Seaborn 热图 - 仅当值高于给定阈值时才显示热图
Seaborn Heatmap - Display the heatmap only if values are above given threshold
下面的python代码显示了句子的相似度，它使用通用句子编码器来实现相同的。
from absl import logging
import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import re
import seaborn as sns
module_url = "https://tfhub.dev/google/universal-sentence-encoder/4"
model = hub.load(module_url)
print ("module %s loaded" % module_url)
def embed(input):
return model(input)
def plot_similarity(labels, features, rotation):
corr = np.inner(features, features)
print(corr)
sns.set(font_scale=2.4)
plt.subplots(figsize=(40,30))
g = sns.heatmap(
corr,
xticklabels=labels,
yticklabels=labels,
vmin=0,
vmax=1,
cmap="YlGnBu",linewidths=1.0)
g.set_xticklabels(labels, rotation=rotation)
g.set_title("Semantic Textual Similarity")
def run_and_plot(messages_):
message_embeddings_ = embed(messages_)
plot_similarity(messages_, message_embeddings_, 90)
messages = [
"I want to know my savings account balance",
"Show my bank balance",
"Show me my account",
"What is my bank balance",
"Please Show my bank balance"
]
run_and_plot(messages)
输出显示为热图，如下所示，同时打印值
我只想关注看起来很相似的句子，但是当前的热图显示了所有值。
所以
有没有一种方法可以只查看范围大于 0.6 且小于 0.999 的值的热图？
是否可以打印给定范围内的匹配值对，即 0.6 和 0.99？
谢谢，
罗希特
根据您的问题更新，这里是修订版。显然，在网格中，不能删除单个单元格。但是我们可以大幅减少热图以仅显示相关的值对。热图中存在的重要值越随机分散，这种效果就越不明显。
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from copy import copy
import seaborn as sns
#semi-random data generation
labels = list("ABCDE")
np.random.seed(123)
df = pd.DataFrame(np.random.randint(1, 100, (20, 5)))
df.columns = labels
df.A = df.B - df.D
df.C = df.B + df.A
df.E = df.A + df.C
#your correlation array
corr = df.corr().to_numpy()
print(corr)
#conditions for filtering 0.6<=r<=0.9
val_min = 0.6
val_max = 0.99
#plotting starts here
sns.set(font_scale=2.4)
#two axis objects just for comparison purposes
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15,8))
#define the colormap with clipping values
my_cmap = copy(plt.cm.YlGnBu)
my_cmap.set_over("white")
my_cmap.set_under("white")
#ax1 - full set of conditions as in the initial version
g1 = sns.heatmap(corr,
xticklabels=labels,
yticklabels=labels,
vmin=val_min,
vmax=val_max,
cmap=my_cmap,
linewidths=1.0,
linecolor="grey",
ax=ax1)
g1.set_title("Entire heatmap")
#ax2 - remove empty rows/columns
# use only lower triangle
corr = np.tril(corr)
#delete columns where all elements do not fulfill the conditions
ind_x, = np.where(np.all(np.logical_or(corr<val_min, corr>val_max), axis=0))
corr = np.delete(corr, ind_x, 1)
#update x labels
map_labels_x = [item for i, item in enumerate(labels) if i not in ind_x]
#now the same for rows
ind_y, = np.where(np.all(np.logical_or(corr<val_min, corr>val_max), axis=1))
corr = np.delete(corr, ind_y, 0)
#update y labels
map_labels_y = [item for i, item in enumerate(labels) if i not in ind_y]
#plot heatmap
g2 = sns.heatmap(corr,
xticklabels=map_labels_x,
yticklabels=map_labels_y,
vmin=val_min,
vmax=val_max,
cmap=my_cmap,
linewidths=1.0,
linecolor="grey", ax=ax2)
g2.set_title("Reduced heatmap")
plt.show()
示例输出：
左侧，显示热图所有元素的原始方法。是的，只保留相关的对。
该问题（以及代码）排除了显着的负相关，例如 -0.95。如果这不是预期的，则应使用 np.abs()。
初步回答
我很惊讶还没有人提供独立的解决方案，所以这里有一个：
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from copy import copy
import seaborn as sns
labels = list("ABCDE")
#semi-random data
np.random.seed(123)
df = pd.DataFrame(np.random.randint(1, 100, (20, 5)))
df.columns = labels
df.A = df.B - df.D
df.E = df.A + df.C
corr = df.corr()
sns.set(font_scale=2.4)
plt.subplots(figsize=(10,8))
#define the cmap with clipping values
my_cmap = copy(plt.cm.YlGnBu)
my_cmap.set_over("white")
my_cmap.set_under("white")
g = sns.heatmap(corr,
xticklabels=labels,
yticklabels=labels,
vmin=0.5,
vmax=0.9,
cmap=my_cmap,
linewidths=1.0,
linecolor="grey")
g.set_xticklabels(labels, rotation=60)
g.set_title("Important!")
