3D Plot:: 如何设置图例和颜色条的方向和位置？
3D Plot:: How to set legend and colorbar orientation and position?
我想创建一个 3d 图，可视化 hidden_layer_sizes、max_iter 和 Score 之间的相关性。我不得不 google 稍微绕一圈才能得到预期的情节，但现在我遇到了一些关于图例的问题：
- 我有两个传说
- 第二个图例很小
我的目标是将右边的图例移到底部。但它不起作用，我什至无法删除正确的图例。如果我设置 showlegend=False 只有突出显示的小图例消失，正确的图例仍然存在。
我确定这只是我缺乏剧情经验。如果有任何可能的帮助，我将不胜感激。
MWE
数据
import pandas as pd
df = pd.DataFrame({'hidden_layer_sizes': {0: 25,
1: 25, 2: 25, 3: 25, 4: 25, 5: 50, 6: 50, 7: 50, 8: 50, 9: 50, 10: 75,
11: 75, 12: 75, 13: 75, 14: 75, 15: 100, 16: 100, 17: 100, 18: 100, 19: 100, 20: 125,
21: 125, 22: 125, 23: 125, 24: 125, 25: 150, 26: 150, 27: 150, 28: 150, 29: 150},
'max_iter': {0: 100, 1: 200, 2: 300, 3: 400, 4: 500, 5: 100, 6: 200, 7: 300, 8: 400, 9: 500,
10: 100, 11: 200, 12: 300, 13: 400, 14: 500, 15: 100, 16: 200, 17: 300, 18: 400, 19: 500,
20: 100, 21: 200, 22: 300, 23: 400, 24: 500, 25: 100, 26: 200, 27: 300, 28: 400, 29: 500},
'Score': {0: 0.9270832984321359, 1: 0.9172223807360554, 2: 0.9202868292420568, 3: 0.9187318693456508,
4: 0.9263589700182026, 5: 0.9325454241272417, 6: 0.9351742112383672, 7: 0.934706441722599,
8: 0.9350294733755595, 9: 0.9334167352798914, 10: 0.9355533396303661, 11: 0.9327821227628682,
12: 0.9333376163633981, 13: 0.9322875868305249, 14: 0.9345524934883098, 15: 0.9341786678949748,
16: 0.9306931295155753, 17: 0.9332227354795629, 18: 0.9312008571438402, 19: 0.9335295484755572,
20: 0.9333167395841182, 21: 0.9315595511169302, 22: 0.9301811416101524, 23: 0.9314818362895073,
24: 0.9308551601915486, 25: 0.9296559215457606, 26: 0.9284091216867709, 27: 0.9318823563281231,
28: 0.9295666150206443, 29: 0.9291284919738931},
'Time': {0: 119.91294360160828, 1: 256.4710912704468, 2: 266.6792154312134, 3: 326.7445312023163,
4: 256.8881601810455, 5: 183.77022705078124, 6: 359.7090343952179, 7: 383.6012378692627,
8: 416.3133870601654, 9: 425.7837643623352, 10: 225.39801173210145, 11: 516.9914848804474,
12: 562.7134436607361, 13: 585.6752841472626, 14: 560.5802517414093, 15: 267.22873797416685,
16: 646.1253435134888, 17: 811.1979314804078, 18: 780.6058969974517, 19: 789.9369702339172,
20: 394.0711458206177, 21: 890.7988158226013, 22: 1065.5482338428496, 23: 996.5119229316712,
24: 1096.0208141803741, 25: 524.0947244644165, 26: 1182.684538602829, 27: 1348.3343998908997,
28: 1356.0255290508271, 29: 1053.8607951164245}})
创建情节的代码
import numpy as np
import plotly.graph_objects as go
from scipy.interpolate import griddata
import plotly.io as pio
xi = np.linspace(min(df["hidden_layer_sizes"]), max(df["hidden_layer_sizes"]), num=100)
yi = np.linspace(min(df["max_iter"]), max(df["max_iter"]), num=100)
x_grid, y_grid = np.meshgrid(xi,yi)
z_grid = griddata((df["hidden_layer_sizes"],df["max_iter"]),df["Score"],(x_grid,y_grid),method="cubic")
fig = go.Figure(go.Surface(x=x_grid, y=y_grid, z=z_grid, showlegend=True))
fig.update_layout(title="Test",
width=600, height=600, template="none",
legend=dict(orientation="h"))
fig.show()
如果我没记错的话，您可以使用 update_traces 来设置颜色栏属性。类似于：
fig.update_traces(
colorbar_orientation='h',
colorbar_y=0
)
