kmeans scatter plot: plot different colors per cluster

Question

I am trying to do a scatter plot of a kmeans output which clusters sentences of the same topic together. The problem i am facing is plotting points that belongs to each cluster a certain color.

sentence_list=["Hi how are you", "Good morning" ...] #i have 10 setences km = KMeans(n_clusters=5, init='k-means++',n_init=10, verbose=1)  #with 5 cluster, i want 5 different colors km.fit(vectorized) km.labels_ # [0,1,2,3,3,4,4,5,2,5]  pipeline = Pipeline([('tfidf', TfidfVectorizer())]) X = pipeline.fit_transform(sentence_list).todense() pca = PCA(n_components=2).fit(X) data2D = pca.transform(X) plt.scatter(data2D[:,0], data2D[:,1])  km.fit(X) centers2D = pca.transform(km.cluster_centers_) plt.hold(True) labels=np.array([km.labels_]) print labels 

My problem is in the bottom code for plt.scatter(); what should i use for the parameter c?

1. when i use c=labels in the code, i get this error:

number in rbg sequence outside 0-1 range

2.When i set c= km.labels_ instead, i get the error:

ValueError: Color array must be two-dimensional

plt.scatter(centers2D[:,0], centers2D[:,1],              marker='x', s=200, linewidths=3, c=labels) plt.show() 

Answer 1

from sklearn.cluster import KMeans import matplotlib.pyplot as plt  # Scaling the data to normalize model = KMeans(n_clusters=5).fit(X)  # Visualize it: plt.figure(figsize=(8, 6)) plt.scatter(data[:,0], data[:,1], c=model.labels_.astype(float)) 

Now you have different color for different clusters.

Answer 2

The color= or c= property should be a matplotlib color, as mentioned in the documentation for plot.

To map a integer label to a color just do

LABEL_COLOR_MAP = {0 : 'r',                    1 : 'k',                    ....,                    }  label_color = [LABEL_COLOR_MAP[l] for l in labels] plt.scatter(x, y, c=label_color) 

If you don't want to use the builtin one-character color names, you can use other color definitions. See the documentation on matplotlib colors.