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Classification using fastText

This respository uses biodiversity data from the BioTIME database to classifiy methods texts using fastText.

Requirements

  • a local copy of BioTIME and the metadata.
  • conda (Miniconda or Anaconda)
  • a Python fastText binding (more information in the installation section)

Install

This sections guides you to set up this project to run experiments using Snakemake and fastText.

1. Clone the respository

$ git clone https://github.com/komax/BioTIME-fastText-classification

2. Anaconda environment

  1. Create a new environment, e.g., biotime-fasttext and install all dependencies.
$ conda env create --name biotime-fasttext --file environment.yaml
  1. Activate the conda environment. Either use the anaconda navigator or use this command in your terminal:
$ conda activate biotime-fasttext

or

$ source activate biotime-fasttext

3. Python bindings for fastText

Disclaimer: you can use pip install fasttext in your anaconda environment, but those bindings are outdated.

I recommend doing this: 0. First activate your anaconda environment.

  1. Checkout the github respository from fastText or a stable fork:
$ git clone https://github.com/komax/fastText
  1. Install the python bindings in the fastText respository
pip install .

4. Link or copy your BioTIME data to the respository

Create a symlink or copy your BioTIME data into biotime directory.

5. Ensure to download punkt from nltk

nltk requires to download content to tokenize a sentence. Run this in your python shell:

>>> import nltk
>>> nltk.download('punkt')

or run

$ python scripts/download-nltk-punkt.py

Run experiments with Snakemake

All configuration parameters are stored in Snakefile. Change the parameters to your purpose. Adjust -j <num_cores> in your snakemake calls to make use of multiple cores to run at the same time.

1. Data preparation

$ snakemake normalize_fasttext

2. Cross validation

Create data for cross validation, split the model parameters up in blocks and sort the model parameters by f1 scores on the training data.

$ snakemake sort_f1_scores

3. Train a model

Select the best model (from the cross validation) and train it

$ snakemake train_model

4. Testing a model

$ snakemake test_model

5. Run the entire pipeline

$ snakemake

Visualize the workflow

Snakemake can visualize the workflow using dot. Run the following to generate a png for the workflow.

$ snakemake --dag all | dot -Tpng > dag.png

Customize the experiments

Checkout the Snakefile and adjust this section to configure the experimental setup (parameter selection, cross validation, parallelization):

KFOLD = 2
TEST_SIZE = 0.25
CHUNKS = 4
PARAMETER_SPACE = ModelParams(
    dim=ParamRange(start=10, stop=100, num=2),
    lr=ParamRange(start=0.1, stop=1.0, num=2),
    wordNgrams=ParamRange(start=2, stop=5, num=2),
    epoch=ParamRange(start=5, stop=50, num=2),
    bucket=ParamRange(start=2_000_000, stop=10_000_000, num=2)
)
FIRST_N_SENTENCES = 1

Inspect the experimental results

The (sub)directory data contains intermediate data from data transforms/selection, chunking of the parameter space data/blocks and subsampling for cross validation data/cv.

results entails the parameterization for the experiments as well as the accurancy scores measured as f1 scores on precision and recall:

  • results/blocks contains all chunks (inlcuding the validation scores) as csvs,
  • results/params_scores.csv is the concatenation of all blocks,
  • results/params_scores_sorted.csv ranks the resulting scores by the f1_cross_validation_micro score on the cross validation sets per label. Then, we select the model with the smallest f1_cross_validation_micro_ptp with smallest point to point distance (minimum value to maximium value)