diff --git a/README.md b/README.md index 3873972..791d1c4 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,13 @@

- $${\Huge{\textbf{\textsf{\color{#2E8B57}Bio\color{#4682B4}fit}}}}$$ + $${\Huge{\textbf{\textsf{\color{#2E8B57}Bio\color{red}fit}}}}$$

- Build - GitHub - Documentation - GitHub release + Build + GitHub + Documentation + GitHub release Contributor Covenant DOI

@@ -29,7 +29,7 @@ and configurable processing pipelines. Here are some of the main features of Bio - [CSR (SciPy)](https://github.com/scipy/scipy) - [Arrow](https://github.com/apache/arrow) - 🤗 [Datasets](https://github.com/huggingface/datasets) - - [Biosets](https://github.com/psmyth94/biosets) + - [biofit](https://github.com/psmyth94/biofit) - **Machine Learning Models:** Supports a wide range of machine learning models, including: - [Scikit-learn](https://github.com/scikit-learn/scikit-learn) - [Random Forest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) @@ -98,11 +98,11 @@ print(preprocessor) preprocessed_data = preprocessor.fit_transform(dataset) ``` -Biofit is made with Biosets in mind. You can pass the loaded dataset instead of a string +Biofit is made with biofit in mind. You can pass the loaded dataset instead of a string to load the preprocessors: ```python -from biosets import load_dataset +from biofit import load_dataset dataset = load_dataset("csv", data_files="my_file.csv", experiment_type="snp") @@ -120,7 +120,7 @@ Biofit allows you to create custom preprocessing pipelines using the `sklearn` and Biofit in a single operation: ```python -from biosets import load_dataset +from biofit import load_dataset from biofit.preprocessing import LogTransformer, PreprocessorPipeline from sklearn.preprocessing import StandardScaler