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License: Other
Fast Gradient Boosting Decision Trees with Bit-Level Data Structures
License: Other
Hi @laudv
Thank you for your paper and for sharing your project; it presents a very interesting approach.
I've successfully built and installed it on Google Colab. You can find it here:
https://colab.research.google.com/drive/1lz6ps34TWMsVm07cnW3EC--pIPTY4PgT?usp=sharing
I'm relatively new to Rust, so I'm not entirely sure if I've followed all the necessary steps correctly. During the build process, I encountered several warnings like the one below:
warning: unused import: `BitsliceLayout`
--> src/count_and_sum.rs:11:23
|
11 | use crate::bitslice::{BitsliceLayout, BitsliceWithLayout};
| ^^^^^^^^^^^^^^
|
= note: `#[warn(unused_imports)]` on by default
but your example seems to run smoothly.
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
[<ipython-input-20-7aef979a8edf>](https://localhost:8080/#) in <cell line: 1>()
----> 1 validation[f"prediction_{target}"] = model2.predict(validation[features].to_numpy())
1 frames
[/content/bitboost/python/bitboost/sklearn.py](https://localhost:8080/#) in predict(self, X)
68 check_is_fitted(self, "_is_fitted")
69
---> 70 self._bitboost.set_data(X)
71 return self._bitboost.predict()
72
[/content/bitboost/python/bitboost/bitboost.py](https://localhost:8080/#) in set_data(self, data, cat_features)
126 self._check()
127 assert isinstance(data, np.ndarray)
--> 128 assert data.dtype == self.numt
129 assert data.shape[1] == self._nfeatures
130
AssertionError:
I'm not sure what's causing it or how to resolve the issue.
model = lgb.LGBMRegressor(
n_estimators=100, # If you want to use a larger model we've found 20_000 trees to be better
learning_rate=0.01, # and a learning rate of 0.001
max_depth=5, # and max_depth=6
num_leaves=2**5-1, # and num_leaves of 2**6-1
colsample_bytree=0.1
)
To ensure an "apples-to-apples" comparison, how should I configure BitBoost?
It'd be great if BitBoost could give similar accuracy with fraction of time :)
Best regards,
Marek
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