nyaggle 0.0.1

Code for Kaggle and Offline Competitions.

nyaggle

Code for Kaggle and Offline Competitions

Feature Engineering

Target Encoding

import pandas as pd
import numpy as np

from sklearn.model_selection import KFold
from nyaggle.feature.category_encoder import TargetEncoder


train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
all = pd.concat([train, test]).copy()

cat_cols = [c for c in train.columns if train[c].dtype == np.object]
target_col = 'y'

kf = KFold(5)

# Target encoding with K-fold
te = TargetEncoder(split=kf.split(train))

# use fit/fit_transform to train data, then apply transform to test data
train.loc[:, cat_cols] = te.fit_transform(train[cat_cols], train[target_col])
test.loc[:, cat_cols] = te.transform(test[cat_cols])

# ... or just call fit_transform to concatenated data
all.loc[:, cat_cols] = te.fit_transform(all[cat_cols], all[cat_cols])

NLP

import pandas as pd
from nyaggle.feature.nlp import BertSentenceVectorizer


train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
all = pd.concat([train, test]).copy()

text_cols = ['body']
target_col = 'y'
group_col = 'user_id'


# extract BERT-based sentence vector
bv = BertSentenceVectorizer(text_columns=text_cols)

text_vector = bv.fit_transform(train)


# BERT + SVD, with cuda
bv = BertSentenceVectorizer(text_columns=text_cols, use_cuda=True, n_components=40)

text_vector_svd = bv.fit_transform(train)