The Base Cluster class. Using this class directly in BERTopic will make it skip over the cluster step. As a result, topics need to be passed to BERTopic in the form of its `y` parameter in order to create topic representations. Examples: This will skip over the cluster step
| 2 | |
| 3 | |
| 4 | class BaseCluster: |
| 5 | """The Base Cluster class. |
| 6 | |
| 7 | Using this class directly in BERTopic will make it skip |
| 8 | over the cluster step. As a result, topics need to be passed |
| 9 | to BERTopic in the form of its `y` parameter in order to create |
| 10 | topic representations. |
| 11 | |
| 12 | Examples: |
| 13 | This will skip over the cluster step in BERTopic: |
| 14 | |
| 15 | ```python |
| 16 | from bertopic import BERTopic |
| 17 | from bertopic.cluster import BaseCluster |
| 18 | |
| 19 | empty_cluster_model = BaseCluster() |
| 20 | |
| 21 | topic_model = BERTopic(hdbscan_model=empty_cluster_model) |
| 22 | ``` |
| 23 | |
| 24 | Then, this class can be used to perform manual topic modeling. |
| 25 | That is, topic modeling on a topics that were already generated before |
| 26 | without the need to learn them: |
| 27 | |
| 28 | ```python |
| 29 | topic_model.fit(docs, y=y) |
| 30 | ``` |
| 31 | """ |
| 32 | |
| 33 | def fit(self, X, y=None): |
| 34 | if y is not None: |
| 35 | self.labels_ = y |
| 36 | else: |
| 37 | self.labels_ = None |
| 38 | return self |
| 39 | |
| 40 | def transform(self, X: np.ndarray) -> np.ndarray: |
| 41 | return X |
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