Python-机器学习秘籍-四-

Python 机器学习秘籍（四）

原文：Python machine learning cookbook

协议：CC BY-NC-SA 4.0

十二、可视化数据

在本章中，我们将介绍以下食谱:

• 绘制三维散点图
• 绘制气泡图
• 动画气泡图
• 绘制饼图
• 绘制日期格式的时间序列数据
• 绘制直方图
• 可视化热图
• 动态信号动画

简介

数据可视化是机器学习的重要支柱。它帮助我们制定正确的策略来理解数据。数据的可视化表示帮助我们选择正确的算法。数据可视化的主要目标之一是使用图形和图表清晰地交流。这些图表帮助我们清晰有效地交流信息。

在现实世界中，我们总是会遇到数字数据。我们希望使用图形、线条、点、条等对这些数字数据进行编码，以直观地显示这些数字中包含的信息。这使得复杂的数据分布更加易于理解和使用。这一过程用于各种情况，包括比较分析、跟踪增长、市场分布、民意调查和许多其他情况。

我们使用不同的图表来显示变量之间的模式或关系。我们使用直方图来显示数据的分布。当我们想要查找特定的测量值时，我们使用表格。在本章中，我们将查看各种场景，并讨论在这些情况下我们可以使用什么可视化。

绘制三维散点图

在这个食谱中，我们将学习如何绘制三维散点图并在三维空间中可视化它们。

怎么做…

1. 新建一个 Python 文件，导入以下包:

   import numpy as np
   import matplotlib.pyplot as plt
   from mpl_toolkits.mplot3d import Axes3D
   

2. 创建空图形:

   # Create the figure
   fig = plt.figure()
   ax = fig.add_subplot(111, projection='3d')
   

3. 定义我们应该生成的值的数量:

   # Define the number of values
   n = 250
   

4. 创建一个lambda函数来生成给定范围内的值:

   # Create a lambda function to generate the random values in the given range
   f = lambda minval, maxval, n: minval + (maxval - minval) * np.random.rand(n)
   

5. 使用此函数生成 X、Y 和 Z 值:

   # Generate the values
   x_vals = f(15, 41, n)
   y_vals = f(-10, 70, n)
   z_vals = f(-52, -37, n)
   

6. 绘制这些值:

   # Plot the values
   ax.scatter(x_vals, y_vals, z_vals, c='k', marker='o')
   ax.set_xlabel('X axis')
   ax.set_ylabel('Y axis')
   ax.set_zlabel('Z axis')
   
   plt.show()
   

7. The full code is in the scatter_3d.py file that's already provided to you. If you run this code, you will see the following figure:

   

绘制气泡图

我们来看看如何绘制泡泡图。2D 气泡图中每个圆的大小代表该特定点的振幅。

怎么做…

1. 新建一个 Python 文件，导入以下包:

   import numpy as np
   import matplotlib.pyplot as plt 
   

2. 定义我们应该生成的值的数量:

   # Define the number of values
   num_vals = 40
   

3. 为x和y生成随机值:

   # Generate random values
   x = np.random.rand(num_vals)
   y = np.random.rand(num_vals)
   

4. 定义气泡图中每个点的面积值:

   # Define area for each bubble
   # Max radius is set to a specified value
   max_radius = 25
   area = np.pi * (max_radius * np.random.rand(num_vals)) ** 2  
   

5. 定义颜色:

   # Generate colors
   colors = np.random.rand(num_vals)
   

6. 绘制这些值:

   # Plot the points
   plt.scatter(x, y, s=area, c=colors, alpha=1.0)
   
   plt.show()
   

7. The full code is in the bubble_plot.py file that's already provided to you. If you run this code, you will see the following figure:

   

制作气泡图动画

让我们看看如何制作气泡图的动画。当您想要可视化瞬态和动态数据时，这非常有用。

怎么做…

1. 新建一个 Python 文件，导入以下包:

   import numpy as np
   import matplotlib.pyplot as plt
   from matplotlib.animation import FuncAnimation 
   

2. 让我们定义一个tracker函数来动态更新气泡图:

   def tracker(cur_num):
       # Get the current index 
       cur_index = cur_num % num_points
   

3. 定义颜色:

       # Set the color of the datapoints 
       datapoints['color'][:, 3] = 1.0
   

4. 更新圆的大小:

