導入模塊時，出現有關未定義某些變量的NameError（即使它是）

Question

因此，我試圖將模塊/腳本（.py文件）導入Jupyter筆記本，主要是為了提高可讀性和簡潔性。 但是，當我嘗試在腳本中運行該類時，出現以下錯誤消息：

NameError                                 Traceback (most recent call last)
<ipython-input-48-4d8cbba46ed0> in <module>()
      8 
      9 test_KMeans = KMeans(k=3, maxiter=1000, tol=1e-9)
---> 10 cluster_center = test_KMeans.fit(X)
     11 clusters = test_KMeans.predict(X)
     12 

~/KMeans.py in fit(self, X)
     42         #Choose k random rows of X as the initial cluster centers.
     43         initial_cluster_centers = []
---> 44 
     45         sample = np.random.randint(0,m,size=k)
     46 

NameError: name 'maxiter' is not defined

這是我的腳本：

import numpy as np
from sklearn.decomposition import PCA

k = 3
maxiter = 1000
tol = 1e-9

class KMeans:
    """A K-Means object class. Implements basic k-means clustering.

    Attributes:
        k (int): The number of clusters
        maxiter (int): The maximum number of iterations
        tol (float): A convergence tolerance
    """
    def __init__(self, k, maxiter, tol):
        """Set the paramters.

        Parameters:
            k (int): The number of clusters
            maxiter (int): The maximum number of iterations
            tol (float): A convergence tolerance
        """
        k = 3
        maxiter = 1000
        tol = 1e-9

        self.k = k   # Initialize some attributes.
        self.maxiter = maxiter
        self.tol = tol

    def fit(self, X):
        """Accepts an mxn matrix X of m data points with n features.
        """
        m,n = X.shape
        k = 3
        maxiter = 1000
        tol = 1e-9
        self.m = m
        self.n = n

        #Choose k random rows of X as the initial cluster centers.
        initial_cluster_centers = []

        sample = np.random.randint(0,m,size=k)

        initial_cluster_centers = X[sample, :]

        # Run the k-means iteration until consecutive centers are within the convergence tolerance, or until 
        # iterating the maximum number of times.
        iterations = 0
        old_cluster = np.zeros(initial_cluster_centers.shape)
        new_cluster = initial_cluster_centers

        while iterations < maxiter or np.linalg.norm(old_cluster - new_cluster) >= tol:
            #assign each data point to the cluster center that is closest, forming k clusters
            clusters = np.zeros(m)
            for i in range(0,m):
                distances = np.linalg.norm(X[i] - initial_cluster_centers, ord=2, axis=1) # axis=1 was crucial
                cluster = np.argmin(distances)                                            #in getting this to work
                clusters[i] = cluster
            # Store the old/initial centroid values
            old_cluster = np.copy(new_cluster)
            #Recompute the cluster centers as the means of the new clusters
            for i in range(k):
                points = [X[j] for j in range(m) if clusters[j] == i]
                new_cluster[i] = np.mean(points, axis=0)
                #If a cluster is empty, reassign the cluster center as a random row of X.
                if new_cluster[i] == []:
                    new_cluster[i] = X[np.random.randint(0,m,size=1)]
            iterations += 1

        #Save the cluster centers as attributes.
        self.new_cluster = new_cluster

        #print("New cluster centers:\n", new_cluster)

        return new_cluster

    def predict(self, X):
        """Accept an l × n matrix X of data.
        """
        # Return an array of l integers where the ith entry indicates which 
        # cluster center the ith row of X is closest to.
        clusters = np.zeros(self.m)
        for i in range(0,self.m):
            distances = np.linalg.norm(X[i] - self.new_cluster, ord=2, axis=1)
            cluster = np.argmin(distances)
            clusters[i] = cluster

        print("\nClusters:", clusters)

        return clusters  

然后，我嘗試執行以下操作：

from KMeans import KMeans

X = features_scaled

# k = 3
# maxiter = 1000
# tol = 1e-9

test_KMeans = KMeans(k=3, maxiter=1000, tol=1e-9)
cluster_center = test_KMeans.fit(X)
clusters = test_KMeans.predict(X)

pca = PCA(n_components=2)

pr_components = pca.fit_transform(X) # these are the first 2 principal components

#plot the first two principal components as a scatter plot, where the color of each point is det by the clusters
plt.scatter(pr_components[:,0], pr_components[:,1],
           c=clusters, edgecolor='none', alpha=0.5, #color by clusters
            cmap=plt.cm.get_cmap('tab10', 3)) 
plt.xlabel('principal component 1')
plt.ylabel('principal component 2')
plt.colorbar()
plt.title("K-Means Clustering:")
plt.show()

運行以上代碼后，我得到了我描述的NameError。 我不明白為什么它告訴我maxiter 。 您會看到我在腳本中多次定義了變量k, maxiter, tol ，試圖使其正常工作，但是沒有任何作用。 我曾經有過self.maxiter和self.tol ，但這也不能解決。

我知道此代碼有效，因為我已經多次使用它。 最初，我只是定義了變量k，maxiter和tol.。然后實例化該類並稱為fit和預測方法，由於它們與self一起存儲為屬性，所以一切正常。 但是現在我嘗試將其作為模塊導入，我不知道為什么它不起作用。

謝謝你的幫助！

編輯：這是我的代碼在Jupyter筆記本中的單個單元格中的樣子。.在這種情況下，它確實可以運行並起作用：

from sklearn.decomposition import PCA

class KMeans:
    """A K-Means object class. Implements basic k-means clustering.

