docs(readme): Grammar fixes
parent
e3e27408bb
commit
4f523d1bf5
20
README.md
20
README.md
|
@ -8,8 +8,8 @@
|
||||||
[**Documentation**](https://nyaggle.readthedocs.io/en/latest/index.html)
|
[**Documentation**](https://nyaggle.readthedocs.io/en/latest/index.html)
|
||||||
| [**Slide (Japanese)**](https://docs.google.com/presentation/d/1jv3J7DISw8phZT4z9rqjM-azdrQ4L4wWJN5P-gKL6fA/edit?usp=sharing)
|
| [**Slide (Japanese)**](https://docs.google.com/presentation/d/1jv3J7DISw8phZT4z9rqjM-azdrQ4L4wWJN5P-gKL6fA/edit?usp=sharing)
|
||||||
|
|
||||||
**nyaggle** is a utility library for Kaggle and offline competitions,
|
**nyaggle** is an utility library for Kaggle and offline competitions.
|
||||||
particularly focused on experiment tracking, feature engineering and validation.
|
It is particularly focused on experiment tracking, feature engineering, and validation.
|
||||||
|
|
||||||
- **nyaggle.ensemble** - Averaging & stacking
|
- **nyaggle.ensemble** - Averaging & stacking
|
||||||
- **nyaggle.experiment** - Experiment tracking
|
- **nyaggle.experiment** - Experiment tracking
|
||||||
|
@ -22,19 +22,19 @@ particularly focused on experiment tracking, feature engineering and validation.
|
||||||
|
|
||||||
You can install nyaggle via pip:
|
You can install nyaggle via pip:
|
||||||
|
|
||||||
```Shell
|
```bash
|
||||||
$pip install nyaggle
|
pip install nyaggle
|
||||||
```
|
```
|
||||||
|
|
||||||
## Examples
|
## Examples
|
||||||
|
|
||||||
### Experiment Tracking
|
### 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,
|
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
|
```python
|
||||||
from sklearn.model_selection import train_test_split
|
from sklearn.model_selection import train_test_split
|
||||||
|
@ -55,7 +55,7 @@ result = run_experiment(params,
|
||||||
y_train,
|
y_train,
|
||||||
X_test)
|
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.test_prediction) # Test prediction in numpy array
|
||||||
print(result.oof_prediction) # Out-of-fold 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
|
#### Text Vectorization using BERT
|
||||||
|
|
||||||
You need to install pytorch to your virtual environment to use BertSentenceVectorizer.
|
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
|
```python
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
|
@ -183,7 +183,7 @@ auc, importance = adversarial_validate(train, test, importance_type='gain')
|
||||||
|
|
||||||
### Validation Splitters
|
### 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
|
```python
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
|
|
Loading…
Reference in New Issue