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