mycoai-its 0.0.4

(Fungal ITS) DNA barcode classification with neural networks.

About MycoAI

Welcome! MycoAI is a Python package that implements deep learning models for the classification of (fungal ITS) barcode sequences. Our most sophisticated model is trained on the UNITE dataset, giving it a class coverage of 14,742 species and 3,695 genera. In our paper, we show that MycoAI achieves a higher accuracy than RDP classifier (the previous state-of-the-art fast fungal classification algorithm) on independent test sets. Furthermore, MycoAI is capable of classifying large datasets of 100,000+ sequences in <5 minutes, making it a very suitable algorithm for large-scale biodiversity studies. We refer to DNABarcoder for the most precise (and most computationally demanding) classification.

In short, the package allows users to:

• Classify their own fungal ITS datasets.
• Train neural networks for the taxonomic classification of biological sequences.
• Evaluate taxonomic classifiers in a standardized environment.

Contact

Questions can be directed to Luuk Romeijn.

Installation

MycoAI can be installed via pip:

pip install mycoai

Installing from source

Alternatively, developers can download MycoAI from source:

git clone https://github.com/MycoAI/MycoAI

You can install the specified requirements manually, or create a conda environment with all the necessary dependencies using the command below.

conda env create -f environment.yml

Requirements

MycoAI requires the following packages: Python (version 3.8 or higher), Biopython , Pandas , Plotly , PyTorch , Matplotlib , Numpy , Scikit-learn , Scipy , SentencePiece , Tqdm , and Weights and Biases. The least cumbersome installation process uses the environment file (explained above).

A Graphical Processing Unit (GPU) is not required but will speed up training and classification significantly.

Getting started

We provide the following single-command scripts:

• mycoai-classify: Classify your own fungal ITS dataset.
• mycoai-evaluate Evaluate MycoAI's performance with your own labelled dataset.
• mycoai-map Map your own ITS sequences onto MycoAI's taxonomic map.
• mycoai-train Train a new neural network with your own dataset.

An example of how to use the scripts is given here.

If you find the functionalitiy of these scripts to be too limited for your needs, we highly encourage you to write your own scripts using the available modules. MycoAI was made to be modular and easily integrated into other projects, such as the example here. The package is documented below.

Weights and Biases

Your first run of MycoAI will prompt the following Weights and Biases login option:

wandb: (1) Private W&B dashboard, no account required
wandb: (2) Create a W&B account
wandb: (3) Use an existing W&B account
wandb: (4) Don't visualize my results
wandb: Enter your choice: 

Here, choosing option 1 is sufficient. Weigths and Biases is an AI development platform, which MycoAI mainly uses for live visualization of results.

Classification

mycoai-classify <fasta_filepath>

Taxonomic classification of fungal ITS sequences using a deep neural network. Output is written to a .csv file.

Argument	Required	Description	Values
fasta_filepath	Yes	Path to the FASTA file containing ITS sequences.	path
--out	No	Where to save the output to (default is 'prediction.csv')	path
--model	No	Path to saved SeqClassNetwork model (default is 'models/MycoAI-multi-HLS.pt').	path
--device	No	Forces use of specific device. By default, MycoAI will look for and use GPUs whenever available and falls back to CPU if unsuccessful.	['cpu', 'cuda', 'cuda:0', etc.]

Evaluation

mycoai-evaluate <classification> <reference>

Evaluates predicted classification of fungal ITS sequences. Results will be printed, graphically displayed on Weights and Biases, and written to a .csv file.

Argument	Required	Description	Values
classification	Yes	Path to .csv file containing predicted labels.	path
reference	Yes	Path to .csv or FASTA file containing ground truth labels.	path
--out	No	Where to save the output to (default is 'evaluate.csv')	path

Taxonomic mapping

mycoai-map <fasta_filepath>

Creates a 2D visualization of the final layer of a ITS sequence classification network, serving as a taxonomic map. Output is an .html file which can be opened in your favourite browser. The hidden representation of the final layer of the network is reduced via the t-distributed Stochastic Neighbourhood Embedding (t-SNE) algorithm.

The sequences in the provided FASTA file will be mapped together with a background dataset to serve as context. By default, a randomly selected subset of the UNITE datset is used as context. Should you wish to investigate how your 'unknown' sequences relate to a set of labelled sequences, then provide this labelled dataset in the --context argument.

