| |
|
| | import pytest |
| | import pandas as pd |
| |
|
| | from sklearn.datasets import make_blobs |
| | from sklearn.cluster import KMeans |
| | from hdbscan import HDBSCAN |
| |
|
| | from bertopic import BERTopic |
| |
|
| |
|
| | @pytest.mark.parametrize("cluster_model", ["hdbscan", "kmeans"]) |
| | @pytest.mark.parametrize("samples,features,centers", |
| | [(200, 500, 1), |
| | (500, 200, 1), |
| | (200, 500, 2), |
| | (500, 200, 2), |
| | (200, 500, 4), |
| | (500, 200, 4)]) |
| | def test_hdbscan_cluster_embeddings(cluster_model, samples, features, centers): |
| | embeddings, _ = make_blobs(n_samples=samples, centers=centers, n_features=features, random_state=42) |
| | documents = [str(i + 1) for i in range(embeddings.shape[0])] |
| | old_df = pd.DataFrame({"Document": documents, "ID": range(len(documents)), "Topic": None}) |
| |
|
| | if cluster_model == "kmeans": |
| | cluster_model = KMeans(n_clusters=centers) |
| | else: |
| | cluster_model = HDBSCAN(min_cluster_size=10, metric="euclidean", cluster_selection_method="eom", prediction_data=True) |
| |
|
| | model = BERTopic(hdbscan_model=cluster_model) |
| | new_df, _ = model._cluster_embeddings(embeddings, old_df) |
| |
|
| | assert len(new_df.Topic.unique()) == centers |
| | assert "Topic" in new_df.columns |
| | pd.testing.assert_frame_equal(old_df.drop("Topic", axis=1), new_df.drop("Topic", axis=1)) |
| |
|
| |
|
| | @pytest.mark.parametrize("cluster_model", ["hdbscan", "kmeans"]) |
| | @pytest.mark.parametrize("samples,features,centers", |
| | [(200, 500, 1), |
| | (500, 200, 1), |
| | (200, 500, 2), |
| | (500, 200, 2), |
| | (200, 500, 4), |
| | (500, 200, 4)]) |
| | def test_custom_hdbscan_cluster_embeddings(cluster_model, samples, features, centers): |
| | embeddings, _ = make_blobs(n_samples=samples, centers=centers, n_features=features, random_state=42) |
| | documents = [str(i + 1) for i in range(embeddings.shape[0])] |
| | old_df = pd.DataFrame({"Document": documents, "ID": range(len(documents)), "Topic": None}) |
| | if cluster_model == "kmeans": |
| | cluster_model = KMeans(n_clusters=centers) |
| | else: |
| | cluster_model = HDBSCAN(min_cluster_size=10, metric="euclidean", cluster_selection_method="eom", prediction_data=True) |
| |
|
| | model = BERTopic(hdbscan_model=cluster_model) |
| | new_df, _ = model._cluster_embeddings(embeddings, old_df) |
| |
|
| | assert len(new_df.Topic.unique()) == centers |
| | assert "Topic" in new_df.columns |
| | pd.testing.assert_frame_equal(old_df.drop("Topic", axis=1), new_df.drop("Topic", axis=1)) |
| |
|
