Section outline

  • 我们将更深入地研究这些数据集中的一个。我们来看一下数字数据集 (digits data set)。我们先加载它：

    Python 
    from sklearn.datasets import load_digits
digits = load_digits()

    同样，我们可以通过查看 "keys" 来获取可用属性的概览：

    Python 
    digits.keys()

    输出：dict_keys(['data', 'target', 'target_names', 'images', 'DESCR'])

    我们来看看项目和特征的数量：

    Python 
    n_samples, n_features = digits.data.shape
print((n_samples, n_features))

    输出：

    (1797, 64)
    Python 
    print(digits.data[0])
print(digits.target)

    输出：

    [ 0.  0.  5. 13.  9.  1.  0.  0.  0.  0. 13. 15. 10. 15.  5.  0.  0.  3.
 15.  2.  0. 11.  8.  0.  0.  4. 12.  0.  0.  8.  8.  0.  0.  5. 16.  8.
  0.  0.  9.  8.  0.  0.  4. 11.  0.  1. 12.  7.  0.  0.  2. 14.  5.  0.
  0.  0.  0.  6. 13. 10.  0.  0.  0.]
[0 1 2 ... 8 9 8]

    数据也可以通过 digits.images 获取。这是以 8 行 8 列形式表示的图像的原始数据。

    通过 "data"，一张图像对应一个长度为 64 的一维 Numpy 数组；而 "images" 表示则包含形状为 (8, 8) 的二维 Numpy 数组。

    Python 
    print("一个项目的形状: ", digits.data[0].shape)
print("一个项目的数据类型: ", type(digits.data[0]))
print("一个项目的形状: ", digits.images[0].shape)
print("一个项目的数据类型: ", type(digits.images[0]))

    输出：

    一个项目的形状: (64,)
一个项目的数据类型: <class 'numpy.ndarray'>
一个项目的形状: (8, 8)
一个项目的数据类型: <class 'numpy.ndarray'>

    让我们将数据可视化。这比我们上面使用的简单散点图稍微复杂一些，但我们可以很快完成。

    Python 
    import matplotlib.pyplot as plt

# 设置图表
fig = plt.figure(figsize=(6, 6)) # 图表大小（英寸）
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)

# 绘制数字：每个图像都是 8x8 像素
for i in range(64):
    ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])
    ax.imshow(digits.images[i], cmap=plt.cm.binary, interpolation='nearest')
    # 用目标值标记图像
    ax.text(0, 7, str(digits.target[i]))
plt.show()

    练习

    练习 1

    sklearn 中包含一个“葡萄酒数据集 (wine data set)”。

    • 找到并加载此数据集。

    • 您能找到它的描述吗？

    • 类别的名称是什么？

    • 特征是什么？

    • 数据和带标签的数据在哪里？

    练习 2

    创建葡萄酒数据集中特征 ash 和 color_intensity 的散点图。

    练习 3

    创建葡萄酒数据集特征的散点矩阵。

    练习 4

    获取 Olivetti 人脸数据集并可视化这些人脸。

    解决方案

    练习 1 解决方案

    加载“葡萄酒数据集”：

    Python 
    from sklearn import datasets
wine = datasets.load_wine()

    描述可以通过 "DESCR" 访问：

    Python 
    print(wine.DESCR)

    类别的名称和特征可以通过以下方式获取：

    Python 
    print(wine.target_names)
print(wine.feature_names)

    输出：

    ['class_0' 'class_1' 'class_2']
['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']

    数据和带标签的数据：

    Python 
    data = wine.data
labelled_data = wine.target

    练习 2 解决方案

    Python 
    from sklearn import datasets
import matplotlib.pyplot as plt

wine = datasets.load_wine()
features = 'ash', 'color_intensity'
features_index = [wine.feature_names.index(features[0]),
                  wine.feature_names.index(features[1])]
colors = ['blue', 'red', 'green']

for label, color in zip(range(len(wine.target_names)), colors):
    plt.scatter(wine.data[wine.target==label, features_index[0]],
                wine.data[wine.target==label, features_index[1]],
                label=wine.target_names[label],
                c=color)

plt.xlabel(features[0])
plt.ylabel(features[1])
plt.legend(loc='upper left')
plt.show()