Argument	Required	Description	Values
fasta_filepath	Yes	Path to the FASTA file containing ITS sequences.	path
--out	No	Where to save the output to (default is 'map.html').	path
--classification	No	Classification of input sequences in .csv (default is None).	path
--context	No	Set of labelled sequences, provided in FASTA format, to serve as context in the taxonomic map. (default is 'data/trainset_valid.fasta').	path
--model	No	Path to saved SeqClassNetwork Pytorch model (default is 'models/MycoAI-multi-HLS.pt').	path
--device	No	Forces use of specific device. By default, MycoAI will look for and use GPUs whenever available and falls back to CPU if unsuccessful.	['cpu', 'cuda', 'cuda:0', etc.]

Training

mycoai-train <fasta_filepath>

Trains a deep neural network for taxonomic classification of fungal ITS sequences. Output is a SeqClassNetwork object stored in a .pt file. Training progress can be followed live (updated every epoch) via Weights and Biases.

Even though the script contains many optional arguments for customization, the full functionality of MycoAI can only be accessed through custom-made scripts that users can write themselves with the MycoAI package.

Argument	Required	Description	Values
fasta_filepath	Yes	Path to the FASTA file containing ITS sequences for training.	path
--out	No	Path to where the trained model should be saved to (default is 'model.pt').	path
--base_arch_type	No	Type of the to-be-trained base architecture (default is BERT).	['CNN', 'BERT']
--output_type	No	Whether to use the multi-head output or not (default is 'multi').	['multi', 'infer_sum']
--no_hls	No	If specified, turns off hierarchical label smoothing (default is HLS)	-
--levels	No	Specifies the levels that should be trained (or their weights). Must be space-separated. Can be a list of strings, e.g. 'family genus'. Can also be a list of floats, indicating the weight per level, e.g. '0 0 0 1 1 0' (default is al levels, i.e. '1 1 1 1 1 1').	list of strings, floats, or integers (space-separated)
--epochs	No	Number of epochs to train for (default is 50).	int
--batch_size	No	Number of samples per batch (default is 64).	int
--valid_split	No	Fraction of data to be used for validation (default is 0.1).	float
--learning_rate	No	Learning rate in Adam optimizer (default is 0.0001).	float
--weighted_loss	No	Strength of how much per-class-loss should be weighted by the reciprocal class size, for imbalanced classes (default is 0.5).	float
--device	No	Forces use of specific device. By default, MycoAI will look for and use GPUs whenever available and falls back to CPU if unsuccessful.	['cpu', 'cuda', 'cuda:0', etc.]

The MycoAI package

MycoAI is a deep learning based (fungal ITS) taxonomic sequence classification development platform built upon the PyTorch framework. It was designed to be modular and easily integrated into other projects. This is achieved through an object-oriented design. The most important modules are:

• mycoai.data.Data: data parsing, filtering, and encoding to TensorData object.
• mycoai.data.encoders: contains several encoding methods that aid in converting a Data object to a network-readable TensorData object.
• mycoai.data.TensorData: network-readable data format (Tensors).
• mycoai.modules.BERT: transformer-based architecture.
• mycoai.modules.SeqClassNetwork: wrapper class that stores encoder objects and adds an output layer as well as other application-related functionalities to a base architecture like BERT.
• mycoai.train.SeqClassTrainer: trains a SeqClassNetwork model.
• mycoai.evaluate.Evaluator: evaluates a classification output.
• mycoai.utils: contains constants and helper functions.

An example script is available here. This script should be able to run on your machine without any modifications directly after installing MycoAI.

mycoai.data.Data

Data container that parses and filters sequence data and encodes it into the network-readable TensorData format. After initializing a Data object, the sequence/taxonomy data is stored within its data attribute.

• __init___(self, filepath, tax_parser='unite', allow_duplicates=False, name=None)
  • filepath: Path of to-be-loaded file in FASTA format (str)
  • tax_parser: Function that parses the FASTA headers and extracts the taxonomic labels on all six levels (from phylum to species). If None, no labels will be extracted. If 'unite', will follow the UNITE format. Also supports user-custom functions, as long as they return a list following the format: [id, phylum, class, order, family, genus, species] (function, default is 'unite').
  • allow_duplicates: Drops duplicate entries if False (default is False)
  • name: Name of dataset. Will be inferred from filename if None (default is None).

