式相关图。 该代码是 SO 和其他网站上可用的不同解决方案的组合。 在上面的解决方案中 bigreddot 已经详细解释了事情。 相关热图代码如下:
import pandas as pd
from bokeh.io import output_file, show
from bokeh.models import BasicTicker, ColorBar, LinearColorMapper, ColumnDataSource, PrintfTickFormatter
from bokeh.plotting import figure
from bokeh.transform import transform
from bokeh.palettes import Viridis3, Viridis256
# Read your data in pandas dataframe
data = pd.read_csv(%%%%%Your Path%%%%%)
#Now we will create correlation matrix using pandas
df = data.corr()
df.index.name = 'AllColumns1'
df.columns.name = 'AllColumns2'
# Prepare data.frame in the right format
df = df.stack().rename("value").reset_index()
# here the plot :
output_file("CorrelationPlot.html")
# You can use your own palette here
# colors = ['#d7191c', '#fdae61', '#ffffbf', '#a6d96a', '#1a9641']
# I am using 'Viridis256' to map colors with value, change it with 'colors' if you need some specific colors
mapper = LinearColorMapper(
palette=Viridis256, low=df.value.min(), high=df.value.max())
# Define a figure and tools
TOOLS = "box_select,lasso_select,pan,wheel_zoom,box_zoom,reset,help"
p = figure(
tools=TOOLS,
plot_width=1200,
plot_height=1000,
title="Correlation plot",
x_range=list(df.AllColumns1.drop_duplicates()),
y_range=list(df.AllColumns2.drop_duplicates()),
toolbar_location="right",
x_axis_location="below")
# Create rectangle for heatmap
p.rect(
x="AllColumns1",
y="AllColumns2",
width=1,
height=1,
source=ColumnDataSource(df),
line_color=None,
fill_color=transform('value', mapper))
# Add legend
color_bar = ColorBar(
color_mapper=mapper,
location=(0, 0),
ticker=BasicTicker(desired_num_ticks=10))
p.add_layout(color_bar, 'right')
show(p)
参考资料:
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