yascikit-learn 0.1.3

Yet another scikit-learn

yascikit-learn

Yet another scikit-learn

Installation

pip install yascikit-learn

USAGE

Naive Bayes

Negation Naive Bayes

from yasklearn.naive_bayes import NegationNB
from sklearn import datasets

dataset = datasets.load_iris()
X = dataset.data
y = dataset.target
nnb = NegationNB().fit(X, y)
nnb.predict(X)

Selective Naive Bayes

from yasklearn.naive_bayes import SelectiveNB
from sklearn import datasets

dataset = datasets.load_iris()
X = dataset.data
y = dataset.target
snb = SelectiveNB().fit(X, y)
snb.predict(X)

Universal Set Naive Bayes

from yasklearn.naive_bayes import UniversalSetNB
from sklearn import datasets

dataset = datasets.load_iris()
X = dataset.data
y = dataset.target
unb = UniversalSetNB().fit(X, y)
unb.predict(X)

FTRLProximal

from yasklearn.ftrl_proximal import FTRLProximalClassifier
from sklearn import datasets

dataset = datasets.load_iris()
X = dataset.data
y = dataset.target
ftrlc = FTRLProximalClassifier().fit(X, y)
ftrlc.predict(X)

Topic modeling

PLSA

from yasklearn.decomposition import PLSA
from sklearn import datasets

dataset = datasets.load_iris()
X = dataset.data
plsa = PLSA(n_components=3, random_state=1).fit(X)
plsa.predict(X)

PLSV

Note that PLSV has not implemented predict method.

from yasklearn.decomposition import PLSV
from sklearn.datasets import fetch_20newsgroups

newsgroups = fetch_20newsgroups(subset='train')
X = list(map(lambda x: x.split(), newsgroups.data))
plsv = PLSV(n_components=20, n_dimension=2, random_state=1)
plsv.fit_transform(X)

Clustering

XMeans

from yasklearn.cluster import XMeans
from sklearn import datasets

dataset = datasets.load_iris()
X = dataset.data
xm = XMeans(n_clusters=3, random_state=1)
xm.fit_predict(X)

KMedoids

from yasklearn.cluster import KMedoids
from sklearn import datasets

dataset = datasets.load_iris()
X = dataset.data
km = KMedoids(n_clusters=3, random_state=1)
km.fit_predict(X)

XMedoids

from yasklearn.cluster import XMedoids
from sklearn import datasets

dataset = datasets.load_iris()
X = dataset.data
xm = XMedoids(n_clusters=3, random_state=1)
xm.fit_predict(X)

Utility

from yasklearn.model_selection import train_dev_test_split
import numpy as np

X = np.arange(10).reshape((5, 2))
y = range(5)
X_train, X_dev, X_test, y_train, y_dev, y_test = train_dev_test_split(
    X, y, dev_size=0.33, random_state=1)