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Draft for logs (issue MaartenGr#2192)
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@PipaFlores
PipaFlores committed Nov 4, 2024
1 parent c3ec85d commit 478dc12
Showing 1 changed file with 31 additions and 11 deletions.
42 changes: 31 additions & 11 deletions bertopic/_bertopic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2144,7 +2144,7 @@ def merge_topics(
documents.Topic = documents.Topic.map(mapping)
self.topic_mapper_.add_mappings(mapping, topic_model=self)
documents = self._sort_mappings_by_frequency(documents)
self._extract_topics(documents, mappings=mappings)
self._extract_topics(documents, mappings=mappings, verbose=self.verbose)
self._update_topic_size(documents)
self._save_representative_docs(documents)
self.probabilities_ = self._map_probabilities(self.probabilities_)
Expand Down Expand Up @@ -3986,11 +3986,6 @@ def _extract_topics(
Returns:
c_tf_idf: The resulting matrix giving a value (importance score) for each word per topic
"""
if verbose:
action = "Fine-tuning" if fine_tune_representation else "Extracting"
method = "representation models" if fine_tune_representation else "c-TF-IDF for topic reduction"
logger.info(f"Representation - {action} topics using {method}.")

documents_per_topic = documents.groupby(["Topic"], as_index=False).agg({"Document": " ".join})
self.c_tf_idf_, words = self._c_tf_idf(documents_per_topic)
self.topic_representations_ = self._extract_words_per_topic(
Expand Down Expand Up @@ -4204,8 +4199,14 @@ def _c_tf_idf(
tf_idf: The resulting matrix giving a value (importance score) for each word per topic
words: The names of the words to which values were given
"""
documents = self._preprocess_text(documents_per_topic.Document.values)
if self.verbose:
action = "Calculating" if self.ctfidf_model is None else "Updating"
logger.info(f"Bag of Words - {action} Bag of Words for each topic")

# Tokenize the documents
documents = self._preprocess_text(documents_per_topic.Document.values)
if self.verbose:
logger.info("Bag of Words - Tokenization using vectorizer")
if partial_fit:
X = self.vectorizer_model.partial_fit(documents).update_bow(documents)
elif fit:
Expand All @@ -4220,21 +4221,27 @@ def _c_tf_idf(
else:
words = self.vectorizer_model.get_feature_names()

# Setting the multiplier for the c-TF-IDF
multiplier = None
if self.ctfidf_model.seed_words and self.seed_topic_list:
logger.info("Bag of Words - Setting multiplier for c-TF-IDF using seed words and seed topic list")
seed_topic_list = [seed for seeds in self.seed_topic_list for seed in seeds]
multiplier = np.array(
[self.ctfidf_model.seed_multiplier if word in self.ctfidf_model.seed_words else 1 for word in words]
)
multiplier = np.array([1.2 if word in seed_topic_list else value for value, word in zip(multiplier, words)])
elif self.ctfidf_model.seed_words:
logger.info("Bag of Words - Setting multiplier for c-TF-IDF using seed words")
multiplier = np.array(
[self.ctfidf_model.seed_multiplier if word in self.ctfidf_model.seed_words else 1 for word in words]
)
elif self.seed_topic_list:
logger.info("Bag of Words - Setting multiplier for c-TF-IDF using seed topic list")
seed_topic_list = [seed for seeds in self.seed_topic_list for seed in seeds]
multiplier = np.array([1.2 if word in seed_topic_list else 1 for word in words])

# Calculate the c-TF-IDF matrix
logger.info("Bag of Words - Calculating c-TF-IDF matrix")
if fit:
self.ctfidf_model = self.ctfidf_model.fit(X, multiplier=multiplier)

