To begin with, searching for an optimal number of topics in a text collection seems a very poorly stated task, because this number of topics heavily depends on the task at hand. One can take 10 topics and it might be enough, or 100 topics, or 1000. What's more, the whole notion of a topic is a bit obscure: people think of topics just as of some meaningful stories, concepts or ideas. And there is a parent-child relationship between such topics, eg. topic "Coconut juice" is a child of topic "Beverages". This means that for one dataset one can train a good topic model with, let's say 10 big parent topics, or another good topic model with, for example 100 more concrete, smaller topics.
So, what is this repository about then? It gives an opportunity to try different method to find an appropriate, approximate number of topics, the number which in order of magnitude is close to the number of not-so-small topics.
The first method is just about optimizing something for the number of topics. That is, train several models with different number of topics, calculate some quality function for those models, and find the one which is the best.
Scores, available for optimizing:
- Perplexity (which is minimized). Definitely, this is not the best choice (TODO: links, why).
- Rényi entropy. This one was shown to be a good indicator of some kind of model stability: the more stable the model, the less its entropy.
Let's say, one have her text collection as a vowpal wabbit file vw.txt:
doc_1 |@publisher mann_ivanov_ferber |@title atlas obscura |@text earth:8 travel:10 baobab:1 ...
doc_2 |@publisher chook_and_geek |@title black hammer |@text hero:10 whiskey:2 barbalien:4 ...
doc_3 |@publisher eksmo |@title dune |@text sand:7 arrakis:6 spice:12 destiny:2 ...
...
Then it is possible to find an optimal number of topics for this collection by looking at some topic model's characteristics (scores) and choosing the number of topics which corresponds to the best model.
The searching process can be started like this:
python run_search.py \
vw.txt \ # path to vowpal wabbit file
@text:1 \ # main modality and its weight
result.json \ # output file path (the file may not exist)
-m @publisher:5 \ # other modality and its weight
--modality @title:2 \ # other modality and its weight
optimize_scores \ # search method
--min-num-topics 1 \ # minimum number of topics in the text collection
--max-num-topics 10 \ # maximum number of topics in the text collection
--num-topics-interval 2 \ # search step in number of topics
--num-fit-iterations 100 \ # number of fit iterations for each model training
--num-restarts 10 \ # number of training restarts that differ in seed
perplexity \ # what score to optimize
renyi_entropy \ # another score to optimize
--threshold-factor 2.0 \ # previous score parameter
intratext_coherence \ # one more score
top_tokens_coherence \ # and yet another oneAnd the result.json file will look like this: (TODO: try on real data to get meaningful values)
{
"score_results":
{
"perplexity_score":
{
"optimum": 9.0,
"optimum_std": 0.0,
"num_topics_values": [1.0, 3.0, 5.0, 7.0, 9.0],
"score_values": [1374.69, 685.37, 494.05, 377.24, 313.09],
"num_topics_values_std": [0.0, 0.0, 0.0, 0.0, 0.0],
"score_values_std": [0.0, 0.0, 0.0, 0.0, 0.0]
},
"renyi_entropy_score":
{
"optimum": 3.0,
"optimum_std": 0.0,
"num_topics_values": [1.0, 3.0, 5.0, 7.0, 9.0],
"score_values": [1983797813.52, 1.37, 1.63, 1.84, 2.00],
"num_topics_values_std": [0.0, 0.0, 0.0, 0.0, 0.0],
"score_values_std": [9.87e-07, 2.30e-16, 2.30e-16, 4.60e-16, 6.90e-16]
},
"intratext_coherence_score":
{
"optimum": 1.0,
"optimum_std": 0.0,
"num_topics_values": [1.0, 3.0, 5.0, 7.0, 9.0],
"score_values": [72.90, 21.92, 12.73, 9.21, 6.88],
"num_topics_values_std": [0.0, 0.0, 0.0, 0.0, 0.0],
"score_values_std": [1.47e-14, 0.0, 3.68e-15, 1.84e-15, 2.76e-15]
},
"top_tokens_coherence_score":
{
"optimum": 1.0,
"optimum_std": 0.0,
"num_topics_values": [1.0, 3.0, 5.0, 7.0, 9.0],
"score_values": [0.834, 0.42, 0.76, 0.79, 0.53],
"num_topics_values_std": [0.0, 0.0, 0.0, 0.0, 0.0],
"score_values_std": [3.45e-16, 1.15e-16, 1.15e-16, 2.30e-16, 1.15e-16]
}
}
}Here optimum means the optimal number of topics according to the score, score_values are the values of the score, each value corresponds to the number of topics in num_topics_values by the same index.