Data parsing

By default, the UNITE labelling format is assumed:

>KY106084|k__Fungi;p__Ascomycota;c__Saccharomycetes;o__Saccharomycetales;f__Saccharomycetaceae;g__Zygotorulaspora;s__Zygotorulaspora_florentina|SH0987707.09FU

If your dataset deviates from this format, you can write your own parsing function and pass it into the tax_parser argument. Any function is accepted, as long as it returns a list of the format [id, phylum, class, order, family, genus, species]:

from mycoai.data import Data

def custom_parser(fasta_header):
    '''Example parsing function for header with comma-separated labels'''
    return = fasta_header.split(",")

dataset = Data('dataset.fasta', tax_parser=custom_parser)

Data filtering

The Data object contains several methods for data filtering and manipulation, such as:

• train_valid_split(self, valid_split, export_fasta=False): splits up the Data object into a train and validation split, returning two Data objects. Writes FASTA file if export_fasta is True. If export_fasta is of type list[str], will write train and validation dataset to specified filepaths.

• class_filter(self, level, min_samples=0, max_samples=np.inf, max_classes=np.inf, remove_unidentified=False): This method can be used for reducing class imbalance by filtering out sequences as well as for creating manageable data subsets. Retains at most max_samples sequences at specified taxon level for which at least min_samples are available in that class. Ensures perfect class balance when min_samples==max_samples. Randomly selects a max_classes number of classes.

• sequence_quality_filter(self, tolerance=0.05): Removes sequences with more than tolerated number of uncertain bases.

• sequence_length_filter(self, tolerance=4): Removes sequences with that fall outside of the tolerated range.

  • tolerance: Tolerated range of lengths. In case of an integer, the tolerated range is defined as the sequences that fall within the specified number of standard deviations from the mean length (int|list|range, default is 4).

• export_fasta(self, filepath): Exports this Data object to a FASTA file.

Data filtering operations happen in-place even though each of the filtering methods also return the filtered object (self).

from mycoai.data import Data

# Load data
data = mycoai.Data('dataset.fasta') 
# Select a subset of 1000 species from those that have at least 5 samples
data = data.class_filter('species', min_samples=5, max_classes=1000)
# Remove sequences with more than 5% of bases not in [A,C,G,T]
data = data.sequence_quality_filter(tolerance=0.05)
# Remove sequences with more than 4 stds from the mean length
data = data.sequence_length_filter(tolerance=4)
# Export to FASTA file
data.export_fasta('filtered.fasta')

Data encoding

The data must be encoded (i.e. converted into tensors of numbers) before being inputted into a neural network. The following method converts a Data object into a TensorData object that our sequence classifiers can operate on:

• encode_dataset(self, dna_encoder, tax_encoder='categorical', export_path=None): Converts data into a TensorData object.
  • dna_encoder: Specifies the encoder used for generating the sequence tensor. Can be an existing object, or one of ['4d', 'kmer-tokens', 'kmer-onehot', 'kmer-spectral', 'bpe'], which will initialize an encoder of that type (DNAEncoder|str).
  • tax_encoder: Specifies the encoder used for generating the taxonomies tensor. Can be an existing object, or 'categorical', which will initialize an encoder of that type (TaxonEncoder, default is 'categorical').
  • export_path: Path to save encodings to (str, default is None).

More information about the encoders and their compatibility with several network types is given here.

NOTE: When using a training/validation split, it is very important to encode the validation dataset with the same encoders that were used for the training dataset (such that e.g. Ascomycota is assigned to the same index in both datasets). This can be achieved by passing the dna_encoder and tax_encoder attributes of the newly created TensorData object into the dna_encoder and tax_encoder arguments of the Data.encode_dataset method:

from mycoai.data import Data

# Load data and split into training and validation set
data = Data('dataset.fasta')
train_data, valid_data = data.train_valid_split(0.2)

# It is important to encode the validation dataset with the same encoders
train_data = train_data.encode_dataset('bpe') # Byte Pair Encoding
valid_data = valid_data.encode_dataset(dna_encoder=train_data.dna_encoder,
                                       tax_encoder=train_data.tax_encoder)

mycoai.data.encoders

Contains several encoding methods that aid in converting a Data object to a network-readable TensorData object. These encoding methods are very important: not only are they used for encoding the training/validation datasets, they must also be stored in the sequence classification network such that it can encode any future dataset by itself and decode its own predictions. Two encoding schemes are required, one for encoding the DNA sequences and one for en-/decoding taxonomic labels.

