quick_anomaly_detector.models.TrainEmbedding

class quick_anomaly_detector.models.TrainEmbedding(lr=0.001, num_epochs=1000, patience=10, batch_size=32, embedding_dim=4, nhead=8, d_hid=64, nlayers=2, dropout=0.5)

__init__(lr=0.001, num_epochs=1000, patience=10, batch_size=32, embedding_dim=4, nhead=8, d_hid=64, nlayers=2, dropout=0.5)

Methods

__init__([lr, num_epochs, patience, ...])
embedding_predict(X[, y])
get_encode(x)
load_context(context)	Loads artifacts from the specified PythonModelContext that can be used by predict() when evaluating inputs.
log_model(model_uri[, experiment_id, ...])	If you need credential, make sure you have them in your environment:
padding(sequences, max_length)
predict(context[, model_input, params])	Evaluates a pyfunc-compatible input and produces a pyfunc-compatible output.
train(df_train, df_valid, feature_name, ...)