Introduction

When a Gensim coherence score comes back as NaN, the problem is usually not that LDA is mathematically broken. It is usually a data-pipeline issue: empty documents, mismatched texts and dictionary objects, topics with unusable top words, or a coherence metric that has too little information to compute stable co-occurrence statistics.

What Coherence Actually Uses

A common misunderstanding is that coherence is computed from the fitted LdaModel alone. In practice, Gensim's CoherenceModel also depends on evaluation inputs such as:

• topic words
• tokenized texts
• a dictionary aligned with those texts
• sometimes the corpus, depending on the metric

That means the LDA model can train successfully while the coherence step still fails to produce a valid number.

For c_v, the common inputs are tokenized texts plus a matching dictionary. If those objects do not correspond to the same cleaned corpus used during training, coherence can collapse into NaN.

A Safe End-to-End Example

The safest pattern is to build the dictionary, corpus, model, and coherence inputs from the same tokenized documents.

1from gensim import corpora
2from gensim.models import CoherenceModel, LdaModel
3
4texts = [
5    ["machine", "learning", "model"],
6    ["topic", "model", "gensim"],
7    ["python", "library", "topic"],
8    ["deep", "learning", "python"],
9]
10
11dictionary = corpora.Dictionary(texts)
12corpus = [dictionary.doc2bow(text) for text in texts]
13
14lda = LdaModel(
15    corpus=corpus,
16    id2word=dictionary,
17    num_topics=2,
18    random_state=0,
19    passes=10,
20)
21
22coherence_model = CoherenceModel(
23    model=lda,
24    texts=texts,
25    dictionary=dictionary,
26    coherence="c_v",
27)
28
29print(coherence_model.get_coherence())

This works because every object comes from the same token stream. That alignment is more important than many people realize.

The Most Common Causes of NaN

The first major cause is empty or nearly empty documents. If preprocessing removes too many tokens, coherence metrics may not have enough co-occurrence structure to evaluate the topics.

The second cause is inconsistent inputs. Examples include:

• training the model on one dictionary but passing a different dictionary to CoherenceModel
• computing coherence with texts that were filtered differently from the training texts
• passing empty topic-word lists after aggressive filtering

The third cause is data sparsity. On tiny datasets, certain metrics can become unstable because the topic words almost never co-occur in the evaluation texts.

Validate the Pipeline Before Scoring

Before assuming there is a Gensim bug, inspect the inputs explicitly.

1print("documents:", len(texts))
2print("dictionary size:", len(dictionary))
3print("empty docs:", sum(1 for text in texts if not text))
4print("first topic:", lda.show_topic(0, topn=5))

If you see many empty documents or a dictionary with only a handful of tokens, the coherence result is probably reflecting bad inputs rather than a scoring failure.

It also helps to inspect all topic word lists:

for topic_id in range(lda.num_topics):
    print(topic_id, lda.show_topic(topic_id, topn=5))

If the topics are full of artifacts, repeated junk tokens, or meaningless fragments, a NaN score may simply be the most obvious symptom.

Metric Choice Matters

Gensim supports multiple coherence metrics such as c_v, u_mass, c_uci, and c_npmi. They do not all use the same evidence.

In practice:

• 'c_v is often a good default for interpreted topic quality'
• 'u_mass can be more corpus-dependent and less intuitive'
• sparse datasets can make some metrics less stable than others

If one metric produces NaN, it is worth verifying both the metric's input expectations and whether the corpus is large and rich enough for that metric to be meaningful.

Preprocessing Can Quietly Break Coherence

Aggressive preprocessing is a frequent culprit. For example, if you remove rare terms, common terms, short tokens, numbers, and punctuation too aggressively, many documents may become tiny or empty.

A safer workflow is:

1. preprocess the texts once
2. inspect document lengths after preprocessing
3. build dictionary and corpus from those exact texts
4. train the model
5. compute coherence on the same text representation

Changing tokenization or filtering between step 4 and step 5 is one of the easiest ways to create NaN results.

Common Pitfalls

The most common mistake is passing texts, dictionary, and corpus objects that were not built from the same cleaned token stream.

Another issue is over-filtering. If preprocessing leaves many documents empty or nearly empty, coherence loses the co-occurrence evidence it needs.

People also sometimes treat every coherence metric as interchangeable. Different metrics rely on different information, so a working setup for one metric is not automatically valid for another.

Finally, poor topics and broken coherence inputs are related but not identical. Inspect both the topic words and the underlying tokenized texts before deciding where the problem really sits.

Summary

• A NaN coherence score usually points to bad evaluation inputs or severe sparsity, not a mysterious LDA failure.
• Build the dictionary, corpus, and coherence texts from the same tokenized documents.
• Check for empty documents, tiny vocabularies, and unusable topic-word lists.
• Confirm that the chosen coherence metric matches the available evaluation data.
• Validate the preprocessing pipeline before spending time retuning the topic model itself.