import utilities
REUTERS_DIRECTORY = "/home/0/srini/WWW/674/public/reuters"
# Change num_files to a sample size of between 1-21 reuters files
vector_dataset = utilities.preprocessData(directoy=REUTERS_DIRECTORY, num_files=5)
just_body = vector_dataset["words_vectors"]
topic_and_body = vector_dataset["words_and_topics_vectors"]
ground_truth_labels = vector_dataset["topics_classes"]
# metric can be any in scipy.spatial.distance module
# i.e. 'minkowski', 'euclidean', 'dice', 'jaccard', 'cosine', etc.
# num_means should be < 50 for a sample of under 8 files or an error may be thrown
k_results = utilities.kmeans_cluster(topic_and_body, ground_truth_labels, num_means=40, metric="euclidean")
# k_results = utilities.kmeans_cluster(just_body, ground_truth_labels, num_means=40, metric='euclidean')
kclusters = k_results["clusters"] # to see clustered labels in an interactive console
# linkage can be 'complete','average' 'ward' (only for euclidean)
# metric can be 'cosine', 'manhatten', 'euclidean'
h_results = utilities.hierarchical_cluster(
topic_and_body, ground_truth_labels, number_of_leafs=40, linkage="complete", metric="euclidean"
)
# h_results = utilities.hierarchical_cluster(just_body, ground_truth_labels, number_of_leafs=leafs, linkage='complete', metric='euclidean')
hclusters = h_results["clusters"] # to see clustered labels in an interactive console
#!/usr/bin/env python import utilities REUTERS_DIRECTORY = "/home/0/srini/WWW/674/public/reuters" #REUTERS_DIRECTORY = "../reuters" # Change num_files to a sample size of between 1-21 reuters files vector_dataset = utilities.preprocessData(reuters_directory=REUTERS_DIRECTORY, num_files=8) just_body = vector_dataset["words_vectors"] topic_and_body = vector_dataset["words_and_topics_vectors"] ground_truth_labels = vector_dataset["topics_classes"] int_labels = vector_dataset["topics_ints"] # utilities.knn_classify(topic_and_body, int_labels, 0.5) # utilities.naive_bayes_classify(topic_and_body, int_labels, 0.5) # utilities.dtree_classify(topic_and_body, int_labels, 0.5) utilities.knn_classify(topic_and_body, int_labels, 0.66) utilities.naive_bayes_classify(topic_and_body, int_labels, 0.66) utilities.dtree_classify(topic_and_body, int_labels, 0.66) # utilities.knn_classify(topic_and_body, int_labels, 0.8) # utilities.naive_bayes_classify(topic_and_body, int_labels, 0.8) # utilities.dtree_classify(topic_and_body, int_labels, 0.8)