SemiSupervised
Describe
This is a Semi-supervised learning framework of Python. You can use it for classification task in machine learning.
Install
pip install semisupervised
API
We have implemented following semi-supervised learning algorithm.
All the methods are similar to Sklearn Semi-supervised API.
Step 1. The unlabeled samples should be labeled as -1.
Step2. model.fit(X,y)
Step3. model.predict(X_test)
-
LabelPropagation
-
S3VM
-
PseudoLabelNeuralNetworkClassifier
This is pseudo-label semi-supervised learning,
PseudoLabelNeuralNetworkClassifiershould work withPseudoCallback.
Statement
Some of the code comes from the Internet.
Examples
from __future__ import absolute_import
import numpy as np
from sklearn import datasets
from sklearn import metrics
from sklearn.model_selection import train_test_split
# normalization
def normalize(x):
return (x - np.min(x))/(np.max(x) - np.min(x))
def get_data():
X, y = datasets.load_breast_cancer(return_X_y=True)
X = normalize(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.6, random_state = 0)
rng = np.random.RandomState(42)
random_unlabeled_points = rng.rand(len(X_train)) < 0.1
y_train[random_unlabeled_points] = -1
#
index, = np.where(y_train != -1)
label_X_train = X_train[index,:]
label_y_train = y_train[index]
index, = np.where(y_train == -1)
unlabel_X_train = X_train[index,:]
unlabel_y = -1*np.ones(unlabel_X_train.shape[0]).astype(int)
return label_X_train, label_y_train, unlabel_X_train, unlabel_y, X_test, y_test
label_X_train, label_y_train, unlabel_X_train, unlabel_y, X_test, y_test = get_data()
# import
from semisupervised import S3SVM
model = S3SVM()
model.fit(np.vstack((label_X_train, unlabel_X_train)), np.append(label_y_train, unlabel_y))
# predict
predict = model.predict(X_test)
acc = metrics.accuracy_score(y_test, predict)
# metric
print("accuracy", acc)Please see examples folder for more examples.