Another way to run the process may be via bash script
#!/bin/bash
general_args=(
./sample/vw.txt
@text:1
result.json
-m @title:3
--modality @publisher:2
)
search_method_args=(
optimize_scores
--max-num-topics 10
--min-num-topics 1
--num-topics-interval 2
--num-fit-iterations 2
--num-restarts 3
perplexity
renyi_entropy
intratext_coherence
top_tokens_coherence
--cooc-file ./sample/cooc_values.json
)
python run_search.py "${general_args[@]}" "${search_method_args[@]}"Or sitting in a .py file or a Jupyter Notebook:
from topnum.data import VowpalWabbitTextCollection
from topnum.scores import (
DiversityScore,
EntropyScore,
IntratextCoherenceScore,
PerplexityScore,
SophisticatedTopTokensCoherenceScore,
)
from topnum.search_methods import OptimizeScoresMethod
modalities={
'@text': 1,
'@title': 3,
'@publisher': 2,
}
text_collection = VowpalWabbitTextCollection(
'sample/vw.txt',
main_modality='@text',
modalities=modalities,
)
modality_names = list(modalities.keys())
scores = [
PerplexityScore(
'perplexity_score',
class_ids=modality_names,
),
EntropyScore(
'renyi_entropy_score',
class_ids=modality_names,
),
DiversityScore(
'diversity_score',
class_ids=modality_names,
),
IntratextCoherenceScore(
'intratext_coherence_score',
data=text_collection,
),
SophisticatedTopTokensCoherenceScore(
'top_tokens_coherence_score',
data=text_collection,
)
]
optimizer = OptimizeScoresMethod(
scores=scores,
min_num_topics=1,
max_num_topics=10,
num_topics_interval=2,
num_fit_iterations=2,
num_restarts=3,
)
optimizer.search_for_optimum(text_collection)
with open('result.json', 'w') as f:
f.write(json.dumps(optimizer._result))More about available scores one can find here in the module.
The idea is to search for new interpretable topics as long as possible, training many topic models. As the searching for an appropriate number of topics in a document collection is a task at hand, when all the interpretable topics are collected in the bank, their number may serve as this appropriate number of topics.
For some more details one may look here.
The approach is described in the following paper:
Sergei Koltcov, Vera Ignatenko, and Sergei Pashakhin. "Fast tuning of topic models: an application of Rényi entropy and renormalization theory.", 2019.
Briefly, one model with a big number of topics is trained. Then, the number of topics is gradually reduced to one single topic: on each iteration two topics are selected by some criterion and merged into one. Minimum value of entropy is supposed to show the best, optimal, number of topics, when the model is most stable.
The method can be invoked like this:
python run_search.py \
vw.txt \ # path to vowpal wabbit file
@text:1 \ # main modality and its weight
result.json \ # output file path (the file may not exist)
-m @publisher:5 \ # other modality and its weight
--modality @title:2 \ # other modality and its weight
renormalize \ # search method
--max-num-topics 100 \ # maximum number of topics in the text collection
--num-fit-iterations 100 \ # number of fit iterations for each model training
--num-restarts 10 \ # number of training restarts that differ in seed
--matrix phi # matrix to use for renormalizationBy assumption, optimal number of topics is supposed provide some stability in model training, when models trained on different subsets of documents from the same corpus are alike.
The idea is similar to the one described in the following paper:
Derek Greene, Derek O’Callaghan, and Pádraig Cunningham. "How many topics? stability analysis for topic models", 2014.
However, here we are not using such notion as reference ranking set.
We just train several topic models on different parts of the corpus and compare them all in pairs.
Also one may take a look at this demo notebook about the stability approach used in the library.
.
├── run_search.py # Main script which handles all the methods and their parameters and provides a way to run the process through the command line
├── demos # Demo notebooks with experiments on real data
├── sample # Toy data sample and scripts to try
└── topnum # Core library functionality
├── data # Train data handling (eg. Vowpal Wabbit files)
├── scores # Scores that are available for optimizing or tracking
└── search_methods # Some techniques and ideas that can be used for finding an appropriate number of topics
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent