Plot LSA的时候出现了下面的 ：
from sklearn.decomposition import PCA, TruncatedSVD
from sklearn.linear_model import LogisticRegression
import matplotlib
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
def plot_LSA(test_data, test_labels, savepath="PCA_demo.csv", plot=True):
lsa = TruncatedSVD(n_components=2) # Truncated SVD works on term count/tf-idf matrices as returned by the vectorizers in sklearn.feature_extraction.text. In that context, it is known as latent semantic analysis (LSA).
lsa.fit(test_data)
lsa_scores = lsa.transform(test_data)
color_mapper = {label:idx for idx,label in enumerate(set(test_labels))}
color_column = [color_mapper[label] for label in test_labels]
print ('colormapper=',color_mapper)
#print ('colorColumn=',color_column)
colors = ['blue','green','red']
if plot:
plt.scatter(lsa_scores[:,0], lsa_scores[:,1], s=8, alpha=.8, c=test_labels, cmap=matplotlib.colors.ListedColormap(colors))
red_patch = mpatches.Patch(color='red', label='Negative')
blue_patch = mpatches.Patch(color='blue', label='Neutral')
green_patch = mpatches.Patch(color='green', label='Positive')
plt.legend(handles=[red_patch, green_patch, blue_patch], prop={'size': 30})
fig = plt.figure(figsize=(16, 16))
plot_LSA(X_train_counts, y_train)
plt.show()
ValueError: Found array with 1 feature(s) (shape=(11822, 1)) while a minimum of 2 is required.
请大家帮忙解答一下。谢谢！
