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@ -8,8 +8,8 @@
[**Documentation**](https://nyaggle.readthedocs.io/en/latest/index.html)
| [**Slide (Japanese)**](https://docs.google.com/presentation/d/1jv3J7DISw8phZT4z9rqjM-azdrQ4L4wWJN5P-gKL6fA/edit?usp=sharing)
**nyaggle** is a utility library for Kaggle and offline competitions,
particularly focused on experiment tracking, feature engineering and validation.
**nyaggle** is an utility library for Kaggle and offline competitions.
It is particularly focused on experiment tracking, feature engineering, and validation.
- **nyaggle.ensemble** - Averaging & stacking
- **nyaggle.experiment** - Experiment tracking
@ -22,19 +22,19 @@ particularly focused on experiment tracking, feature engineering and validation.
You can install nyaggle via pip:
```Shell
$pip install nyaggle
```bash
pip install nyaggle
```
## Examples
### Experiment Tracking
`run_experiment()` is an high-level API for experiment with cross validation.
`run_experiment()` is a high-level API for experiments with cross validation.
It outputs parameters, metrics, out of fold predictions, test predictions,
feature importance and submission.csv under the specified directory.
feature importance, and submission.csv under the specified directory.
It can be combined with mlflow tracking.
To enable mlflow tracking, include the optional `with_mlflow=True` parameter.
```python
from sklearn.model_selection import train_test_split
@ -55,7 +55,7 @@ result = run_experiment(params,
y_train,
X_test)
# You can get outputs that needed in data science competitions with 1 API
# You can get outputs that are needed in data science competitions with 1 API
print(result.test_prediction) # Test prediction in numpy array
print(result.oof_prediction) # Out-of-fold prediction in numpy array
@ -134,7 +134,7 @@ all.loc[:, cat_cols] = te.fit_transform(all[cat_cols], all[cat_cols])
#### Text Vectorization using BERT
You need to install pytorch to your virtual environment to use BertSentenceVectorizer.
MaCab and mecab-python3 are also required if you use Japanese BERT model.
MaCab and mecab-python3 are also required if you use the Japanese BERT model.
```python
import pandas as pd
@ -183,7 +183,7 @@ auc, importance = adversarial_validate(train, test, importance_type='gain')
### Validation Splitters
nyaggle provides a set of validation splitters that compatible with sklearn interface.
nyaggle provides a set of validation splitters that are compatible with sklearn.
```python
import pandas as pd