def convert_embeddings(out_path, embeddings_path):
with open(embeddings_path, "rb") as f:
embeddings = np.load(f)
embeddings_tensor = tf.convert_to_tensor(embeddings)
writer = tf.compat.v2.summary.create_file_writer(out_path)
with writer.as_default():
summary.npmi_embeddings(embeddings_tensor, 1)
writer.close()
def generate_testdata(self):
run_names = ["run_1", "run_2"]
ground_truth = {
"run_1": [
["Description", "A", "B"],
["name_1", 1.0, -1.0],
["name_2", -0.5, 0.5],
],
"run_2": [
["Description", "A", "B"],
["name_1", 1.0, -1.0],
["name_2", -0.5, np.nan],
],
}
embedding_ground_truth = {
"run_1": [[1.0, 0.5], [-0.5, 0.5]],
"run_2": [[1.0, 0.5], [-0.5, 0.5]],
}
for run_name in run_names:
subdir = os.path.join(self.logdir, run_name)
writer = tf.compat.v2.summary.create_file_writer(subdir)
data = ground_truth[run_name]
python_result = []
python_annotations = []
python_classes = []
for row_index, row in enumerate(data):
if row_index > 0:
python_result.append([])
for col_index, column in enumerate(row):
if row_index == 0:
if col_index > 0:
python_classes.append(column)
else:
if col_index == 0:
python_annotations.append(column)
else:
python_result[len(python_result) -
1].append(column)
with writer.as_default():
tensor_result = tf.convert_to_tensor(python_result)
tensor_annotations = tf.convert_to_tensor(python_annotations)
tensor_classes = tf.convert_to_tensor(python_classes)
tensor_embeddings = tf.convert_to_tensor(
embedding_ground_truth[run_name])
summary.npmi_values(tensor_result, 1)
summary.npmi_annotations(tensor_annotations, 1)
summary.npmi_metrics(tensor_classes, 1)
summary.npmi_embeddings(tensor_embeddings, 1)
writer.close()