您可能需要调整位置本身，以免出现任何重叠。
你在这里说的是两个不同的东西：legend和colorbar，前者是图形布局的属性，后者是图形数据或轨迹的属性.要获得您在这里的目标，只需包含以下内容：
fig.update_layout(legend = dict(orientation="h", x = -0.25, y = -0.10))
fig.update_traces(colorbar = dict(orientation='h', y = -0.25, x = 0.5))
情节 1
也就是说，如果您想完全保留“小”图例。如果没有，只需使用：
fig.update_layout(showlegend = False)
情节 2
完整代码：
import numpy as np
import plotly.graph_objects as go
from scipy.interpolate import griddata
import plotly.io as pio
import pandas as pd
df = pd.DataFrame({'hidden_layer_sizes': {0: 25,
1: 25, 2: 25, 3: 25, 4: 25, 5: 50, 6: 50, 7: 50, 8: 50, 9: 50, 10: 75,
11: 75, 12: 75, 13: 75, 14: 75, 15: 100, 16: 100, 17: 100, 18: 100, 19: 100, 20: 125,
21: 125, 22: 125, 23: 125, 24: 125, 25: 150, 26: 150, 27: 150, 28: 150, 29: 150},
'max_iter': {0: 100, 1: 200, 2: 300, 3: 400, 4: 500, 5: 100, 6: 200, 7: 300, 8: 400, 9: 500,
10: 100, 11: 200, 12: 300, 13: 400, 14: 500, 15: 100, 16: 200, 17: 300, 18: 400, 19: 500,
20: 100, 21: 200, 22: 300, 23: 400, 24: 500, 25: 100, 26: 200, 27: 300, 28: 400, 29: 500},
'Score': {0: 0.9270832984321359, 1: 0.9172223807360554, 2: 0.9202868292420568, 3: 0.9187318693456508,
4: 0.9263589700182026, 5: 0.9325454241272417, 6: 0.9351742112383672, 7: 0.934706441722599,
8: 0.9350294733755595, 9: 0.9334167352798914, 10: 0.9355533396303661, 11: 0.9327821227628682,
12: 0.9333376163633981, 13: 0.9322875868305249, 14: 0.9345524934883098, 15: 0.9341786678949748,
16: 0.9306931295155753, 17: 0.9332227354795629, 18: 0.9312008571438402, 19: 0.9335295484755572,
20: 0.9333167395841182, 21: 0.9315595511169302, 22: 0.9301811416101524, 23: 0.9314818362895073,
24: 0.9308551601915486, 25: 0.9296559215457606, 26: 0.9284091216867709, 27: 0.9318823563281231,
28: 0.9295666150206443, 29: 0.9291284919738931},
'Time': {0: 119.91294360160828, 1: 256.4710912704468, 2: 266.6792154312134, 3: 326.7445312023163,
4: 256.8881601810455, 5: 183.77022705078124, 6: 359.7090343952179, 7: 383.6012378692627,
8: 416.3133870601654, 9: 425.7837643623352, 10: 225.39801173210145, 11: 516.9914848804474,
12: 562.7134436607361, 13: 585.6752841472626, 14: 560.5802517414093, 15: 267.22873797416685,
16: 646.1253435134888, 17: 811.1979314804078, 18: 780.6058969974517, 19: 789.9369702339172,
20: 394.0711458206177, 21: 890.7988158226013, 22: 1065.5482338428496, 23: 996.5119229316712,
24: 1096.0208141803741, 25: 524.0947244644165, 26: 1182.684538602829, 27: 1348.3343998908997,
28: 1356.0255290508271, 29: 1053.8607951164245}})
xi = np.linspace(min(df["hidden_layer_sizes"]), max(df["hidden_layer_sizes"]), num=100)
yi = np.linspace(min(df["max_iter"]), max(df["max_iter"]), num=100)
x_grid, y_grid = np.meshgrid(xi,yi)
z_grid = griddata((df["hidden_layer_sizes"],df["max_iter"]),df["Score"],(x_grid,y_grid),method="cubic")
fig = go.Figure(go.Surface(x=x_grid, y=y_grid, z=z_grid, showlegend=True))
fig.update_layout(title="Test",
width=600, height=600, template="none",
# legend=dict(orientation="h")
)
fig.update_layout(legend = dict(orientation="h", x = -0.25, y = -0.10))
fig.update_traces(colorbar = dict(orientation='h', y = -0.25, x = 0.5))
