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Merged
merged 2 commits into from
May 20, 2023
Merged

Update project for 0.0.3 #14

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98710fd
Update dependencies
frederikhoengaard May 20, 2023
5f51194
Update project for 0.0.3
frederikhoengaard May 20, 2023
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1 change: 1 addition & 0 deletions Pipfile
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@ pandas = "==1.5.*"
scikit-learn = "*"
tqdm = "*"
jupyter = "*"
xgboost = "*"

[dev-packages]
black = "==23.*"
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62 changes: 37 additions & 25 deletions Pipfile.lock
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13 changes: 13 additions & 0 deletions README.md
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Expand Up @@ -5,6 +5,18 @@

The aim of lazy-learn is exactly that. Given a dataset, easy-learn will analyse types and distributions of attributes, preprocess, feature-engineer and ultimately train models to be used for further evaluation or inference.

## Upcoming features

Current stable version is 0.0.3. The upcoming updates will support:
- Abstract construction of model architectures
- XGBoost, LightGBM, Adaboost and Catboost architectures
- Time partitioning of datasets
- Automated Hyperparameter Optimisation (HPO)
- Text features
- An interface to AutoGluon
- Outlier detection and handling
- Automated suggestions of performance metrics

## Usage

Using lazy-learn revolves around the `LazyLearner` class. You can think of it as a kind of project, and it is the wrapper for any experiment within lazy-learn.
Expand All @@ -17,6 +29,7 @@ lazy-learn requires:

- pandas
- scikit-learn
- xgboost

### User Installation
```
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2 changes: 1 addition & 1 deletion pyproject.toml
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Expand Up @@ -4,7 +4,7 @@ build-backend = "hatchling.build"

[project]
name = "lazylearn"
version = "0.0.2"
version = "0.0.3"
authors = [
{ name="Frederik P. Høngaard", email="mail@frederikhoengaard.com" },
]
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