Encoding DNA sequences

MycoAI includes several DNA encoding techniques, each compatible with specific neural architecture designs. They all inherit from the same DNAEncoder base class. An overview is given below:

Name	Description	Tensor shape*	Example encoding: ACGACGT	Model
BytePairEncoder	Keeps track of the most frequently appearing combinations of characters, resulting in a fixed-sized vocabulary of flexibly-sized words. The occurrence of a word is indicated by a unique index (token).	$[n,l]$	[[1,5,6,2]] assuming tokens 'ACGA' and 'CGT'	Transformer
KmerSpectrum	Encodes each sequence into a frequency vector for its $k$-mers.	$[n,1,4^k]$	[[[0.12,...,0.07,...]]]	CNN
FourDimDNA	4-channel representation, comparable to the 3-channel RGB representation of images.	$[n,4,l]$	[[[1,0,0,0],[0,1,0,0],[0,0,1,0],[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]]]	CNN
KmerTokenizer	Encodes each possible $k$-mer with a unique index (token)	$[n,l]$	[[1,5,5,2]] for $k=3$	Transformer
KmerOneHot	One-hot encoding of $k$-mers, given each possible $k$-mer its own channel	$[n,4^k,l]$	[[[0,...,1,...,0],[0,...,1,...,0]]]	CNN

*=for $n$ sequences with (padded) length $l$

Note that in case of BytePairEncoder and KmerTokenizer, five token values have special meanings. For example, in the table above 1 indicates the CLS (start) token and 2 indicates the SEP (end) token. We also use dedicated tokens for padding, unknown values, and masking.

The BytePairEncoder is currently the only DNAEncoder that has a encode_fast method implemented. This method allows the encoder to efficiently translate an entire Data object into a tensor, making it (by far) the fastest encoding technique.

The example below shows how to initialize the most important encoding methods with compatible architectures.

from mycoai.data import Data
from mycoai.encoders import *
from mycoai.modules import BERT, SimpleCNN

data = Data('dataset.fasta')

# Byte Pair Encoding with a transformer
dna_encoder = BytePairEncoder(data) # Must initialize on Data object
arch = BERT(dna_encoder.vocab_size) # Must specify vocab_size

# K-mer encoding with transformer
dna_encoder = KmerTokenizer(k=4)
arch = BERT(dna_encoder.vocab_size) # Again, must specify vocab_size

# K-mer spectral encoding with CNN
dna_encoder = KmerSpectrum(k=4)
arch = SimpleCNN(in_channels=1) # 1 input channel

# 4D encoding with CNN
dna_encoder = FourDimDNA()
arch = SimpleCNN(in_channels=4) # 4 input channels

# (Can also do this before initializing architecture:)
data = data.encode_dataset(dna_encoder)

Encoding taxonomic labels

TaxonEncoder: Sparse categorical encoding method of taxonomic labels on 6 levels. For every level, each label is assigned to a unique index by a list of encoders stored in its lvl_encoders attribute. Furthermore, hierarchical inference_matrices are generated during training, as the model keeps track of which parent label a sequence of a specific child label is most oftenly assigned to.

• __init__(self, data): A TaxonEncoder object requires initialization with a Data object passed as argument. This will initialize the label encoders as well as the inference matrices.
• encode(self, data_row): Assigns integers to taxonomic level. Also builds inference matrices during training, for which it assumes that this method is called for all rows of the training dataset.
• encode_fast(self, data): Assigns integers to all taxonomic levels, optimized to operate on an entire Data object at once (use only outside of training).
• decode(self, labels: np.ndarray, levels: list=utils.LEVELS): Decodes an array of index labels into their corresponding strings at specified levels.
• infer_parent_probs(self, y, parent_lvl): Calculates probabilities for parents given child probabilities based on inference matrices (Equation 1 in paper).
• infer_child_probs(self, y, child_lvl): Calculates probabilities for children given parent probabilities, based on inference matrices (Equation 3 in paper).

mycoai.data.TensorData

Holds data as tensors in sequences and taxonomies attributes. The class directly inherits from torch.utils.data.Dataset, allowing it to work with PyTorch dataloaders. A TensorData object also contains the exact encoder instances that were used for generating the tensors in the dataset, stored in its dna_encoder and tax_encoder attributes.

• __init__(self, sequences=None, taxonomies=None, dna_encoder=None,tax_encoder=None, name=None, filepath=None): Initializes TensorData object from tensors (Data.encode_dataset will use this), or imports a pre-saved object from the specified filepath (after being exported by TensorData.export_data).

• export_data(self, export_path): Saves sequences, taxonomies, and encoders to file. This is useful when your dataset is very large and encoding is slow.

Other built-in TensorData methods provide insight into the class distribution and/or are related to weighted loss/sampling.

Weighted loss/sampling

The TensorData contains two methods that are related to counteracting the class imbalance problem of many taxonomically labelled datasets.