    练习 3 解决方案

    Python 
    import pandas as pd
from sklearn import datasets
import matplotlib.pyplot as plt # 导入 matplotlib 以便显示图表

wine = datasets.load_wine()

def rotate_labels(df, axes):
    """ 改变标签输出的旋转角度，
        y 轴标签水平，x 轴标签垂直
    """
    n = len(df.columns)
    for x in range(n):
        for y in range(n):
            # 获取子图的轴
            ax = axes[x, y]
            # 使 x 轴名称垂直
            ax.xaxis.label.set_rotation(90)
            # 使 y 轴名称水平
            ax.yaxis.label.set_rotation(0)
            # 确保 y 轴名称在绘图区域之外
            ax.yaxis.labelpad = 50

wine_df = pd.DataFrame(wine.data, columns=wine.feature_names)
axs = pd.plotting.scatter_matrix(wine_df,
                                 c=wine.target,
                                 figsize=(8, 8),
                                )
rotate_labels(wine_df, axs)
plt.show() # 显示图表

    练习 4 解决方案

    Python 
    from sklearn.datasets import fetch_olivetti_faces
import numpy as np
import matplotlib.pyplot as plt

# 获取人脸数据
faces = fetch_olivetti_faces()
faces.keys()

    输出：dict_keys(['data', 'images', 'target', 'DESCR'])

    Python 
    n_samples, n_features = faces.data.shape
print((n_samples, n_features))

    输出：

    (400, 4096)
    Python 
    np.sqrt(4096)

    输出：64.0

    Python 
    faces.images.shape

    输出：(400, 64, 64)

    Python 
    faces.data.shape

    输出：(400, 4096)

    Python 
    print(np.all(faces.images.reshape((400, 4096)) == faces.data))

    输出：True

    Python 
    # 设置图表
fig = plt.figure(figsize=(6, 6)) # 图表大小（英寸）
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)

# 绘制人脸：每个图像是 64x64 像素
for i in range(64):
    ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])
    ax.imshow(faces.images[i], cmap=plt.cm.bone, interpolation='nearest')
    # 用目标值标记图像
    ax.text(0, 7, str(faces.target[i]))
plt.show()