       # Update the size of the circles 
       datapoints['size'] += datapoints['growth']
   

5. 更新集合中最早数据点的位置:

       # Update the position of the oldest datapoint 
       datapoints['position'][cur_index] = np.random.uniform(0, 1, 2)
       datapoints['size'][cur_index] = 7
       datapoints['color'][cur_index] = (0, 0, 0, 1)
       datapoints['growth'][cur_index] = np.random.uniform(40, 150)
   

6. 更新散点图的参数:

       # Update the parameters of the scatter plot 
       scatter_plot.set_edgecolors(datapoints['color'])
       scatter_plot.set_sizes(datapoints['size'])
       scatter_plot.set_offsets(datapoints['position'])
   

7. 定义main功能，创建一个空图形:

   if __name__=='__main__':
       # Create a figure 
       fig = plt.figure(figsize=(9, 7), facecolor=(0,0.9,0.9))
       ax = fig.add_axes([0, 0, 1, 1], frameon=False)
       ax.set_xlim(0, 1), ax.set_xticks([])
       ax.set_ylim(0, 1), ax.set_yticks([])
   

8. 定义在任何给定时间点将出现在图上的点数:

       # Create and initialize the datapoints in random positions 
       # and with random growth rates.
       num_points = 20
   

9. 使用随机值定义数据点:

       datapoints = np.zeros(num_points, dtype=[('position', float, 2),
               ('size', float, 1), ('growth', float, 1), ('color', float, 4)])
       datapoints['position'] = np.random.uniform(0, 1, (num_points, 2))
       datapoints['growth'] = np.random.uniform(40, 150, num_points)
   

10. 创建散点图，每帧更新一次:

```py

    # Construct the scatter plot that will be updated every frame
    scatter_plot = ax.scatter(datapoints['position'][:, 0], datapoints['position'][:, 1],
                      s=datapoints['size'], lw=0.7, edgecolors=datapoints['color'],
                      facecolors='none')
```

11. 使用tracker功能启动动画:

```py
    # Start the animation using the 'tracker' function 
    animation = FuncAnimation(fig, tracker, interval=10)

    plt.show()
```

12. The full code is in the dynamic_bubble_plot.py file that's already provided to you. If you run this code, you will see the following figure:

![How to do it…](https://p6-xtjj-sign.byteimg.com/tos-cn-i-73owjymdk6/665bf296d5a74393baa005a8ec8842cc~tplv-73owjymdk6-jj-mark-v1:0:0:0:0:5o6Y6YeR5oqA5pyv56S-5Yy6IEAg5biD5a6i6aOe6b6Z:q75.awebp?rk3s=f64ab15b&x-expires=1771469965&x-signature=4vBQEz33cvWcvRZH7bsbFjs4YoM%3D)

绘制饼图

让我们看看如何绘制饼图。当您想要可视化组中一组标签的百分比时，这很有用。

怎么做…

1. 新建一个 Python 文件，导入以下包:

   import numpy as np
   import matplotlib.pyplot as plt 
   

2. 定义标签和值:

   # Labels and corresponding values in counter clockwise direction
   data = {'Apple': 26, 
           'Mango': 17,
           'Pineapple': 21, 
           'Banana': 29, 
           'Strawberry': 11}
   

3. 定义可视化的颜色:

   # List of corresponding colors
   colors = ['orange', 'lightgreen', 'lightblue', 'gold', 'cyan']
   

4. 定义一个变量，通过将饼图的一部分与其他部分分开来突出显示该部分。如果不想突出显示任何部分，请将所有值设置为0 :

   # Needed if we want to highlight a section
   explode = (0, 0, 0, 0, 0)  
   

5. 绘制饼图。注意，如果使用 Python 3，应该在下面的函数调用中使用list(data.values()):

   # Plot the pie chart
   plt.pie(data.values(), explode=explode, labels=data.keys(), 
           colors=colors, autopct='%1.1f%%', shadow=False, startangle=90)
   
   # Aspect ratio of the pie chart, 'equal' indicates tht we 
   # want it to be a circle
   plt.axis('equal')
   
   plt.show()
   

6. The full code is in the pie_chart.py file that's already provided to you. If you run this code, you will see the following figure:

   

