问潜在语义分析结果

EN

import sklearn
# Import all of the scikit learn stuff
from __future__ import print_function
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.preprocessing import Normalizer
from sklearn import metrics
from sklearn.cluster import KMeans, MiniBatchKMeans
import pandas as pd
import warnings
# Suppress warnings from pandas library
warnings.filterwarnings("ignore", category=DeprecationWarning,
module="pandas", lineno=570)
import numpy


example = ["Coffee brewed by expressing or forcing a small amount of 
nearly boiling water under pressure through finely ground coffee 
beans.", 
"An espresso-based coffee drink consisting of espresso with 
microfoam (steamed milk with small, fine bubbles with a glossy or 
velvety consistency)", 
"American fast-food dish, consisting of french fries covered in 
cheese with the possible addition of various other toppings", 
"Pounded and breaded chicken is topped with sweet honey, salty 
dill pickles, and vinegar-y iceberg slaw, then served upon crispy 
challah toast.", 
"A layered, flaky texture, similar to a puff pastry."]

''''
example = ["Machine learning is super fun",
"Python is super, super cool",
"Statistics is cool, too",
"Data science is fun",
"Python is great for machine learning",
"I like football",
"Football is great to watch"]
'''

vectorizer = CountVectorizer(min_df = 1, stop_words = 'english')
dtm = vectorizer.fit_transform(example)
pd.DataFrame(dtm.toarray(),index=example,columns=vectorizer.get_feature_names()).head(10)

# Get words that correspond to each column
vectorizer.get_feature_names()

# Fit LSA. Use algorithm = “randomized” for large datasets
lsa = TruncatedSVD(2, algorithm = 'arpack')
dtm_lsa = lsa.fit_transform(dtm.astype(float))
dtm_lsa = Normalizer(copy=False).fit_transform(dtm_lsa)

pd.DataFrame(lsa.components_,index = ["component_1","component_2"],columns = vectorizer.get_feature_names())

pd.DataFrame(dtm_lsa, index = example, columns = "component_1","component_2"])

xs = [w[0] for w in dtm_lsa]
ys = [w[1] for w in dtm_lsa]
xs, ys

# Plot scatter plot of points
%pylab inline
import matplotlib.pyplot as plt
figure()
plt.scatter(xs,ys)
xlabel('First principal component')
ylabel('Second principal component')
title('Plot of points against LSA principal components')
show()

#Plot scatter plot of points with vectors
%pylab inline
import matplotlib.pyplot as plt
plt.figure()
ax = plt.gca()
ax.quiver(0,0,xs,ys,angles='xy',scale_units='xy',scale=1, linewidth = .01)
ax.set_xlim([-1,1])
ax.set_ylim([-1,1])
xlabel('First principal component')
ylabel('Second principal component')
title('Plot of points against LSA principal components')
plt.draw()
plt.show()

# Compute document similarity using LSA components
similarity = np.asarray(numpy.asmatrix(dtm_lsa) * 
numpy.asmatrix(dtm_lsa).T)
pd.DataFrame(similarity,index=example, columns=example).head(10)