• weighted_sampler(self, level='species', strength=1.0, unknown_frac=0.0): Yields a random sampler that balances out (either fully or to some extent) the label distributions by over-/undersampling small/large classes.

  • level: Level at which the data balancing will be applied to (str).
  • strength: Amount of balancing to be applied. If 0, maintains the original data distribution. If 1, ensures perfect class balance. Numbers in between represent varying degrees of balance, where the weight of a class of size $c$ is determined by ${c^{-strength}}$ (float, default is 1.0).
  • unknown_frac: Sample unidentified classes an unknown_frac fraction of times. Will not sample the unidentified class if 0, potentially leading to a lot of wasted data (float, default is 0.0).

• weighted_loss(self, loss_function, sampler=None, strength=1.0): Returns a list of six weighted loss functions that balance out (either fully or to some extent) the label distributions by weighting small/large classes more/less.

  • loss_function: Loss function to be used as basis. The recommended loss function is mycoai.train.CrossEntropyLoss. Loss function must support the specification of a weight and ignore_index parameter (torch.nn.Module).
  • sampler: If provided, will correct for the expected data distribution (on all taxonomic levels) given the specified sampler. Thus, you can use both a weighted sampler ánd a weighted loss function with varying strengths.
  • strength: Amount of balancing to be applied. If 0, maintains the original data distribution. If 1, ensures perfect class balance. Numbers in between represent varying degrees of balance, where the weight of a class of size $c$ is determined by ${c^{-strength}}$ (float, default is 1.0).

Example usage of the TensorData object is provided below.

import torch
from mycoai.data import Data, TensorData
from mycoai.train import CrossEntropyLoss, SeqClassTrainer

# One way of creating a TensorData object (encoding a Data object)
data = Data('dataset.fasta').encode_dataset('bpe')

# Exporting the newly created object
data.export_data('dataset.pt')

# Alternative way of creating TensorData object (loading stored object)
data = TensorData(filepath='dataset.pt')

# Applying weighted sampling and weighted loss
sampler = data.weighted_sampler(strength=0.5) # Not fully balanced
# Remaining class imbalance is counteracted using weighted loss
loss = data.weighted_loss(CrossEntropyLoss, strength=1, sampler=sampler)

# Weighted loss/sampler can later be inputted to training procedure
model = torch.load('model.pt')
model, history = SeqClassTrainer.train(model, data, sampler=sampler, loss=loss)

mycoai.modules.BERT

BERT base model, transformer encoder to be used for various tasks. Inherits from torch.nn.Module, just like all other pre-implemented architectures that are part of MycoAI.

• __init__(self, vocab_size, d_model=256, d_ff=512, h=8, N=6, dropout=0.1, mode='default'): initializes the transformer given the specified hyperparameters and vocabulary size.
  • vocab_size: Number of unique tokens in vocabulary. Can be the vocab_size attribute of BytePairEncoder or KmerTokenizer (int).
  • d_model: Dimension of token representations (embeddings) in model (int, default is 256)
  • d_ff: Dimension of hidden layer feed-forward sublayers (int, default is 512)
  • h: Number of heads used for multi-head self-attention, must be a divisor of d_model (int, default is 8).
  • N: How many encoder/decoder layers the transformer has (int, default is 6)
  • dropout Dropout probability to use throughout network (float, default is 0.1)
  • mode: Determines the forward method that BERT will use. Users are never required to specify this, MycoAI will change the mode of BERT depending on the task (One of ['default', 'classification', 'mlm']).

Note that MycoAI supports the pre-training of a BERT module through the mycoai.train.MLMTrainer class. This will perform Masked Language Modelling on the sequences that are part of the specified dataset.

import torch
from mycoai.data import TensorData
from mycoai.modules import BERT
from mycoai.train import MLMTrainer

data = TensorData(filepath='dataset.pt') # Reading TensorData from file

# Initializing BERT module
model = BERT(vocab_size=data.dna_encoder.vocab_size)

# Pre-training BERT
model, history = MLMTrainer.train(model, data, epochs=50)
torch.save(model, 'pretrained.pt') # Saving the pre-trained model

mycoai.modules.SeqClassNetwork

Performs taxonomic classification based on DNA sequences. It is a wrapper class that stores encoder objects and adds an output layer as well as other application-related functionalities to a base architecture (such as BERT). The encoders are stored in its dna_encoder and tax_encoder attributes. The base architecture and output network are found in its base_arch and output attributes, respectively. Is stored on utils.DEVICE upon initialization.

• __init__(self, base_arch, dna_encoder, tax_encoder, fcn_layers=[], dropout=0, output='multi', max_level='species', chained_config=[False,True,True]): Initializes the module for the specified base architecture, encoders, and output head.

