戒酒的李白
78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
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(
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"samples,features,centers",
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[
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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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],
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)
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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(
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min_cluster_size=10,
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metric="euclidean",
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cluster_selection_method="eom",
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prediction_data=True,
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)
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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(
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"samples,features,centers",
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[
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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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],
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)
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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(
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min_cluster_size=10,
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metric="euclidean",
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cluster_selection_method="eom",
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prediction_data=True,
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)
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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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