    Attributes:
        k (int): The number of clusters
        maxiter (int): The maximum number of iterations
        tol (float): A convergence tolerance
    """
    def __init__(self, k, maxiter, tol):
        """Set the paramters.

        Parameters:
            k (int): The number of clusters
            maxiter (int): The maximum number of iterations
            tol (float): A convergence tolerance
        """
        self.k = k   # Initialize some attributes.
        self.maxiter = maxiter
        self.tol = tol

    def fit(self, X):
        """Accepts an mxn matrix X of m data points with n features.
        """
        m,n = X.shape
        self.m = m
        self.n = n

        #Choose k random rows of X as the initial cluster centers.
        initial_cluster_centers = []

        sample = np.random.randint(0,m,size=self.k)

        initial_cluster_centers = X[sample, :]

        # Run the k-means iteration until consecutive centers are within the convergence tolerance, or until 
        # iterating the maximum number of times.
        iterations = 0
        old_cluster = np.zeros(initial_cluster_centers.shape)
        new_cluster = initial_cluster_centers

        while iterations < maxiter or np.linalg.norm(old_cluster - new_cluster) >= tol:
            #assign each data point to the cluster center that is closest, forming k clusters
            clusters = np.zeros(m)
            for i in range(0,m):
                distances = np.linalg.norm(X[i] - initial_cluster_centers, ord=2, axis=1) # axis=1 was crucial
                cluster = np.argmin(distances)                                            #in getting this to work
                clusters[i] = cluster
            # Store the old/initial centroid values
            old_cluster = np.copy(new_cluster)
            #Recompute the cluster centers as the means of the new clusters
            for i in range(k):
                points = [X[j] for j in range(m) if clusters[j] == i]
                new_cluster[i] = np.mean(points, axis=0)
                #If a cluster is empty, reassign the cluster center as a random row of X.
                if new_cluster[i] == []:
                    new_cluster[i] = X[np.random.randint(0,m,size=1)]
            iterations += 1

        #Save the cluster centers as attributes.
        self.new_cluster = new_cluster

        #print("New cluster centers:\n", new_cluster)

        return new_cluster

    def predict(self, X):
        """Accept an l × n matrix X of data.
        """
        # Return an array of l integers where the ith entry indicates which 
        # cluster center the ith row of X is closest to.
        clusters = np.zeros(self.m)
        for i in range(0,self.m):
            distances = np.linalg.norm(X[i] - self.new_cluster, ord=2, axis=1)
            cluster = np.argmin(distances)
            clusters[i] = cluster

        print("\nClusters:", clusters)

        return clusters

X = features_scaled

k = 3
maxiter = 1000
tol = 1e-9

test_KMeans = KMeans(k,maxiter,tol)
test_KMeans.fit(X)
clusters = test_KMeans.predict(X)

pca = PCA(n_components=2)

pr_components = pca.fit_transform(X) # these are the first 2 principal components

#plot the first two principal components as a scatter plot, where the color of each point is det by the clusters
plt.scatter(pr_components[:,0], pr_components[:,1],
           c=clusters, edgecolor='none', alpha=0.5, #color by clusters
            cmap=plt.cm.get_cmap('tab10', 3)) 
plt.xlabel('principal component 1')
plt.ylabel('principal component 2')
plt.colorbar()
plt.title("K-Means Clustering:")
plt.show()

Answer 1

追溯似乎表明Jupyter與Kmeans.py中的當前代碼狀態不同步（因為它指向第44行...這是空的）。 因此，如果計算時間不長，您可以嘗試退出並重新啟動Jupyter，以解決問題。

導入模塊時，Python執行模塊的代碼。 如果在導入模塊后對模塊的代碼進行了更改，則這些更改不會反映在Python解釋器的狀態中。 這可以解釋為什么Jupyter筆記本的錯誤似乎與Kmeans.py的狀態不同步。

除了退出並重新啟動Python外，您還可以重新加載modules 。 例如，在Python3.4或更高版本中，您可以使用

import sys
import importlib
from Kmeans import Kmeans

# make changes to Kmeans.py
importlib.reload(sys.modules['Kmeans'])
# now the Python interpreter should be aware of changes made to Kmeans.py

但是，使用IPython，有一種更簡單的方法。 您可以啟用自動重新加載 ：

從命令行運行：

ipython profile create

然后通過添加~/.ipython/profile_default/ipython_config.py

c.InteractiveShellApp.extensions = ['autoreload']     
c.InteractiveShellApp.exec_lines = ['%autoreload 2']

退出並重新啟動IPython以使更改生效。 現在，當對定義該模塊的基礎代碼進行更改時，IPython將自動重新加載任何模塊。 在大多數情況下，自動重新加載效果很好，但是在某些情況下，自動重新加載可能會失敗。 有關自動重載及其注意事項的更多信息，請參閱文檔 。