hestia-ood 0.0.24

Suite of tools for analysing the independence between training and evaluation biosequence datasets and to generate new generalisation-evaluating hold-out partitions

Hestia

Computational tool for generating generalisation-evaluating evaluation sets.

• Documentation: https://ibm.github.io/Hestia-OOD
• Source Code: https://github.com/IBM/Hestia-OOD
• Webserver: http://peptide.ucd.ie/Hestia

Contents

Table of Contents

• Intallation Guide
• Documentation
• Examples
• License

Installation

Installing in a conda environment is recommended. For creating the environment, please run:

conda create -n autopeptideml python
conda activate autopeptideml

1. Python Package

1.1.From PyPI

pip install hestia-ood

1.2. Directly from source

pip install git+https://github.com/IBM/Hestia-OOD

3. Optional dependencies

3.1. Molecular similarity

RDKit is a dependency necessary for calculating molecular similarities:

pip install rdkit

3.2. Sequence alignment

For using MMSeqs as alignment algorithm is necessary install it in the environment:

conda install -c bioconda mmseqs2

For using Needleman-Wunch:

conda install -c bioconda emboss

If installation not in conda environment, please check installation instructions for your particular device:

• Linux:

  wget https://mmseqs.com/latest/mmseqs-linux-avx2.tar.gz
  tar xvfz mmseqs-linux-avx2.tar.gz
  export PATH=$(pwd)/mmseqs/bin/:$PATH
  

  sudo apt install emboss
  

  sudo apt install emboss
  

• Windows: Download binaries from EMBOSS and MMSeqs2-latest

• Mac:

  sudo port install emboss
  brew install mmseqs2
  

Documentation

1. DatasetGenerator

The HestiaDatasetGenerator allows for the easy generation of training/validation/evaluation partitions with different similarity thresholds. Enabling the estimation of model generalisation capabilities. It also allows for the calculation of the ABOID (Area between the similarity-performance curve (Out-of-distribution) and the In-distribution performance).

from hestia.dataset_generator import HestiaDatasetGenerator, SimilarityArguments

# Initialise the generator for a DataFrame
generator = HestiaDatasetGenerator(df)

# Define the similarity arguments (for more info see the documentation page https://ibm.github.io/Hestia-OOD/datasetgenerator)
args = SimilarityArguments(
    data_type='protein', field_name='sequence',
    similarity_metric='mmseqs2+prefilter', verbose=3
)

# Calculate the similarity
generator.calculate_similarity(args)

# Calculate partitions
generator.calculate_partitions(min_threshold=0.3,
                               threshold_step=0.05,
                               test_size=0.2, valid_size=0.1)

# Save partitions
generator.save_precalculated('precalculated_partitions.gz')

# Load pre-calculated partitions
generator.from_precalculated('precalculated_partitions.gz')

# Training code

for threshold, partition in generator.get_partitions():
    train = df.iloc[partition['train']]
    valid = df.iloc[partition['valid']]
    test = df.iloc[partition['test']]

# ...

# Calculate AU-GOOD
generator.calculate_augood(results, 'test_mcc')

# Plot GOOD
generator.plot_good(results, 'test_mcc')

# Compare two models
results = {'model A': [values_A], 'model B': [values_B]}
generator.compare_models(results, statistical_test='wilcoxon')

2. Similarity calculation

Calculating pairwise similarity between the entities within a DataFrame df_query or between two DataFrames df_query and df_target can be achieved through the calculate_similarity function:

from hestia.similarity import calculate_similarity
import pandas as pd

df_query = pd.read_csv('example.csv')

# The CSV file needs to have a column describing the entities, i.e., their sequence, their SMILES, or a path to their PDB structure.
# This column corresponds to `field_name` in the function.

sim_df = calculate_similarity(df_query, species='protein', similarity_metric='mmseqs+prefilter',
                              field_name='sequence')

More details about similarity calculation can be found in the Similarity calculation documentation.

3. Clustering

Clustering the entities within a DataFrame df can be achieved through the generate_clusters function:

from hestia.similarity import calculate_similarity
from hestia.clustering import generate_clusters
import pandas as pd

df = pd.read_csv('example.csv')
sim_df = calculate_similarity(df, species='protein', similarity_metric='mmseqs+prefilter',
                              field_name='sequence')
clusters_df = generate_clusters(df, field_name='sequence', sim_df=sim_df,
                                cluster_algorithm='CDHIT')

There are three clustering algorithms currently supported: CDHIT, greedy_cover_set, or connected_components. More details about clustering can be found in the Clustering documentation.

4. Partitioning

Partitioning the entities within a DataFrame df into a training and an evaluation subsets can be achieved through 4 different functions: ccpart, graph_part, reduction_partition, and random_partition. An example of how cc_part would be used is:

from hestia.partition import ccpart
import pandas as pd

df = pd.read_csv('example.csv')
train, test = cc_part(df, species='protein', similarity_metric='mmseqs+prefilter',
                      field_name='sequence', threshold=0.3, test_size=0.2)

train_df = df.iloc[train, :]
test_df = df.iloc[test, :]

License

Hestia is an open-source software licensed under the MIT Clause License. Check the details in the LICENSE file.