ModuleNotFoundError: 没有名为“adspy_shared_utilities”的模块

Question

我正在尝试使用该adspy包为我的 KNN 分类器绘制决策边界，但是每当我使用该包时，它都不会导入。我已经使用conda提示下载了几次，但没有任何反应。

带有错误信息的代码：

from adspy_shared_utilities import plot_fruit_knn

plot_fruit_knn(X_train, y_train, 5, 'uniform')


ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-7-ddf0c07df9f1> in <module>()
----> 1 from adspy_shared_utilities import plot_fruit_knn
      2 
      3 plot_fruit_knn(X_train, y_train, 5, 'uniform')

ModuleNotFoundError: No module named 'adspy_shared_utilities'

请问我该如何解决？

Answer 1

没有名为 adspy_shared_utilities 的模块，但这是一些与课程材料一起保存的脚本。您应该将脚本保存在保存 Python 文件的同一目录中。

Answer 2

没有这样的模块。您可以使用下面的代码来可视化该数据 -

import matplotlib.cm as cm
from matplotlib.colors import ListedColormap, BoundaryNorm
import matplotlib.patches as mpatches
import matplotlib.patches as mpatches
X = df[['mass', 'width', 'height', 'color_score']]
y = df['fruit_label']
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

def plot_fruit_knn(X, y, n_neighbors, weights):
    X_mat = X[['height', 'width']].values
    y_mat = y.values
# Create color maps
    cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF','#AFAFAF'])
    cmap_bold  = ListedColormap(['#FF0000', '#00FF00', '#0000FF','#AFAFAF'])
    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
    clf.fit(X_mat, y_mat)
# Plot the decision boundary by assigning a color in the color map
    # to each mesh point.

    mesh_step_size = .01  # step size in the mesh
    plot_symbol_size = 50

    x_min, x_max = X_mat[:, 0].min() - 1, X_mat[:, 0].max() + 1
    y_min, y_max = X_mat[:, 1].min() - 1, X_mat[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, mesh_step_size),
                         np.arange(y_min, y_max, mesh_step_size))
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.figure()
    plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
# Plot training points
    plt.scatter(X_mat[:, 0], X_mat[:, 1], s=plot_symbol_size, c=y, cmap=cmap_bold, edgecolor = 'black')
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    patch0 = mpatches.Patch(color='#FF0000', label='apple')
    patch1 = mpatches.Patch(color='#00FF00', label='mandarin')
    patch2 = mpatches.Patch(color='#0000FF', label='orange')
    patch3 = mpatches.Patch(color='#AFAFAF', label='lemon')
    plt.legend(handles=[patch0, patch1, patch2, patch3])
plt.xlabel('height (cm)')
plt.ylabel('width (cm)')
#plt.title("4-Class classification (k = %i, weights = '%s')" % (n_neighbors, weights))    
plt.show()
plot_fruit_knn(X_train, y_train, 5, 'uniform')

这将给出如下所示的输出图