def get_word_embeddings(layer):
path = io.get_word_embeddings_path(layer)
exist(
path,
"No cached embeddings. Run the model first with the --plot option enabled."
)
return io.read_csv(path, sep=",").to_numpy()
def analyze_doc_embeddings(dataset, path, id1, id2, filename):
embeddings = io.read_csv(path, sep=",")
reduced_emb_doc = reduce_dimensions(embeddings)
doc_labels = generate_doc_labels(embeddings, dataset)
label1 = doc_labels[id1]
label2 = doc_labels[id2]
# assert label1 == label2
visualize_highlight(reduced_emb_doc, id1, id2, label1, filename=filename, labels=doc_labels, colors=dataset_colors[dataset])
def analyze_word_embeddings(dataset, path, threshold, edge_type, best, n=10):
global top_words_dict
embeddings = io.read_csv(path, sep=",")
embeddings_array = embeddings.to_numpy().tolist()
unique_labels = sorted(list(set([label.split("\t")[2] for label in file.get_labels(dataset)])), reverse=True)
vocab = file.get_vocab(dataset)
max_indices = []
max_values = []
all_words = []
all_labels = []
results_dict = {}
for index, emb in enumerate(embeddings_array):
array = np.array(emb)
max_index = array.argmax()
max_indices.append(max_index)
max_values.append(array[max_index])
all_words.append(vocab[index])
all_labels.append(unique_labels[max_index])
results_dict[index] = {
"max_index": max_index,
"max_value": array[max_index],
"word": vocab[index],
"label": unique_labels[max_index]
}
assert len(max_values) == len(max_indices) == len(all_words) == len(all_labels)
results_df = pd.DataFrame.from_dict(results_dict,orient="index")
top_words = {}
for u_label in unique_labels:
largest = results_df[results_df["label"] == u_label].nlargest(n, columns="max_value")["word"].tolist()
top_words[u_label] = largest
# print(top_words)
# key = f"{threshold}:{edge_type}"
if best:
top_words_dict[dataset][edge_type] = top_words
def get_ordered_document_triples_metrics(edge_type, dataset=FLAGS.dataset):
path = io.get_ordered_document_triples_metrics_path(edge_type, dataset)
exist(path, "Ordered document triples metrics do not exist yet.")
return io.read_csv(path, sep=",")
def get_eval_logs(dataset=FLAGS.dataset, version=FLAGS.version):
path = io.get_eval_log_path(dataset, version)
if not exists(path):
return None
return io.read_csv(path, sep=';')
def get_filtered_triples(dataset=FLAGS.dataset):
return io.read_csv(io.get_filtered_word_triples_path(dataset), sep=",")
def get_all_relations():
path = io.get_all_wiki_relations_path()
exist(path, "Run `analyze_properties.py` to generate this file")
return io.read_csv(path, sep="+")
def get_filtered_relations():
path = io.get_filtered_wiki_relations_path()
exist(path, "Run `analyze_properties.py` to generate this file")
return io.read_csv(path, sep="\n")["ID"].tolist()
def get_doc2idf(dataset=FLAGS.dataset):
return io.read_csv(io.get_doc2idf_path(dataset), sep=",")
def get_entity2id(dataset=FLAGS.dataset):
return io.read_csv(io.get_entity2id_path(dataset), sep=",")
