biofit 0.0.1

BioFit: Bioinformatics Machine Learning Framework

$${\Huge{\textbf{\textsf{\color{#2E8B57}Bio\color{red}fit}}}}$$

Biofit is a machine learning library designed for bioinformatics datasets. It provides tools for transforming, extracting, training, and evaluating machine learning models on biomedical data. It also provides automatic data preprocessing, visualization, and configurable processing pipelines. Here are some of the main features of Biofit:

• Automatic Data Preprocessing: Automatically preprocess biomedical datasets using built-in preprocessing steps.
• Automatic Visualization: Automatically visualize data using built-in visualization methods geared towards biomedical data.
• Configurable Processing Pipelines: Define and customize data processing pipelines.
• Data Handling Flexibility: Support for a wide range of data formats, including:
  • Pandas
  • Polars
  • NumPy
  • CSR (SciPy)
  • Arrow
  • 🤗 Datasets
  • Biosets
• Machine Learning Models: Supports a wide range of machine learning models, including:
  • Scikit-learn
    • Random Forest
    • Support Vector Machine
    • Logistic Regression
    • Lasso Regression
  • LightGBM
  • More to come!
• Caching and Reuse: Caches intermediate results using Apache Arrow for efficient reuse.
• Batch Processing and Multiprocessing: Utilize batch processing and multiprocessing for efficient handling of large-scale data.

Installation

You can install Biofit via pip:

pip install biofit

Quick Start

Preprocessing Data

Biofit provides preprocessing capabilities tailored for omics data. You can use built-in classes to load preprocessing steps based on the experiment type or create custom preprocessing pipelines. The preprocessing pipeline in Biofit uses a syntax similar to sklearn and supports distributed processing.

Using a Preprocessor

Biofit allows you to fit and transform your data in a few lines, similar to sklearn. For example, you can use the LogTransformer to apply a log transformation to your data:

from biofit.preprocessing import LogTransformer
import pandas as pd

dataset = pd.DataFrame({"feature1": [1, 2, 3, 4, 5]})
log_transformer = LogTransformer()
preprocessed_data = log_transformer.fit_transform(dataset)
# Applying log transformation: 100%|█████████████████████████████| 5/5 [00:00<00:00, 7656.63 examples/s]
print(preprocessed_data)
#    feature1
# 0  0.000000
# 1  0.693147
# 2  1.098612
# 3  1.386294
# 4  1.609438

Auto Preprocessing

You can automatically apply standard preprocessing steps by specifying the experiment type. This allows you to load tailored preprocessing steps for the type of data you are working with, such as "otu", "asv", "snp", or "maldi":

from biofit.preprocessing import AutoPreprocessor

preprocessor = AutoPreprocessor.for_experiment("snp", [{"min_prevalence": 0.1}, None])
print(preprocessor)
# [('min_prevalence_row', MinPrevalencFilter(min_prevalence=0.1)),
#  ('min_prevalence', MinPrevalenceFeatureSelector(min_prevalence=0.01))]

# Fit and transform the dataset using the preprocessor
preprocessed_data = preprocessor.fit_transform(dataset)

Biofit is made with Biosets in mind. You can pass the loaded dataset instead of a string to load the preprocessors:

from biosets import load_dataset

dataset = load_dataset("csv", data_files="my_file.csv", experiment_type="snp")

preprocessor = AutoPreprocessor.for_experiment(dataset)
print(preprocessor)
# [('min_prevalence_row', MinPrevalencFilter(min_prevalence=0.01)),
#  ('min_prevalence', MinPrevalenceFeatureSelector(min_prevalence=0.01))]
preprocessed_data = preprocessor.fit_transform(dataset)

Custom Preprocessing Pipeline

Biofit allows you to create custom preprocessing pipelines using the PreprocessorPipeline class. This allows chaining multiple preprocessing steps from sklearn and Biofit in a single operation:

from biofit import load_dataset
from biofit.preprocessing import LogTransformer, PreprocessorPipeline
from sklearn.preprocessing import StandardScaler

# Load the dataset
dataset = load_dataset("csv", data_files="my_file.csv")

# Define a custom preprocessing pipeline
pipeline = PreprocessorPipeline(
    [("scaler", StandardScaler()), ("log_transformer", LogTransformer())]
)

# Fit and transform the dataset using the pipeline
preprocessed_data = pipeline.fit_transform(dataset.to_pandas())

For further details, check the advance usage documentation.

License

Biofit is licensed under the Apache 2.0 License. See the LICENSE file for more information.

Contributing

If you would like to contribute to Biofit, please read the CONTRIBUTING guidelines.