问题

我最近发现在Orange中可以为散点图创建彩色区域。我知道Orange是基于Python构建的，所以我想我应该能够重新创建这个功能，但我遇到了一些困难。我还没有弄清楚如何将pandas数据框转换为Orange格式。更重要的是，我正在一个Spark环境中工作，所以如果我能从pyspark转到Orange会更好。

我已经在seaborn和matplotlib中设置了一个基本的散点图，以查看是否能够弄清楚它。

import seaborn as sns
import matplotlib.pyplot as plt

# 从Seaborn加载Iris数据集
iris = sns.load_dataset("iris")

# 创建散点图
sns.scatterplot(x="sepal_length", y="petal_width", hue="species", data=iris)

# 添加标签和标题
plt.xlabel("Sepal Length")
plt.ylabel("Petal Width")
plt.title("Scatter Plot of Sepal Length vs. Petal Width")

# 显示图形
plt.legend()
plt.show()

<details>
<summary>英文:</summary>

I recently found out that you can create color regions for scatter plots in Orange.  I know Orange sits on top of python, so I figured I&#39;d be able to recreate this, but I&#39;m having a hard time.  I haven&#39;t figured out how to convert a pandas dataframe for orange.  More importantly, I&#39;m working in a spark environment, so if I could go from pyspark to orange that would be better.

I&#39;ve set up a basic scatter plot in both seaborn and matplotlib to see if I could figure it out.

import seaborn as sns
import matplotlib.pyplot as plt

Load the Iris dataset from Seaborn

iris = sns.load_dataset("iris")

Create a scatter plot

sns.scatterplot(x="sepal_length", y="petal_width", hue="species", data=iris)

Add labels and title

plt.xlabel("Sepal Length")
plt.ylabel("Petal Width")
plt.title("Scatter Plot of Sepal Length vs. Petal Width")

Show the plot

plt.legend()
plt.show()

[![enter image description here][1]][1]


  [1]: https://i.stack.imgur.com/om4pt.png

</details>


# 答案1
**得分**: 1

根据[Orange文档](https://orange3.readthedocs.io/projects/orange-visual-programming/en/latest/widgets/visualize/scatterplot.html#intelligent-data-visualization)：

&gt; 如果在颜色部分选择了一个分类变量，得分计算如下。对于每个数据实例，该方法在投影的2D空间中找到10个最近的邻居，即属性对的组合上。然后检查其中有多少个具有相同的颜色。然后，投影的总得分是具有相同颜色的邻居的平均数。

您可以使用scikit-learn的k最近邻分类器获得类似的结果。在[它们的文档](https://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html)中有一个使用鸢尾花数据集的示例。

