print([book["book_id3"] for book in books])
print()
# Create term-document representation
X = preprocessing_util.convert_to_term_document(documents, min_df=0.1, max_df=0.9)
for feature_number in range(10,24,4):
print("Features: {}".format(feature_number))
# SVD
Y = preprocessing_util.apply_svd(X, feature_number)
# Cosine similarity matrix
dist = 1 - cosine_similarity(Y)
###############################################################################
# Do the actual clustering
k = 4
ac = AgglomerativeClustering(linkage="average", n_clusters=k, affinity="cosine")
print("Clustering sparse data with {}".format(ac))
t0 = time()
ac.fit(dist)
print("done in {}".format(time() - t0))
print()
# Create a 3d scatter plot of the corpus
plot_util.create_3d_plot_for_sparse_matrix(Y, ac.labels_, show=False)
plt.show()
books = collection_reader.read_books_from_mongo()
documents = collection_reader.extract_corpus(books)
print("{} books:".format(len(documents)))
print([book["book_id3"] for book in books])
print()
# Create term-document representation
X = preprocessing_util.convert_to_term_document(documents,
min_df=0.1,
max_df=0.9)
# SVD
X = preprocessing_util.apply_svd(X, min(X.shape))
###############################################################################
# Do the actual clustering
print("Clustering data")
k = 5
model = Birch(branching_factor=8,
n_clusters=k,
threshold=0.5,
compute_labels=True)
model.fit(X)
# Metrics
benchmark.clustering_metrics(X, model.predict(X))
# Create a 3d scatter plot of the corpus
plot_util.create_3d_plot_for_sparse_matrix(X, model.predict(X))
from util import plot_util, preprocessing_util, benchmark, collection_reader
if __name__ == "__main__":
# Read data
books = collection_reader.read_books_from_mongo()
documents = collection_reader.extract_corpus(books)
print("{} books:".format(len(documents)))
print([book["book_id3"] for book in books])
print()
# Create term-document representation
X = preprocessing_util.convert_to_term_document(documents,
min_df=0.1,
max_df=0.9)
# SVD
X = preprocessing_util.apply_svd(X, min(X.shape))
###############################################################################
# Do the actual clustering
print("Clustering data")
k = 4
method = DBSCAN(eps=0.8, min_samples=1).fit(X)
# Metrics
benchmark.clustering_metrics(X, method.labels_)
# Create a 3d scatter plot of the corpus
plot_util.create_3d_plot_for_sparse_matrix(X, method.labels_)
###############################################################################
# Do the actual clustering
k = 5
import matplotlib.pyplot as plt
the_metrics = []
for i in range(1):
km = KMeans(n_clusters=k, verbose=False)
#print("Clustering sparse data with {}".format(km))
#t0 = time()
km.fit(X)
#print("done in %0.3fs" % (time() - t0))
#print()
# Metrics
the_metrics.append(benchmark.clustering_metrics(X, km.labels_))
# Plot:
# create_2d_plot_for_sparse_matrix(X, km.labels_)
plot_util.create_3d_plot_for_sparse_matrix(X, km.labels_, block=False)
print("All the metrics: ")
for a in the_metrics:
print(a)
plt.show()
print("Done!")