  • base_arch: The body for the neural network. Can be one of the pre-implemented MycoAI modules as well as any custom PyTorch module (torch.nn.Module).
  • dna_encoder: The DNA encoder used for the expected input (DNAEncoder).
  • tax_encoder: The label encoder used for the (predicted) labels (TaxonEncoder).
  • fcn_layers: List of node numbers for fully connected part before the output head (list[int], default is []).
  • dropout: Dropout percentage for the dropout layer (float, default is 0).
  • output: The type of output head(s) for the neural network. More information is found here (One of ['infer_parent', 'infer_sum', 'multi', 'chained', 'tree'], default is 'multi').
  • max_level: Until what level to predict (only for 'infer_parent', 'infer_sum', and 'multi' output heads) (str, default is 'species').
  • chained_config: List of length 3 indicating the configuration for ChainedMultiHead. Corresponding to arguments: ascending, use_probs, and all_access (list[bool], default is [False, True, True]).

• __call__(self, x): A forward pass through the neural network. Outputs a list of six tensors, one per taxonomic level (from phylum to species). Calls the forward method. Note that the forward method has several alternative implementations to support extracting the latent space as well autoregressive models (with/without teacher forcing).

• classify(input_data): Classifies sequences in FASTA file, Data, or TensorData object, returns a pandas DataFrame. If required, will parse/encode input_data before classification.

• latent_space(input_data): Extracts latent space for given input data. Equals a forward pass up until the final layer of the base architecture or the bottleneck (least no. dimension) layer of the fully-connected component of the network (e.g. in case of a CNN). Input data can be a path to a FASTA file, or a Data or TensorData object. Returns a numpy array. If required, will parse/encode input_data before classification.

Output architectures

An important component of a SeqClassNetwork object is its output architecture. Implementations for several (experimental) output architectures are given in mycoai.modules.output_heads. If you wish your own output architecture to be supported by MycoAI's models and its training procedure, make sure to output a list of 6 tensors, each corresponding to a taxonomic level (from phylum to species). The most relevant output architectures are explained below.

Name	Description
MultiHead	Predicting multiple taxon levels using different and independent output heads (softmax-activated linear layers). This output architecture was proven to yield models with better representations of the (hierarchical) taxonomic space and is the recommend output architecture.
ChainedMultiHead	Like MultiHead, but each taxon level also gets input from the previously predicted level. Whether these connections move from phylum-to-species or species-to-phylum is determined by ascending=False or True, respectively. The all_access argument defines whether the next output head is also inputted with the base architecture output. The use_prob argument determines whether probabilities are forwarded to the next level or the original, non-softmax-activated linear output.
InferSum	Sums Softmax probabilities of child classes to infer the probability of a parent, using the taxon encoder's inference matrices. This allows a single-headed output architecture while still classifying all taxonomic levels.
InferParent	Like InferSum, but infers parent classes by looking in the inference matrix and seeing what parent a child class is most often part of (instead of summing).

Note that a MultiHead output can be converted to an InferSum output head by calling the multi_to_infer_sum method of a SeqClassNetwork object.

Storing/loading a model

A SeqClassNetwork object can be stored for later use via torch.save and loaded through torch.load:

import torch
from mycoai.data import TensorData
from mycoai.modules import BERT, SeqClassNetwork

data = TensorData(filepath='dataset.pt') # Reading TensorData from file
arch = BERT(data.dna_encoder.vocab_size) # Initializing BERT module

# Creating SeqClassNetwork model and saving it
model = SeqClassNetwork(arch, data.dna_encoder, data.tax_encoder)
torch.save(model, 'model.pt')

# Loading an existent one (either on CPU/GPU via utils.DEVICE)
mycoai_trained = torch.load('models/MycoAI-multi-HLS.pt', 
                            map_location=utils.DEVICE)

# Making and saving a classification
prediction = mycoai_trained.classify(data)
prediction.to_csv('prediction.csv') 

mycoai.train.SeqClassTrainer

Trains a SeqClassNetwork object for multi-class classification on 6 taxonomic levels. Requires no initialization, i.e. all methods are static methods. Supports Hierarchical Label Smoothing (HLS), mixed precision, and curriculum learning. Will initialize a Weights and Biases run via which several metrics can be tracked in real time, which is updated every training epoch.

