突出显示并行坐标图的特定数据点

Question

我正在寻找帮助/颜色的特定数据点在并行坐标图上。我似乎找不到方法。

本质上，我想绘制所有数据如下，然后取，例如，数据点的索引[0，1，2]并为它们涂上第三种颜色以突出显示它们（如果可能的话，也会使它们更厚吗？）建议？

from sklearn import datasets
from yellowbrick.features import ParallelCoordinates

iris = datasets.load_iris()
X = iris.data[:, :]
y = iris.target

features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
title = "Plot over Iris Data"

# Instantiate the visualizer
visualizer = ParallelCoordinates(
    classes=classes, features=features, fast=False, alpha=.40, title=title)

# Fit the visualizer and display it
visualizer.fit_transform(X, y)
visualizer.finalize()  # creates title, legend, etc.

visualizer.ax.tick_params(labelsize=22)  # change size of tick labels
visualizer.ax.title.set_fontsize(30)  # change size of title

for text in visualizer.ax.legend_.texts:  # change size of legend texts
     text.set_fontsize(20)

visualizer.fig.tight_layout()  # fit all texts nicely into the surrounding figure
visualizer.fig.show()

Answer 1

当前， 按顺序迭代数据点。因此，儿童 line2d 可视化器的实例将遵循数据的顺序。因此，您可以做：

from sklearn import datasets
from yellowbrick.features import ParallelCoordinates

# New code ----------------------
import matplotlib.pyplot as plt
special_lines = [0, 1, 2]
# Put any property you want here.
special_properties = {'linestyle': '--', 'color': 'k', 
                      'linewidth': 5, 'zorder': float('inf'), 
                      'alpha': 1}
# End of new code ---------------

iris = datasets.load_iris()
X = iris.data[:, :]
y = iris.target

features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
title = "Plot over Iris Data"

# Instantiate the visualizer
visualizer = ParallelCoordinates(
    classes=classes, features=features, fast=False, alpha=.40, title=title)

# Fit the visualizer and display it
visualizer.fit_transform(X, y)

# New code ----------------------
for line in [visualizer.ax.get_lines()[i] for i in special_lines]:
    plt.setp(line, **special_properties)
# End of new code ---------------

visualizer.finalize()  # creates title, legend, etc.

visualizer.ax.tick_params(labelsize=22)  # change size of tick labels
visualizer.ax.title.set_fontsize(30)  # change size of title

for text in visualizer.ax.legend_.texts:  # change size of legend texts
     text.set_fontsize(20)
        
visualizer.fig.tight_layout()  # fit all texts nicely into the surrounding figure
visualizer.fig.show()

结果：

​因此，（即使我不期望）他们将在以后的更新中改变这种行为。一种更安全的方法是手动检查该行的数据是否匹配可视化器使用的转换数据。请注意，我们需要通常使用转换的数据，因为 ParallelCoordinates 还实现了正常人。这不是您的情况，但总的来说，我们应该这样做：

# Perform AFTER visualizer.fit_transform(X, y).
import numpy as np

transformed_data = list(visualizer.transform(X[special_lines, :]))
for line in visualizer.ax.get_lines():
    for i, arr in enumerate(transformed_data[:]): 
        if np.array_equal(arr, line.get_data()[1]):
            plt.setp(line, **special_properties)
            break