General Information
seqlearner.SemiSupervisedLearner(X, y, X_t, y_t)
SemiSupervisedLearner is a Wrapper class for semi-supervised learning methods. This class will apply a specific semi-supervised learning method on the sequences and return score for validation sequences. This markdown contains some information about general and helper functions in SemiSupervisedLearner class.
Arguments
- X: list, numpy ndarray, pandas DataFrame, list of training embedding vectors for learning
- y: list, numpy ndarray, pandas DataFrame, list of training labels
- X_t: list, numpy ndarray, pandas DataFrame, list of test embedding vectors for learning
- y_t: list, numpy ndarray, pandas DataFrame, list of test labels
Example: Apply a SemiSupervised algorithm on unlabeled sequences using PseudoLabeling
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)