Итак, я пытаюсь импортировать модуль / скрипт (файл .py) в записную книжку Jupyter, в основном для удобства чтения и краткости.Но затем, когда я пытаюсь запустить класс в сценарии, я получаю следующее сообщение об ошибке:
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
Вот мой сценарий:
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
Затем я пытаюсь сделатьследующее:
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()
После запуска вышеуказанного раздела кода я получаю описанную мной ошибку NameE.Я не понимаю, почему это говорит мне, что maxiter не определено.Вы увидите, что я определил переменные k, maxiter, tol несколько раз в скрипте, пытаясь заставить его работать, но ничего не помогло.У меня были self.maxiter и self.tol в один момент, но это тоже не исправило.
Я знаю, что этот код работает, потому что я использовал его несколько раз.Первоначально я только определил эти переменные k, maxiter и tol. Затем создал экземпляр класса и вызвал методы fit и прогнозирования, и, поскольку они были сохранены как атрибуты с self, все работало нормально.но теперь, когда я пытаюсь импортировать его как модуль, я понятия не имею, почему он не работает.
Спасибо за вашу помощь!
РЕДАКТИРОВАТЬ: Вот как мой код будет выглядеть вотдельная ячейка в блокноте Jupyter. В этом случае он работает и работает:
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()