fig.update_layout(showlegend = False)
fig.show()
我想创建一个 3d 图，可视化 hidden_layer_sizes、max_iter 和 Score 之间的相关性。我不得不 google 稍微绕一圈才能得到预期的情节，但现在我遇到了一些关于图例的问题：
- 我有两个传说
- 第二个图例很小
我的目标是将右边的图例移到底部。但它不起作用，我什至无法删除正确的图例。如果我设置 showlegend=False 只有突出显示的小图例消失，正确的图例仍然存在。
我确定这只是我缺乏剧情经验。如果有任何可能的帮助，我将不胜感激。
MWE
数据
import pandas as pd
df = pd.DataFrame({'hidden_layer_sizes': {0: 25,
1: 25, 2: 25, 3: 25, 4: 25, 5: 50, 6: 50, 7: 50, 8: 50, 9: 50, 10: 75,
11: 75, 12: 75, 13: 75, 14: 75, 15: 100, 16: 100, 17: 100, 18: 100, 19: 100, 20: 125,
21: 125, 22: 125, 23: 125, 24: 125, 25: 150, 26: 150, 27: 150, 28: 150, 29: 150},
'max_iter': {0: 100, 1: 200, 2: 300, 3: 400, 4: 500, 5: 100, 6: 200, 7: 300, 8: 400, 9: 500,
10: 100, 11: 200, 12: 300, 13: 400, 14: 500, 15: 100, 16: 200, 17: 300, 18: 400, 19: 500,
20: 100, 21: 200, 22: 300, 23: 400, 24: 500, 25: 100, 26: 200, 27: 300, 28: 400, 29: 500},
'Score': {0: 0.9270832984321359, 1: 0.9172223807360554, 2: 0.9202868292420568, 3: 0.9187318693456508,
4: 0.9263589700182026, 5: 0.9325454241272417, 6: 0.9351742112383672, 7: 0.934706441722599,
8: 0.9350294733755595, 9: 0.9334167352798914, 10: 0.9355533396303661, 11: 0.9327821227628682,
12: 0.9333376163633981, 13: 0.9322875868305249, 14: 0.9345524934883098, 15: 0.9341786678949748,
16: 0.9306931295155753, 17: 0.9332227354795629, 18: 0.9312008571438402, 19: 0.9335295484755572,
20: 0.9333167395841182, 21: 0.9315595511169302, 22: 0.9301811416101524, 23: 0.9314818362895073,
24: 0.9308551601915486, 25: 0.9296559215457606, 26: 0.9284091216867709, 27: 0.9318823563281231,
28: 0.9295666150206443, 29: 0.9291284919738931},
'Time': {0: 119.91294360160828, 1: 256.4710912704468, 2: 266.6792154312134, 3: 326.7445312023163,
4: 256.8881601810455, 5: 183.77022705078124, 6: 359.7090343952179, 7: 383.6012378692627,
8: 416.3133870601654, 9: 425.7837643623352, 10: 225.39801173210145, 11: 516.9914848804474,
12: 562.7134436607361, 13: 585.6752841472626, 14: 560.5802517414093, 15: 267.22873797416685,
16: 646.1253435134888, 17: 811.1979314804078, 18: 780.6058969974517, 19: 789.9369702339172,
20: 394.0711458206177, 21: 890.7988158226013, 22: 1065.5482338428496, 23: 996.5119229316712,
24: 1096.0208141803741, 25: 524.0947244644165, 26: 1182.684538602829, 27: 1348.3343998908997,
28: 1356.0255290508271, 29: 1053.8607951164245}})
创建情节的代码
import numpy as np
import plotly.graph_objects as go
from scipy.interpolate import griddata
import plotly.io as pio
xi = np.linspace(min(df["hidden_layer_sizes"]), max(df["hidden_layer_sizes"]), num=100)
yi = np.linspace(min(df["max_iter"]), max(df["max_iter"]), num=100)
x_grid, y_grid = np.meshgrid(xi,yi)
z_grid = griddata((df["hidden_layer_sizes"],df["max_iter"]),df["Score"],(x_grid,y_grid),method="cubic")
fig = go.Figure(go.Surface(x=x_grid, y=y_grid, z=z_grid, showlegend=True))
fig.update_layout(title="Test",
width=600, height=600, template="none",
legend=dict(orientation="h"))
fig.show()
如果我没记错的话，您可以使用 update_traces 来设置颜色栏属性。类似于：
fig.update_traces(
colorbar_orientation='h',
colorbar_y=0
)
您可能需要调整位置本身，以免出现任何重叠。
