hpicgs / Topic-Models-and-Dimensionality-Reduction-Sensitivity-Study

Topic-Models-and-Dimensionality-Reduction-Sensitivity-Study

Benchmark Specs

This is the repository for the study "A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations".

It contains all execution and analysis modules.

Datasets

• 20_newsgroups
• lyrics
• seven_categories
• reuters
• BBC news
• emails

Topic Models

• VSM of gensim
• VSM of gensim + tf-idf-Weighting
• LSI of gensim
• LSI of gensim + Linear Combined
• LSI of gensim + tf-idf-Weighting
• LSI of gensim + tf-idf-Weighting + Linear Combined
• NMF of gensim
• NMF of gensim + Linear Combined
• NMF of gensim + tf-idf-Weighting
• NMF of gensim + tf-idf-Weighting + Linear Combined
• LDA of gensim
• LDA of gensim + Linear Combined
• Doc2Vec of gensim
• BERT_all_mpnet_base_v2 the best sentence embedding model according to https://www.sbert.net/docs/pretrained_models.html
• BERT_all_distilroberta_v1 the second best sentence embedding model according to https://www.sbert.net/docs/pretrained_models.html

Dimension Reduction Techniques

• MDS with one investigated hyperparameter n_iter
• SOM with two investigated hyperparameters n and m
• t-SNE with three investigated hyperparameters learning_rate, n_iter, and perplexity
• UMAP with two investigated hyperparameters min_dist and n_neighbors

Quality Metrics

Local Stability Metrics

• Trustworthiness
• Continuity
• Mean Relative Rank Errors
• Local Continuity Meta-Criterion
• Label Preservation

Global Stability Metrics

• Pearson's Correlation
• Soearnab's Rank Correlation
• Cluster Ordering

Class Seperation Metric

• Absolute Difference Distance Consistency

Further scores:

• Silhouette Coefficient
• Rotation from Procrustes Analysis

Analysis

• Heatmaps
• Binary Tests
• Correlation Tests

Dev Setup (Ubuntu)

We have written our code for a Ubuntu 22.04 system.

Dependencies

• openjdk-19-jdk
• ant
• python3-minimal
• python3.10-full
• python3-pip
• git
• RScript from r-base-core

Please install this via

> sudo apt install openjdk-19-jdk ant python3-minimal python3.10-full python3-pip git r-base-core

Setup

> pip3 install numpy==1.23.5
> pip3 install -r requirements.txt
> python3 -m spacy download en_core_web_sm
> python3 -m spacy download en_core_web_lg

For postprocessing we also need ggplot2. Please install it via executing:

> R
> > install.packages("ggplot2")
``
and answering yes at every prompt.

## Run

### Parameter Generator

```bash
> python3 parameter_generator.py > parameters.csv

ML Processing

Repeated calls to main.py using a wide range of parameters (see parameter generator) like this call:

> python3 main.py --perplexity_tsne 30 --n_iter_tsne 1000 --learning_rate auto --n_neighbors_umap 15 --min_dist_umap 0.1 --max_iter_mds 300 --dataset_name 20_newsgroups --topic_model lsi_tfidf --res_file_name ./results/20_newsgroups/results_perplexity_tsne_30_n_iter_tsne_1000_learning_rate_auto_n_neighbors_umap_15_min_dist_umap_0.1_max_iter_mds_300_dataset_name_20_newsgroups_topic_model_lsi_tfidf.csv

For replication, we recommend you to (first) test a command like above. For running the full benchmark you will most probably need a computer cluster and about two weeks. Further calls can be produced by parameter_generator.py. See above.

Analysis

After finishing your runs, it is recommended to run parameter_generator.py again to see which job did finish and which not. The results_files then are copied to a directory called res_files_only, where the results can be collected. Thereafter, the results can be analyzed with one of the four analysis script: experiment_1_correlation_analysis.py, experiment_2_stability_hyperparameters, experiment_3_stability_randomness, experiment_4_stability_jitter.py. The script experiment_5_high_dimensional_similarity calculates the stability metrics for the standard corpus compared to a jittered corpus.

So the standard workflow for [path_to_results_of_dataset] with, e.g., 10 parallel jobs, is:

> python3 experiment_2_stability_hyperparameters.py [path_to_results_of_dataset]/random_seed_0/jitter_amount_0.0 --n_jobs 10
> python3 experiment_3_stability_randomness.py [path_to_results_of_dataset] --n_jobs 10
> python3 experiment_4_stability_jitter.py [path_to_results_of_dataset] --n_jobs 10
> python3 experiment_4_stability_jitter.py [path_to_results_of_dataset] --n_jobs 10 --random_seed 42
> python3 experiment_5_high_dimensional_similarity.py

This will create result csv files in your current working directory. Please note that you should avoid [path_to_results_of_dataset] a final path seperator at the end of your path so that os.path.basename works properly. In addition, those analysis may take a while (depending on the dataset and your machine a couple of second up to 5 minutes per comparison).

In addition, to get a small report of covered analysis results, perform some sanity checks and get a large file of all results per experiment you may want to run:

> python3 perform_sanity_checks [path_to_experiment_results_from_calls_mentioned_above]

Postprocessing

To execute this step you need ggplot2. Please refer to "Setup" for instructions how to install this package. For aesthetics we used ggplot2 for postprocessing our scatter plots in the paper. This is done via the get_r_pdf_plots.py script. If you executed the call under "ML Processing", the according postprocessing call would be:

> python3 get_r_pdf_plots.py --base_path results/20_newsgroups --dataset_name 20_newsgroups

Afterwards, you can find the new scatter plots in the "Analysis_Visualization" directory.

Docker Setup

Build

> docker build . -t python-ml_batch:latest projections_benchmark --build-arg PLATFORM=amd64

Run

> docker run python-ml_batch python3 main.py --perplexity_tsne 40 --n_iter_tsne 6000 --dataset_name reuters --res_file_name ./results/reuters/results_perplexity_tsne_40_n_iter_tsne_6000_dataset_name_reuters.csv

Additionally, mounts and workdir need to be set accordingly.

Batch Run

> ./batch.sh

Results

All of our results obtained by the method described above can be found in result_files/results_[experiment_name].zip After unzipping, the results for each dataset may be found in results_experiment_[experiment_number]_[dataset_name].csv. In addition, a summary covering report is also placed in the respective experiment directory.

You may find all layout .npy (numpy-files) files under: https://drive.google.com/drive/folders/10pZX_hUKPYbOEynvOJ_RCM1frztO53Fw?usp=sharing