• train(model, train_data, valid_data=None, epochs=100, loss=None, batch_size=64, sampler=None, optimizer=None, metrics=utils.EVAL_METRICS, levels=utils.LEVELS, warmup_steps=None, label_smoothing=[0.02,0.02,0.02,0.02,0.02,0], wandb_config={}, wandb_name=None): Trains a SeqClassNetwork object to taxonomically classify sequences. Returns a tuple in which the first returned element corresponds to the trained model, and the second element corresponds to a history dataframe that contains the performance per epoch.

  • model: Neural network architecture (torch.nn.Module).
  • train_data: Preprocessed dataset containing ITS sequences for training (mycoai.data.TensorData).
  • valid_data: Preprocessed dataset containing ITS sequences for validation (mycoai.data.TensorData).
  • epochs: Number of training iterations (int, default is 50).
  • loss:To-be-optimized loss function (or list of functions per level) (list | function, default is CrossEntropyLoss).
  • batch_size: Number of training examples per optimization step (int, default is 64)
  • sampler: Strategy to use for drawing data samples (torch.utils.data.Sampler)
  • optimizer: Optimization strategy (default is Adam) (torch.optim)
  • metrics: Evaluation metrics to report during training, provided as dictionary with metric name as key and function as value (dict{str:function}, default is accuracy, balanced acuracy, precision, recall, f1, and mcc).
  • levels: Specifies the levels that should be trained (and their weights). Can be a list of strings, e.g. ['genus', 'species]. Can also be a list of floats, indicating the weight per level, e.g. [0,0,0,0,1,1]. Can also be a MycoAI weight schedule object, e.g. Constant([0,0,0,0,1,1]) (list| mycoai.train.weight_schedules, default is utils.LEVELS).
  • warmup_steps: When specified, the lr increases linearly for the first warmup_steps then decreases proportionally to 1/sqrt(step_number). Works only for models with d_model attribute (BERT/EncoderDecoder) (int|NoneType,default is 0).
  • label_smoothing: Explained here. List of six decimals that controls how much label smoothing should be added per taxonomic level. The sixth element of this list refersto the amount of weight that is divided uniformly over all classes. Hence, [0,0,0,0,0,0.1] corresponds to standard label smoothing with $\epsilon=0.1$, whereas [0.02,02,0.02,0.02,0.02,0] corresponds to hierarchical label smoothing with $\epsilon=0.1$ (list[float], default is [0.02,0.02,0.02,0.02,0.02,0]).
  • wandb_config: Extra information to be added to weights and biases config data (dict).
  • wandb_name: Name of the run to be displayed on weights and biases (str).

Using Hierarchical Label Smoothing (HLS)

By default, the SeqClassTrainer.train method will use Hierarchical Label Smoothing (HLS). HLS is a method that, like label smoothing, transforms the original one-hot target distribution into a soft distribution that does not only put weight on the target class but also on other classes. The difference with standard label smoothing is that the amount of weight per class is not uniformly distributed, but determined by the target labels on higher taxonomic levels. Whether or not a child class is part of the higher-level target label determines whether extra weight is added to this class. In other words, the amount of label smoothing for a certain taxon is determined by how hierarchically similar this taxon is to the true target label. For instance, if the true label at family-level is Agaricaceae, then a certain amount of weight is added to all genera and species that are part of the Agaricaceae family. This is explained more formally in our paper.

The amount of HLS is controlled by the label_smoothing argument of the SeqClassTrainer.train method. It is a list of six decimals that controls how much label smoothing should be added per taxonomic level. Specifically, the values represent how much weight is divided over child classes of correct parent labels. E.g. if label_smoothing[0]==0.1 and the target phylum P1 has two child taxons C1 and C2, both of these taxons will receive an 0.1/2 amount of weight added to their target distributions. Note that the sixth element of this list does not correspond to species-level smoothing (as species don't have child classes) but refers to the amount of weight that is divided uniformly over all classes. Hence, [0,0,0,0,0,0.1] corresponds to standard label smoothing with $\epsilon=0.1$, [0.02,02,0.02,0.02,0.02,0] to hierarchical label smoothing with $\epsilon=0.1$

import torch
import torch
from mycoai.data import TensorData
from mycoai.train import SeqClassTrainer

train = TensorData(filepath='train_dataset.pt')
valid = TensorData(filepath='validation_dataset.pt')
model = torch.load('model.pt')

settings = [
    [0.00, 0.00, 0.00, 0.00, 0.00, 0.1], # Standard Label Smoothing
    [0.02, 0.02, 0.02, 0.02, 0.02, 0.0], # Hierarchical Label Smoothing
    [0.00, 0.00, 0.00, 0.00, 0.00, 0.0], # No Label Smoothing
    None, # No Label Smoothing (alternative)
]