Expand Down Expand Up @@ -4304,14 +4311,18 @@ def _extract_words_per_topic(
topics = base_topics.copy()
if not self.representation_model or not fine_tune_representation:
# Default representation: c_tf_idf + top_n_words
logger.info("Representation - Extracting top words from c_tf_idf")
topics = {label: values[: self.top_n_words] for label, values in topics.items()}
elif fine_tune_representation and isinstance(self.representation_model, list):
logger.info("Representation - Fine-tuning topics using representation model")
for tuner in self.representation_model:
topics = tuner.extract_topics(self, documents, c_tf_idf, topics)
elif fine_tune_representation and isinstance(self.representation_model, BaseRepresentation):
logger.info("Representation - Fine-tuning topics using representation model")
topics = self.representation_model.extract_topics(self, documents, c_tf_idf, topics)
elif fine_tune_representation and isinstance(self.representation_model, dict):
if self.representation_model.get("Main"):
logger.info("Representation - Fine-tuning topics using main representation model")
main_model = self.representation_model["Main"]
if isinstance(main_model, BaseRepresentation):
topics = main_model.extract_topics(self, documents, c_tf_idf, topics)
Expand All @@ -4322,12 +4333,16 @@ def _extract_words_per_topic(
raise TypeError(f"unsupported type {type(main_model).__name__} for representation_model['Main']")
else:
# Default representation: c_tf_idf + top_n_words
logger.info("Representation - No representation_model['Main'] found")
logger.info("Representation - Extracting top words from c_tf_idf")
topics = {label: values[: self.top_n_words] for label, values in topics.items()}
else:
raise TypeError(f"unsupported type {type(self.representation_model).__name__} for representation_model")

# Extract additional topic aspects
if calculate_aspects and isinstance(self.representation_model, dict):
if any(key != "main" for key in self.representation_model.keys()):
logger.info("Representation - Extracting additional topic aspects")
for aspect, aspect_model in self.representation_model.items():
if aspect != "Main":
aspects = base_topics.copy()
Expand Down Expand Up @@ -4375,9 +4390,6 @@ def _reduce_topics(self, documents: pd.DataFrame, use_ctfidf: bool = False) -> p
else:
raise ValueError("nr_topics needs to be an int or 'auto'! ")

logger.info(
f"Topic reduction - Reduced number of topics from {initial_nr_topics} to {len(self.get_topic_freq())}"
)
return documents

def _reduce_to_n_topics(self, documents: pd.DataFrame, use_ctfidf: bool = False) -> pd.DataFrame:
Expand Down Expand Up @@ -4412,6 +4424,9 @@ def _reduce_to_n_topics(self, documents: pd.DataFrame, use_ctfidf: bool = False)
cluster.fit(distance_matrix)
new_topics = [cluster.labels_[topic] if topic != -1 else -1 for topic in topics]

initial_nr_topics = len(self.get_topics())
logger.info(f"Topic reduction - Reduced number of topics from {initial_nr_topics} to {len(set(new_topics))}")

# Track mappings and sizes of topics for merging topic embeddings
mapped_topics = {from_topic: to_topic for from_topic, to_topic in zip(topics, new_topics)}
basic_mappings = defaultdict(list)
Expand Down Expand Up @@ -4452,7 +4467,7 @@ def _auto_reduce_topics(self, documents: pd.DataFrame, use_ctfidf: bool = False)
unique_topics = sorted(list(documents.Topic.unique()))[self._outliers :]
max_topic = unique_topics[-1]

# Find similar topics
# Find similar topics using HDBSCAN
embeddings = select_topic_representation(
self.c_tf_idf_, self.topic_embeddings_, use_ctfidf, output_ndarray=True
)[0]
Expand All @@ -4464,6 +4479,11 @@ def _auto_reduce_topics(self, documents: pd.DataFrame, use_ctfidf: bool = False)
prediction_data=True,
).fit_predict(norm_data[self._outliers :])

initial_nr_topics = len(self.get_topics())
logger.info(
f"Topic reduction - Auto-reduced number of topics from {initial_nr_topics} to {len(set(predictions))}"
)

# Map similar topics
mapped_topics = {
unique_topics[index]: prediction + max_topic
Expand Down

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