正在与searborn网格热图slowness问题作斗争。我已经从上一个问题扩展了我的数据集，感谢@DizietAsahi为FaceGrid问题提供了解决方案。
现在，我有20x20网格，每个网格中有625个点要映射。即使是一个图层little1也需要很长时间才能获得输出。我在真实数据中有数千个小层。
我的代码大致如下：
import pandas as pd
import numpy as np
import itertools
import seaborn as sns
from matplotlib.colors import ListedColormap
print("seaborn version {}".format(sns.__version__))
# R expand.grid() function in Python
# https://stackoverflow.com/a/12131385/1135316
def expandgrid(*itrs):
product = list(itertools.product(*itrs))
return {'Var{}'.format(i+1):[x[i] for x in product] for i in range(len(itrs))}
ltt= ['little1']
methods=["m" + str(i) for i in range(1,21)]
labels=["l" + str(i) for i in range(1,20)]
times = range(0,100,4)
data = pd.DataFrame(expandgrid(ltt,methods,labels, times, times))
data.columns = ['ltt','method','labels','dtsi','rtsi']
data['nw_score'] = np.random.choice([0,1],data.shape[0])
数据输出到：
Out[36]:
ltt method labels dtsi rtsi nw_score
0 little1 m1 l1 0 0 1
1 little1 m1 l1 0 4 0
2 little1 m1 l1 0 8 0
3 little1 m1 l1 0 12 1
4 little1 m1 l1 0 16 0
... ... ... ... ... ...
237495 little1 m20 l19 96 80 0
237496 little1 m20 l19 96 84 1
237497 little1 m20 l19 96 88 0
237498 little1 m20 l19 96 92 0
237499 little1 m20 l19 96 96 1
[237500 rows x 6 columns]
绘制和定义facet功能：
labels_fill = {0:'red',1:'blue'}
del methods
del labels
def facet(data,color):
data = data.pivot(index="dtsi", columns='rtsi', values='nw_score')
g = sns.heatmap(data, cmap=ListedColormap(['red', 'blue']), cbar=False,annot=True)
for lt in data.ltt.unique():
with sns.plotting_context(font_scale=5.5):
g = sns.FacetGrid(data[data.ltt==lt],row="labels", col="method", size=2, aspect=1,margin_titles=False)
g = g.map_dataframe(facet)
g.add_legend()
g.set_titles(template="")
for ax,method in zip(g.axes[0,:],data.method.unique()):
ax.set_title(method, fontweight='bold', fontsize=12)
for ax,label in zip(g.axes[:,0],data.labels.unique()):
ax.set_ylabel(label, fontweight='bold', fontsize=12, rotation=0, ha='right', va='center')
g.fig.suptitle(lt, fontweight='bold', fontsize=12)
g.fig.tight_layout()
g.fig.subplots_adjust(top=0.8) # make some room for the title
g.savefig(lt+'.png', dpi=300)
我在一段时间后停止了代码，我们可以看到网格正在一个接一个地填充，这非常耗时。生成这张热图的速度慢得令人无法忍受。
我想知道有没有更好的方法来加速这个过程？
Seaborn很慢。如果使用matplotlib而不是seaborn，则每个图形的显示时间大约为半分钟。这仍然很长，但如果生成的是约12000x12000像素的图形，则这是预期的结果。
import time
import pandas as pd
import numpy as np
import itertools
import seaborn as sns
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
print("seaborn version {}".format(sns.__version__))
# R expand.grid() function in Python
# https://stackoverflow.com/a/12131385/1135316
def expandgrid(*itrs):
product = list(itertools.product(*itrs))
return {'Var{}'.format(i+1):[x[i] for x in product] for i in range(len(itrs))}
ltt= ['little1']
methods=["m" + str(i) for i in range(1,21)]
#methods=['method 1', 'method 2', 'method 3', 'method 4']
#labels = ['label1','label2']
labels=["l" + str(i) for i in range(1,20)]
times = range(0,100,4)
data = pd.DataFrame(expandgrid(ltt,methods,labels, times, times))
data.columns = ['ltt','method','labels','dtsi','rtsi']
#data['nw_score'] = np.random.sample(data.shape[0])
data['nw_score'] = np.random.choice([0,1],data.shape[0])
labels_fill = {0:'red',1:'blue'}
del methods
del labels
cmap=ListedColormap(['red', 'blue'])
def facet(data, ax):
data = data.pivot(index="dtsi", columns='rtsi', values='nw_score')
ax.imshow(data, cmap=cmap)
def myfacetgrid(data, row, col, figure=None):
rows = np.unique(data[row].values)
cols = np.unique(data[col].values)
fig, axs = plt.subplots(len(rows), len(cols),
figsize=(2*len(cols)+1, 2*len(rows)+1))
for i, r in enumerate(rows):
row_data = data[data[row] == r]
for j, c in enumerate(cols):
this_data = row_data[row_data[col] == c]
facet(this_data, axs[i,j])
return fig, axs
for lt in data.ltt.unique():
with sns.plotting_context(font_scale=5.5):
t = time.time()
fig, axs = myfacetgrid(data[data.ltt==lt], row="labels", col="method")
print(time.time()-t)
for ax,method in zip(axs[0,:],data.method.unique()):
ax.set_title(method, fontweight='bold', fontsize=12)
for ax,label in zip(axs[:,0],data.labels.unique()):
ax.set_ylabel(label, fontweight='bold', fontsize=12, rotation=0, ha='right', va='center')
print(time.time()-t)
fig.suptitle(lt, fontweight='bold', fontsize=12)
fig.tight_layout()
fig.subplots_adjust(top=0.8) # make some room for the title
print(time.time()-t)
fig.savefig(lt+'.png', dpi=300)
print(time.time()-t)
在这里，时间分为~6秒创建facetgrid，~7秒优化网格布局（通过紧密布局-考虑不使用它！），再花15秒画这个图形。