    更多数据集

    sklearn 提供了许多其他数据集。如果您还需要更多，可以在维基百科的“机器学习研究数据集列表”中找到更多有用的信息。

    We will have a closer look at one of these datasets. We look at the digits data set. We will load it first:
    from sklearn.datasets import load_digits
    digits = load_digits()
    Again, we can get an overview of the available attributes by looking at the "keys":
    digits.keys()
    Output:dict_keys(['data', 'target', 'target_names', 'images', 'DESC
    R'])
    Let's have a look at the number of items and features:
    n_samples, n_features = digits.data.shape
    print((n_samples, n_features))
    (1797, 64)
    print(digits.data[0])
    print(digits.target)
    [ 0. 0. 5. 13. 9. 1. 0.
     0.
     0.
     0. 13. 15. 10. 15.
     5.
     0.
    0. 3.
    15. 2. 0. 11. 8. 0. 0.
     4. 12.
     0.
     0.
     8.
     8.
     0.
     0.
     5.
    8. 0.
    0. 9. 8. 0. 0. 4. 11.
     0.
     1. 12.
     7.
     0.
     0.
     2. 14.
     5. 1
    0. 12.
    0. 0. 0. 0. 6. 13. 10.
     0.
     0.
     0.]
    [0 1 2 ... 8 9 8]
    The data is also available at digits.images. This is the raw data of the images in the form of 8 lines and 8
    columns.
    With "data" an image corresponds to a one-dimensional Numpy array with the length 64, and "images"
    representation contains 2-dimensional numpy arrays with the shape (8, 8)
    print("Shape of an item: ", digits.data[0].shape)
    print("Data type of an item: ", type(digits.data[0]))
    print("Shape of an item: ", digits.images[0].shape)
    31
    print("Data tpye of an item: ", type(digits.images[0]))
    Shape of an item: (64,)
    Data type of an item: <class 'numpy.ndarray'>
    Shape of an item: (8, 8)
    Data tpye of an item: <class 'numpy.ndarray'>
    Let's visualize the data. It's little bit more involved than the simple scatter-plot we used above, but we can do it
    rather quickly.
    # set up the figure
    fig = plt.figure(figsize=(6, 6)) # figure size in inches
    fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.0
    5, wspace=0.05)
    # plot the digits: each image is 8x8 pixels
    for i in range(64):
    ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])
    ax.imshow(digits.images[i], cmap=plt.cm.binary, interpolatio
    n='nearest')
    # label the image with the target value
    ax.text(0, 7, str(digits.target[i]))
    32
    EXERCISES
    EXERCISE 1
    sklearn contains a "wine data set".
    •
    •
    •
    •
    •
    Find and load this data set
    Can you find a description?
    What are the names of the classes?
    What are the features?
    Where is the data and the labeled data?
    EXERCISE 2:
    Create a scatter plot of the features ash and color_intensity of the wine data set.
    33
    EXERCISE 3:
    Create a scatter matrix of the features of the wine dataset.
    EXERCISE 4:
    Fetch the Olivetti faces dataset and visualize the faces.
    SOLUTIONS
    SOLUTION TO EXERCISE 1
    Loading the "wine data set":
    from sklearn import datasets
    wine = datasets.load_wine()
    The description can be accessed via "DESCR":
    In [ ]:
    print(wine.DESCR)
    The names of the classes and the features can be retrieved like this:
    print(wine.target_names)
    print(wine.feature_names)
    ['class_0' 'class_1' 'class_2']
    ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesiu
    m', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proant
    hocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wine
    s', 'proline']
    data = wine.data
    labelled_data = wine.target
    SOLUTION TO EXERCISE 2:
    from sklearn import datasets
    import matplotlib.pyplot as plt
    34
    wine = datasets.load_wine()
    features = 'ash', 'color_intensity'
    features_index = [wine.feature_names.index(features[0]),
    wine.feature_names.index(features[1])]
    colors = ['blue', 'red', 'green']
    for label, color in zip(range(len(wine.target_names)), colors):
    plt.scatter(wine.data[wine.target==label, features_index[0]],
    wine.data[wine.target==label, features_index[1]],
    label=wine.target_names[label],
    c=color)
    plt.xlabel(features[0])
    plt.ylabel(features[1])
    plt.legend(loc='upper left')
    plt.show()
    SOLUTION TO EXERCISE 3:
    import pandas as pd
    from sklearn import datasets
    wine = datasets.load_wine()
    def rotate_labels(df, axes):
    """ changing the rotation of the label output,
    y labels horizontal and x labels vertical """
    35
    n = len(df.columns)
    for x in rangeNo:
    for y in rangeNo:
    # to get the axis of subplots
    ax = axs[x, y]
    # to make x axis name vertical
    ax.xaxis.label.set_rotation(90)
    # to make y axis name horizontal
    ax.yaxis.label.set_rotation(0)
    # to make sure y axis names are outside the plot area
    ax.yaxis.labelpad = 50
    wine_df = pd.DataFrame(wine.data, columns=wine.feature_names)
    axs = pd.plotting.scatter_matrix(wine_df,
    c=wine.target,
    figsize=(8, 8),
    );
    rotate_labels(wine_df, axs)
    36
    SOLUTION TO EXERCISE 4
    from sklearn.datasets import fetch_olivetti_faces
    # fetch the faces data
    faces = fetch_olivetti_faces()
    faces.keys()
    Output:dict_keys(['data', 'images', 'target', 'DESCR'])
    37
    n_samples, n_features = faces.data.shape
    print((n_samples, n_features))
    (400, 4096)
    np.sqrt(4096)
    Output:64.0
    faces.images.shape
    Outputsad400, 64, 64)
    faces.data.shape
    Outputsad400, 4096)
    print(np.all(faces.images.reshape((400, 4096)) == faces.data))
    True
    # set up the figure
    fig = plt.figure(figsize=(6, 6)) # figure size in inches
    fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.0
    5, wspace=0.05)
    # plot the digits: each image is 8x8 pixels
    for i in range(64):
    ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])
    ax.imshow(faces.images[i], cmap=plt.cm.bone, interpolation='ne
    arest')
    # label the image with the target value
    ax.text(0, 7, str(faces.target[i]))
    38
    FURTHER DATASETS
    sklearn has many more datasets available. If you still need more, you will find more on this nice List of
    datasets for machine-learning research at Wikipedia.