PesudoLabeling
seqlearner.SemiSupervisedLearner.naive_bayes(alg, sample_rate)
Pseudo Labeling method for the semi-supervised learning. This method will train a classifier algorithm for labeled sequences. Then, it will predict the labels of unlabeled sequences.
Arguments
- alg: Scikit-learn Object, Sklearn classifier object to be used in training and prediction phase
- sample_rate: Float, The proportion of unlabeled sequences from X_t
Example: predict the unlabeled sequences
from sklearn.model_selection import train_test_split
from seqlearner import MultiTaskLearner
labeled_path = "../data/labeled.csv"
unlabeled_path = "../data/unlabeled.csv"
mtl = MultiTaskLearner(labeled_path, unlabeled_path)
encoding = mtl.embed(word_length=5)
X, y, X_t, y_t = train_test_split(mtl.sequences, mtl.labels, test_size=0.33)
score = mtl.semi_supervised_learner(X, y, X_t, y_t, ssl="pseudo_labeling", sample_rate=0.3)