fix pypi release

.github/workflows/CI.yml

@@ -49,7 +49,7 @@ jobs:
       - name: Save Artifacts
         uses: actions/upload-artifact@v3
         with:
-          name: wheels
+          name: release-dists
           path: dist
 
   macos-build-test:
@@ -98,7 +98,7 @@ jobs:
       - name: Save Artifacts
         uses: actions/upload-artifact@v3
         with:
-          name: wheels
+          name: release-dists
           path: dist
 
   linux-build-test:
@@ -150,7 +150,7 @@ jobs:
       - name: Save Artifacts
         uses: actions/upload-artifact@v3
         with:
-          name: wheels
+          name: release-dists
           path: dist
 
   cargo-build-test:

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "perpetual"
-version = "0.0.1"
+version = "0.0.2"
 edition = "2021"
 authors = ["Mutlu Simsek <[email protected]>"]
 homepage = "https://perpetual-ml.com/docs"

README.md

@@ -14,13 +14,24 @@
 
 ## _A self-generalizing, hyperparameter-free gradient boosting machine_
 
-PerpetualBooster is a gradient boosting machine (GBM) algorithm which doesn't have hyperparameters to be tuned so that you can use it without needing hyperparameter optimization packages unlike other GBM algorithms. Similar to AutoML libraries, it has a `budget` parameter which ranges between (0, 1). Increasing the `budget` parameter increases predictive power of the algorithm and gives better results on unseen data. Start with a small budget and increase it once you are confident with your features. If you don't see any improvement with further increasing `budget`, it means that you are already extracting the most predictive power out of your data.
+PerpetualBooster is a gradient boosting machine (GBM) algorithm which doesn't have hyperparameters to be tuned so that you can use it without needing hyperparameter optimization packages unlike other GBM algorithms. Similar to AutoML libraries, it has a `budget` parameter which ranges between `(0, 1)`. Increasing the `budget` parameter increases predictive power of the algorithm and gives better results on unseen data. Start with a small budget and increase it once you are confident with your features. If you don't see any improvement with further increasing `budget`, it means that you are already extracting the most predictive power out of your data.
 
-Hyperparameter optimization usually takes 100 iterations with plain GBM algorithms. PerpetualBooster achieves the same accuracy in the single run. Thus, it achieves around 100x speed-up at the same accuracy with different `budget` levels and with different datasets. The speed-up might be slightly lower or significantly higher than 100 depending on the dataset.
+Hyperparameter optimization usually takes 100 iterations with plain GBM algorithms. PerpetualBooster achieves the same accuracy in the single run. Thus, it achieves around 100x speed-up at the same accuracy with different `budget` levels and with different datasets. The speed-up might be slightly lower or significantly higher than 100x depending on the dataset.
 
 PerpetualBooster prevents overfitting with a generalization algorithm. The paper is work-in-progress to explain the algorithm.
 
-All of the algorithm code is written in [Rust](https://www.rust-lang.org/), with a python wrapper. Check examples folders for both Rust and Python. All of the rust code for the package can be found in the [src](src/) directory, while all of the python wrapper code is in the [python-package](python-package/) directory.
+All of the algorithm code is written in [Rust](https://www.rust-lang.org/) with a python wrapper. All of the rust code for the package can be found in the [src](src/) directory, while all of the python wrapper code is in the [python-package](python-package/) directory.
+
+## Usage
+
+You can use the algorithm like in the example below. Check examples folders for both Rust and Python.
+
+```python
+from perpetual import PerpetualBooster
+
+model = PerpetualBooster(objective="SquaredLoss")
+model.fit(X, y, budget=0.4)
+```
 
 ## Documentation
 
@@ -37,5 +48,5 @@ pip install perpetual
 To use in a rust project, add the following to your Cargo.toml file.
 
 ```toml
-perpetual = "0.0.1"
+perpetual = "0.0.2"
 ```

python-package/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "py-perpetual"
-version = "0.0.1"
+version = "0.0.2"
 edition = "2021"
 authors = ["Mutlu Simsek <[email protected]>"]
 homepage = "https://perpetual-ml.com/docs"