   如果您将爆炸阵列更改为(0, 0.2, 0, 0, 0，则它将突出显示草莓部分。您将看到下图:

   

绘制日期格式的时间序列数据

让我们看看如何使用日期格式绘制时间序列数据。这在随时间可视化股票数据时非常有用。

怎么做…

1. 新建一个 Python 文件，导入以下包:

   import numpy
   import matplotlib.pyplot as plt
   from matplotlib.mlab import csv2rec
   import matplotlib.cbook as cbook
   from matplotlib.ticker import Formatter
   

2. 定义一个函数来格式化日期。__init__功能设置类变量:

   # Define a class for formatting
   class DataFormatter(Formatter):
       def __init__(self, dates, date_format='%Y-%m-%d'):
           self.dates = dates
           self.date_format = date_format
   

3. 在任何给定时间提取值，并以以下格式返回:

       # Extact the value at time t at position 'position'
       def __call__(self, t, position=0):
           index = int(round(t))
           if index >= len(self.dates) or index < 0:
               return ''
   
           return self.dates[index].strftime(self.date_format)
   

4. 定义main功能。我们将使用 matplotlib 中提供的苹果股票报价 CSV 文件:

   if __name__=='__main__':
       # CSV file containing the stock quotes 
       input_file = cbook.get_sample_data('aapl.csv', asfileobj=False)
   

5. 加载 CSV 文件:

       # Load csv file into numpy record array
       data = csv2rec(input_file)
   

6. 提取这些值的子集来绘制它们:

       # Take a subset for plotting
       data = data[-70:]
   

7. 创建格式化程序对象，并用日期初始化它:

       # Create the date formatter object
       formatter = DataFormatter(data.date)
   

8. 定义 X 轴和 Y 轴:

       # X axis
       x_vals = numpy.arange(len(data))
   
       # Y axis values are the closing stock quotes
       y_vals = data.close 
   

9. 绘制数据:

       # Plot data
       fig, ax = plt.subplots()
       ax.xaxis.set_major_formatter(formatter)
       ax.plot(x_vals, y_vals, 'o-')
       fig.autofmt_xdate()
       plt.show()
   

10. The full code is in the time_series.py file that's already provided to you. If you run this code, you will see the following figure:

![How to do it…](https://p6-xtjj-sign.byteimg.com/tos-cn-i-73owjymdk6/97fa0d00526840089e3a4a0d12e8b2a2~tplv-73owjymdk6-jj-mark-v1:0:0:0:0:5o6Y6YeR5oqA5pyv56S-5Yy6IEAg5biD5a6i6aOe6b6Z:q75.awebp?rk3s=f64ab15b&x-expires=1771469965&x-signature=VtXUUDZYntopnKbm5bjswJaVsPA%3D)

绘制直方图

让我们看看如何在这个食谱中绘制直方图。我们将比较两组数据，并建立一个比较直方图。

怎么做…

1. 新建一个 Python 文件，导入以下包:

   import numpy as np
   import matplotlib.pyplot as plt 
   

2. 我们来对比一下这个食谱中苹果和橘子的产量。让我们定义一些值:

   # Input data
   apples = [30, 25, 22, 36, 21, 29]
   oranges = [24, 33, 19, 27, 35, 20]
   
   # Number of groups
   num_groups = len(apples)
   

3. 创建图形并定义其参数:

   # Create the figure
   fig, ax = plt.subplots()
   
   # Define the X axis
   indices = np.arange(num_groups)
   
   # Width and opacity of histogram bars
   bar_width = 0.4
   opacity = 0.6
   

4. 绘制直方图:

   # Plot the values
   hist_apples = plt.bar(indices, apples, bar_width, 
           alpha=opacity, color='g', label='Apples')
   
   hist_oranges = plt.bar(indices + bar_width, oranges, bar_width,
           alpha=opacity, color='b', label='Oranges')
   

5. 设置绘图参数:

   plt.xlabel('Month')
   plt.ylabel('Production quantity')
   plt.title('Comparing apples and oranges')
   plt.xticks(indices + bar_width, ('Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'))
   plt.ylim([0, 45])
   plt.legend()
   plt.tight_layout()
   
   plt.show()
   

6. The full code is in the histogram.py file that's already provided to you. If you run this code, you will see the following figure:

   