你在这里说的是两个不同的东西：legend和colorbar，前者是图形布局的属性，后者是图形数据或轨迹的属性.要获得您在这里的目标，只需包含以下内容：
fig.update_layout(legend = dict(orientation="h", x = -0.25, y = -0.10))
fig.update_traces(colorbar = dict(orientation='h', y = -0.25, x = 0.5))
情节 1
也就是说，如果您想完全保留“小”图例。如果没有，只需使用：
fig.update_layout(showlegend = False)
情节 2
完整代码：
import numpy as np
import plotly.graph_objects as go
from scipy.interpolate import griddata
import plotly.io as pio
import pandas as pd
df = pd.DataFrame({'hidden_layer_sizes': {0: 25,
1: 25, 2: 25, 3: 25, 4: 25, 5: 50, 6: 50, 7: 50, 8: 50, 9: 50, 10: 75,
11: 75, 12: 75, 13: 75, 14: 75, 15: 100, 16: 100, 17: 100, 18: 100, 19: 100, 20: 125,
21: 125, 22: 125, 23: 125, 24: 125, 25: 150, 26: 150, 27: 150, 28: 150, 29: 150},
'max_iter': {0: 100, 1: 200, 2: 300, 3: 400, 4: 500, 5: 100, 6: 200, 7: 300, 8: 400, 9: 500,
10: 100, 11: 200, 12: 300, 13: 400, 14: 500, 15: 100, 16: 200, 17: 300, 18: 400, 19: 500,
20: 100, 21: 200, 22: 300, 23: 400, 24: 500, 25: 100, 26: 200, 27: 300, 28: 400, 29: 500},
'Score': {0: 0.9270832984321359, 1: 0.9172223807360554, 2: 0.9202868292420568, 3: 0.9187318693456508,
4: 0.9263589700182026, 5: 0.9325454241272417, 6: 0.9351742112383672, 7: 0.934706441722599,
8: 0.9350294733755595, 9: 0.9334167352798914, 10: 0.9355533396303661, 11: 0.9327821227628682,
12: 0.9333376163633981, 13: 0.9322875868305249, 14: 0.9345524934883098, 15: 0.9341786678949748,
16: 0.9306931295155753, 17: 0.9332227354795629, 18: 0.9312008571438402, 19: 0.9335295484755572,
20: 0.9333167395841182, 21: 0.9315595511169302, 22: 0.9301811416101524, 23: 0.9314818362895073,
24: 0.9308551601915486, 25: 0.9296559215457606, 26: 0.9284091216867709, 27: 0.9318823563281231,
28: 0.9295666150206443, 29: 0.9291284919738931},
'Time': {0: 119.91294360160828, 1: 256.4710912704468, 2: 266.6792154312134, 3: 326.7445312023163,
4: 256.8881601810455, 5: 183.77022705078124, 6: 359.7090343952179, 7: 383.6012378692627,
8: 416.3133870601654, 9: 425.7837643623352, 10: 225.39801173210145, 11: 516.9914848804474,
12: 562.7134436607361, 13: 585.6752841472626, 14: 560.5802517414093, 15: 267.22873797416685,
16: 646.1253435134888, 17: 811.1979314804078, 18: 780.6058969974517, 19: 789.9369702339172,
20: 394.0711458206177, 21: 890.7988158226013, 22: 1065.5482338428496, 23: 996.5119229316712,
24: 1096.0208141803741, 25: 524.0947244644165, 26: 1182.684538602829, 27: 1348.3343998908997,
28: 1356.0255290508271, 29: 1053.8607951164245}})
xi = np.linspace(min(df["hidden_layer_sizes"]), max(df["hidden_layer_sizes"]), num=100)
yi = np.linspace(min(df["max_iter"]), max(df["max_iter"]), num=100)
x_grid, y_grid = np.meshgrid(xi,yi)
z_grid = griddata((df["hidden_layer_sizes"],df["max_iter"]),df["Score"],(x_grid,y_grid),method="cubic")
fig = go.Figure(go.Surface(x=x_grid, y=y_grid, z=z_grid, showlegend=True))
fig.update_layout(title="Test",
width=600, height=600, template="none",
# legend=dict(orientation="h")
)
fig.update_layout(legend = dict(orientation="h", x = -0.25, y = -0.10))
fig.update_traces(colorbar = dict(orientation='h', y = -0.25, x = 0.5))
fig.update_layout(showlegend = False)
fig.show()