plt.show()
示例输出：
下面的python代码显示了句子的相似度，它使用通用句子编码器来实现相同的。
from absl import logging
import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import re
import seaborn as sns
module_url = "https://tfhub.dev/google/universal-sentence-encoder/4"
model = hub.load(module_url)
print ("module %s loaded" % module_url)
def embed(input):
return model(input)
def plot_similarity(labels, features, rotation):
corr = np.inner(features, features)
print(corr)
sns.set(font_scale=2.4)
plt.subplots(figsize=(40,30))
g = sns.heatmap(
corr,
xticklabels=labels,
yticklabels=labels,
vmin=0,
vmax=1,
cmap="YlGnBu",linewidths=1.0)
g.set_xticklabels(labels, rotation=rotation)
g.set_title("Semantic Textual Similarity")
def run_and_plot(messages_):
message_embeddings_ = embed(messages_)
plot_similarity(messages_, message_embeddings_, 90)
messages = [
"I want to know my savings account balance",
"Show my bank balance",
"Show me my account",
"What is my bank balance",
"Please Show my bank balance"
]
run_and_plot(messages)
输出显示为热图，如下所示，同时打印值
我只想关注看起来很相似的句子，但是当前的热图显示了所有值。
所以
有没有一种方法可以只查看范围大于 0.6 且小于 0.999 的值的热图？
是否可以打印给定范围内的匹配值对，即 0.6 和 0.99？ 谢谢， 罗希特
根据您的问题更新，这里是修订版。显然，在网格中，不能删除单个单元格。但是我们可以大幅减少热图以仅显示相关的值对。热图中存在的重要值越随机分散，这种效果就越不明显。
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from copy import copy
import seaborn as sns
#semi-random data generation
labels = list("ABCDE")
np.random.seed(123)
df = pd.DataFrame(np.random.randint(1, 100, (20, 5)))
df.columns = labels
df.A = df.B - df.D
df.C = df.B + df.A
df.E = df.A + df.C
#your correlation array
corr = df.corr().to_numpy()
print(corr)
#conditions for filtering 0.6<=r<=0.9
val_min = 0.6
val_max = 0.99
#plotting starts here
sns.set(font_scale=2.4)
#two axis objects just for comparison purposes
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15,8))
#define the colormap with clipping values
my_cmap = copy(plt.cm.YlGnBu)
my_cmap.set_over("white")
my_cmap.set_under("white")
#ax1 - full set of conditions as in the initial version
g1 = sns.heatmap(corr,
xticklabels=labels,
yticklabels=labels,
vmin=val_min,
vmax=val_max,
cmap=my_cmap,
linewidths=1.0,
linecolor="grey",
ax=ax1)
g1.set_title("Entire heatmap")
#ax2 - remove empty rows/columns
# use only lower triangle
corr = np.tril(corr)
#delete columns where all elements do not fulfill the conditions
ind_x, = np.where(np.all(np.logical_or(corr<val_min, corr>val_max), axis=0))
corr = np.delete(corr, ind_x, 1)
#update x labels
map_labels_x = [item for i, item in enumerate(labels) if i not in ind_x]
#now the same for rows
ind_y, = np.where(np.all(np.logical_or(corr<val_min, corr>val_max), axis=1))
corr = np.delete(corr, ind_y, 0)
#update y labels
map_labels_y = [item for i, item in enumerate(labels) if i not in ind_y]
#plot heatmap
g2 = sns.heatmap(corr,
xticklabels=map_labels_x,
yticklabels=map_labels_y,
vmin=val_min,
vmax=val_max,
cmap=my_cmap,
linewidths=1.0,
linecolor="grey", ax=ax2)
g2.set_title("Reduced heatmap")
plt.show()
示例输出：
左侧，显示热图所有元素的原始方法。是的，只保留相关的对。
该问题（以及代码）排除了显着的负相关，例如 -0.95。如果这不是预期的，则应使用 np.abs()。
初步回答
我很惊讶还没有人提供独立的解决方案，所以这里有一个：
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from copy import copy
import seaborn as sns
labels = list("ABCDE")
#semi-random data
np.random.seed(123)
df = pd.DataFrame(np.random.randint(1, 100, (20, 5)))
df.columns = labels
df.A = df.B - df.D
df.E = df.A + df.C
corr = df.corr()
sns.set(font_scale=2.4)
plt.subplots(figsize=(10,8))
#define the cmap with clipping values
my_cmap = copy(plt.cm.YlGnBu)
my_cmap.set_over("white")
my_cmap.set_under("white")
g = sns.heatmap(corr,
xticklabels=labels,
yticklabels=labels,
vmin=0.5,
vmax=0.9,
cmap=my_cmap,
linewidths=1.0,
linecolor="grey")
g.set_xticklabels(labels, rotation=60)
g.set_title("Important!")
plt.show()
示例输出：