# E.g. custom optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for smoothing in settings:
    model, history = SeqClassTrainer.train(
        model, train, valid, label_smoothing=smoothing, optimizer=optimizer
    )

mycoai.evaluate.Evaluator

Evaluation and analysis of ITS classification algorithms. An Evaluator object is initialized for a specific classifier-dataset combination. It is designed to operate on classifications (in pd.DataFrame format) made by any type of classifier, also non-deep learning classifiers. Results will be graphically displayed on a Weights and Biases run, which is created upon initialization. It is important to call the wandb_finish method before initializing a new SeqClassTrainer or Evaluator object, such that the previous W&B run can finish nicely before a new one is created.

• __init__(self, classification, reference, classifier=None, wandb_config={}, wandb_name=None): Initializes Evaluator instance for specified dataset (and model).
  • classification: The to-be-evaluated classification (prediction) (pd.DataFrame).
  • reference: Reference dataset with true labels (pd.DataFrame | mycoai.Data).
  • classifier: If provided and equipped with a .get_config method, will add its configuration information to the wandb run (Default is None).
  • wandb_config: Extra information to be added to weights and biases config data (dict).
  • wandb_name: Name of the run to be displayed on weights and biases (str).
• test(self, metrics=utils.EVAL_METRICS, levels=utils.LEVELS): Calculates classification performance in terms of specified metrics. Results are printed, returned as pd.DataFrame, and graphically displayed on W&B.
• detailed_report(self, level='species', train_data=None, latent_repr=None): Provides detailed information (on W&B) for one specified level. If train_data is specified, will create graphs that plot compare the class-specific performance to its occurrence frequency in the dataset. If latent_repr is specified, will create a 2D visualization of the inputted latent space with class-specific performance information that can be read when hovering over the data points. The dimensions of the latent space are reduced by applying the t-distributed Stochastic Neighbourhood Embedding (t-SNE) algorithm to the first 50 principal components of the latent space.

Visualizing a model's latent space

Visualizing the model's latent space creates a taxonomic map of the dataset that can lead to insights in the model's inner workings and overall performance. Such a visualization can be created as follows:

import torch
from mycoai.data import Data
from mycoai.evaluate import Evaluator

test_data = Data('test.fasta') # Loading a test set
model = torch.load('model.pt') # Loads a pre-saved SeqClassNetwork object
classification = model.classify(test_data)
latent_repr = model.latent_space(test_data)

# Results will be visible on W&B
evaluator = Evaluator(classification, test_data, classifier=model)
evaluator.detailed_report('species', latent_repr=latent_repr)

mycoai.utils

This module contains several package-wide constants and helper functions. Modifying some of the variables specified here can break the workings of the code. However, there are constants that can be set according to the user's preference:

• VERBOSE: controls the verbosity (how much is printed) of MycoAI. Set this value to 0 to hide most print statements (one of [0,1,2], default is 1).
• PRED_BATCH_SIZE: batch size for predictions (outside of training). Setting this value higher will lead to the consumption of more (GPU) memory, whereas setting this value lower will lead to slower performance (int, default is 64).
• WANDB_PROJECT: name of the W&B project to push the results to (str, default is 'ITS Classification').

Furthermore, the user might be interested in the following helper functions:

• set_output_dir(path, parent=''): Sets (global) output directory, creates new if it does not exist. Returns a string with the path to the newly created directory. Controls the OUTPUT_DIR variable, which avoids the accumulation of results files in your working directory.
• set_device(name): Sets (global) PyTorch device (either 'cpu', 'cuda', or 'cuda:0', 'cuda:1', etc.). Can be helpful to force the use of cpu even if you have a GPU available. By default and upon initialization, MycoAI will look for available GPUs and fall back to CPU only if no GPU is available.
• get_config(object=None, prefix=''): Returns configuration information. Most MycoAI objects are equipped with a get_config method. This helper function calls the get_config method of the inputted object, returning a dictionary of configuration information such as hyperparameter settings. MycoAI uses this functionality to log and attach as much information as possible to Weights and Biases runs. However, this feature can also be very useful if you wish to document or know more about the settings of your model/training.

Switching to CPU or a specific GPU

The example below demonstrates how you can use the utils module to force the use of CPU or a specific GPU:

from mycoai import utils

utils.set_device('cpu') # Switches to cpu
utils.set_device('cuda:2') # Switches to the 3rd cuda device (3rd GPU)

License

Distributed under the MIT License. See LICENSE for more information.