可视化热图

让我们看看如何在这个食谱中可视化热图。这是数据的图形表示，其中两个组是逐点关联的。矩阵中包含的单个值在图中表示为颜色值。

怎么做…

1. 新建一个 Python 文件，导入以下包:

   import numpy as np
   import matplotlib.pyplot as plt
   

2. 定义两组:

   # Define the two groups 
   group1 = ['France', 'Italy', 'Spain', 'Portugal', 'Germany'] 
   group2 = ['Japan', 'China', 'Brazil', 'Russia', 'Australia']
   

3. 生成随机 2D 矩阵:

   # Generate some random values
   data = np.random.rand(5, 5)
   

4. 创建图形:

   # Create a figure
   fig, ax = plt.subplots()
   

5. 创建热图:

   # Create the heat map
   heatmap = ax.pcolor(data, cmap=plt.cm.gray)
   

6. 绘制这些值:

   # Add major ticks at the middle of each cell
   ax.set_xticks(np.arange(data.shape[0]) + 0.5, minor=False)
   ax.set_yticks(np.arange(data.shape[1]) + 0.5, minor=False)
   
   # Make it look like a table 
   ax.invert_yaxis()
   ax.xaxis.tick_top()
   
   # Add tick labels
   ax.set_xticklabels(group2, minor=False)
   ax.set_yticklabels(group1, minor=False)
   
   plt.show()
   

7. The full code is in the heatmap.py file that's provided to you. If you run this code, you will see the following figure:

   

动态信号动画

当我们可视化实时信号时，很高兴看到波形是如何建立的。在这个食谱中，我们将看到如何制作动态信号的动画，并在实时遇到它们时可视化它们。

怎么做…

1. 新建一个 Python 文件，导入以下包:

   import numpy as np
   import matplotlib.pyplot as plt
   import matplotlib.animation as animation 
   

2. 创建一个生成阻尼正弦信号的函数:

   # Generate the signal
   def generate_data(length=2500, t=0, step_size=0.05):
       for count in range(length):
           t += step_size
           signal = np.sin(2*np.pi*t)
           damper = np.exp(-t/8.0)
           yield t, signal * damper 
   

3. 定义一个 initializer函数来初始化图的参数:

   # Initializer function
   def initializer():
       peak_val = 1.0
       buffer_val = 0.1
   

4. 设置这些参数:

       ax.set_ylim(-peak_val * (1 + buffer_val), peak_val * (1 + buffer_val))
       ax.set_xlim(0, 10)
       del x_vals[:]
       del y_vals[:]
       line.set_data(x_vals, y_vals)
       return line
   

5. 定义一个函数来绘制值:

   def draw(data):
       # update the data
       t, signal = data
       x_vals.append(t)
       y_vals.append(signal)
       x_min, x_max = ax.get_xlim()
   

6. 如果这些值超过了当前的 X 轴限制，则更新并扩展图表:

       if t >= x_max:
           ax.set_xlim(x_min, 2 * x_max)
           ax.figure.canvas.draw()
   
       line.set_data(x_vals, y_vals)
   
       return line
   

7. 定义main功能:

   if __name__=='__main__':
       # Create the figure
       fig, ax = plt.subplots()
       ax.grid()
   

8. 提取行:

       # Extract the line
       line, = ax.plot([], [], lw=1.5)
   

9. 创建变量并将其初始化为空列表:

       # Create the variables
       x_vals, y_vals = [], []
   

10. 使用动画师对象

```py

    # Define the animator object
    animator = animation.FuncAnimation(fig, draw, generate_data, 
            blit=False, interval=10, repeat=False, init_func=initializer)

    plt.show()
```

定义并开始动画

11. The full code is in the moving_wave_variable.py file that's already provided to you. If you run this code, you will see the following figure:

![How to do it…](https://p6-xtjj-sign.byteimg.com/tos-cn-i-73owjymdk6/59909783f4504cd9a1fa917ff6f5b4ca~tplv-73owjymdk6-jj-mark-v1:0:0:0:0:5o6Y6YeR5oqA5pyv56S-5Yy6IEAg5biD5a6i6aOe6b6Z:q75.awebp?rk3s=f64ab15b&x-expires=1771469965&x-signature=TtxItQnF%2BvrPSN8GHBA%2B1%2FHRPAo%3D)