README

dm_utils is a utility for Data Mining.

Installation

pip install dm_utils

Usage

• dm_utils.hom : hold-out method

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from dm_utils.hom import HOM

x, y = load_iris(return_X_y=True, as_frame=True)
xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2, random_state=42)
# classification task, xgboost and lightgbm model
hom = HOM(task='cls', model=['xgb', 'lgb'])
hom.fit(xtrain, ytrain, record_time=True)
ypred = (hom.predict(xtest) > 0.5).argmax(axis=1)
print(accuracy_score(ypred, ytest))

• dm_utils.oof : out of fold prediction

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from dm_utils.oof import OOF

x, y = load_breast_cancer(return_X_y=True, as_frame=True)
xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2, random_state=42)
# classification task, 2*xgboost, 2*lightgbm and 1*catboost model for 5-fold oof
oof = OOF(task='cls', model=['xgb', 'xgb', 'lgb', 'lgb', 'cb'])
oof.fit(xtrain, ytrain, record_time=True)
ypred = oof.predict(xtest) > 0.5
print(accuracy_score(ypred, ytest))

Features

support algorithm: scikit-learn, xgboost, lightgbm, catboost, ngboost and pytorch-tabnet