From 4f523d1bf55b2b62de91e1f5f3e2bc3f5d1a5222 Mon Sep 17 00:00:00 2001 From: r Date: Sat, 23 Apr 2022 22:56:29 -0500 Subject: [PATCH] docs(readme): Grammar fixes --- README.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 04d5b38..6a300a6 100644 --- a/README.md +++ b/README.md @@ -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