我已经修改了这个示例以使其更类似于您分享的截图：

```python
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.colors import ListedColormap

from sklearn import datasets, neighbors
from sklearn.inspection import DecisionBoundaryDisplay

n_neighbors = 10

# 导入鸢尾花数据集
iris = datasets.load_iris()

# 选择特征
features = [2, 3]
X = iris.data[:, features]
y = iris.target

# 创建颜色映射
cmap_light = ListedColormap(["blue", "red", "green"])
cmap_bold = ["blue", "red", "green"]

# 创建一个最近邻分类器的实例并拟合数据。
clf = neighbors.KNeighborsClassifier(n_neighbors, weights="distance")
clf.fit(X, y)

# 绘制边界
_, ax = plt.subplots()
DecisionBoundaryDisplay.from_estimator(
    clf,
    X,
    cmap=cmap_light,
    ax=ax,
    response_method="predict",
    plot_method="pcolormesh",
    xlabel=iris.feature_names[features[0]],
    ylabel=iris.feature_names[features[1]],
    shading="auto",
    alpha=0.3,
)

# 绘制训练点
sns.scatterplot(
    x=X[:, 0],
    y=X[:, 1],
    hue=iris.target_names[y],
    palette=cmap_bold,
    alpha=1.0,
    edgecolor="black",
)

这是结果图：

import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.colors import ListedColormap

from sklearn import datasets, neighbors
from sklearn.inspection import DecisionBoundaryDisplay

n_neighbors = 10

# import iris dataset
iris = datasets.load_iris()

# Select features
features = [2, 3]
X = iris.data[:, features]
y = iris.target

# Create color maps
cmap_light = ListedColormap(["blue", "red", "green"])
cmap_bold = ["blue", "red", "green"]

# we create an instance of Neighbours Classifier and fit the data.
clf = neighbors.KNeighborsClassifier(n_neighbors, weights="distance")
clf.fit(X, y)

# Plot boundaries
_, ax = plt.subplots()
DecisionBoundaryDisplay.from_estimator(
    clf,
    X,
    cmap=cmap_light,
    ax=ax,
    response_method="predict",
    plot_method="pcolormesh",
    xlabel=iris.feature_names[features[0]],
    ylabel=iris.feature_names[features[1]],
    shading="auto",
    alpha=0.3,
)

# Plot training points
sns.scatterplot(
    x=X[:, 0],
    y=X[:, 1],
    hue=iris.target_names[y],
    palette=cmap_bold,
    alpha=1.0,
    edgecolor="black",
)

答案2

得分: 1

以下是翻译好的代码部分：

from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import matplotlib

#
#加载数据
#
iris = load_iris(as_frame=True)
iris_x = iris.data
iris_y = iris.target

iris_x.columns = [col.capitalize()[:-5] for col in iris_x.columns]

#
#为每个类别选择颜色
#

# 在所有类别中自动选择颜色
np.random.seed(2)
class_colors = np.random.choice(
    list(matplotlib.colors.CSS4_COLORS),
    size=len(iris_y.unique()),
    replace=False
)

# 或者，为每个类别指定颜色：
class_colors = ['tab:red', 'tab:green', 'tab:blue']

print('类别颜色为：', class_colors)
display(matplotlib.colors.ListedColormap(class_colors))

#从每种颜色创建一个颜色映射
class_cmaps = [
    matplotlib.colors.LinearSegmentedColormap.from_list('自定义', ['w', color])
    for color in class_colors
]
#查看颜色映射
# for cmap in class_cmaps: display(cmap)

#
#选择特征并拟合KNN分类器
#
feat0 = '花瓣长度'
feat1 = '花瓣宽度'
iris_x = iris_x[[feat0, feat1]]

n_neighbors = 10
knn = KNeighborsClassifier(n_neighbors=n_neighbors, weights='distance').fit(iris_x.values, iris_y)

#
#定义特征空间并获取整个区域的预测
#
x_grid, y_grid = np.meshgrid(
    np.linspace(iris_x[feat0].min(), iris_x[feat0].max(), 100),
    np.linspace(iris_x[feat1].min(), iris_x[feat1].max(), 100)
)
grid_flat = np.hstack([x_grid.reshape(-1, 1), y_grid.reshape(-1, 1)])

#在特征空间的每个点上，获取：
#预测类别和最近的邻居
classes = knn.predict(grid_flat)
neighbors = knn.kneighbors(grid_flat, return_distance=False)
#对于每个点，邻居中有多少与预测类别匹配
prop_per_gridpt = [sum(iris_y[row_neighbors] == clas) / n_neighbors
                   for row_neighbors, clas
                   in zip(neighbors, classes)]

#将比例转换为颜色。每个类别都有一种颜色。
rgb_per_gridpt = [
    class_cmaps[clas](prop)
    for clas, prop in zip(classes, prop_per_gridpt)
]
rgb_per_gridpt = np.array(rgb_per_gridpt).reshape(x_grid.shape + (4,))

#绘制图像
f, ax = plt.subplots(figsize=(8, 8))
ax.scatter(iris_x[feat0], iris_x[feat1], c=np.choose(iris_y.values, class_colors), s=60,
           alpha=0.7, linewidth=2)
ax.set_xlabel(feat0)
ax.set_ylabel(feat1)
ax.set_title(f'散点图 {feat0} vs. {feat1}')

ax.imshow(rgb_per_gridpt, extent=ax.axis(), alpha=0.5,
          interpolation='bicubic', origin='lower')

from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
#
#Load data
#
iris = load_iris(as_frame=True)
iris_x = iris.data
iris_y = iris.target
iris_x.columns = [col.capitalize()[:-5] for col in iris_x.columns]
#
#Choose a color for each class
#
# Choose automatically across all classes
np.random.seed(2)
class_colors = np.random.choice(
list(matplotlib.colors.CSS4_COLORS),
size=len(iris_y.unique()),
replace=False
)
# Alternatively, specify per class:
class_colors = ['tab:red', 'tab:green', 'tab:blue']
print('Class colors are:', class_colors)
display( matplotlib.colors.ListedColormap(class_colors) )
#Create a colormap out of each color
class_cmaps = [
matplotlib.colors.LinearSegmentedColormap.from_list('Custom', ['w', color])
for color in class_colors
]
#View the colormap
# for cmap in class_cmaps: display(cmap)
#
#Select features and fit KNN classifier
#
feat0 = 'Petal length'
feat1 = 'Petal width'
iris_x = iris_x[[feat0, feat1]]
n_neighbors = 10
knn = KNeighborsClassifier(n_neighbors=n_neighbors, weights='distance').fit(iris_x.values, iris_y)
#
#Define a feature space and get a prediction over the entire area
#
x_grid, y_grid = np.meshgrid(
np.linspace(iris_x[feat0].min(), iris_x[feat0].max(), 100),
np.linspace(iris_x[feat1].min(), iris_x[feat1].max(), 100)
)
grid_flat = np.hstack([x_grid.reshape(-1, 1), y_grid.reshape(-1, 1)])
#At each point in the feature space, get the:
#predicted class and nearest neighbors
classes = knn.predict(grid_flat)
neighbors = knn.kneighbors(grid_flat, return_distance=False)
#For each point, what proportion of neighbors match the predicted class
prop_per_gridpt = [sum(iris_y[row_neighbors] == clas) / n_neighbors
for row_neighbors, clas
in zip(neighbors, classes)]
#Convert proportions to colours. Each class has a colour.
rgb_per_gridpt = [
class_cmaps[clas](prop)
for clas, prop in zip(classes, prop_per_gridpt)
]
rgb_per_gridpt = np.array(rgb_per_gridpt).reshape(x_grid.shape + (4,))
#Plot
f, ax = plt.subplots(figsize=(8, 8))
ax.scatter(iris_x[feat0], iris_x[feat1], c=np.choose(iris_y.values, class_colors), s=60,
alpha=0.7, linewidth=2)
ax.set_xlabel(feat0)
ax.set_ylabel(feat1)
ax.set_title(f'Scatter plot of {feat0} vs. {feat1}')
ax.imshow(rgb_per_gridpt, extent=ax.axis(), alpha=0.5,
interpolation='bicubic', origin='lower')