DLMatchingModel
Extracts model predictions. The method calls the tf.keras.predict method of the. It automatically constructs a data generator from the left and right datasets iterating over all the elements of their Cartesian product.
NSMatchingModel
Extracts model predictions. It automatically constructs a data generator from the left and right datasets iterating over all the elements of their Cartesian product. The predictions are calculated using a custom loop over the batches of the generator.
RefutationModel
Extracts model predictions. It automatically constructs a data generator from the left and right datasets iterating over all the elements of their Cartesian product. The predictions are calculated using a custom loop over the batches of the generator.
Usage
# S3 method for class 'neer_match.matching_model.DLMatchingModel'
predict(object, left, right, ...)
# S3 method for class 'neer_match.matching_model.NSMatchingModel'
predict(object, left, right, batch_size = 32L, ...)
# S3 method for class 'neer_match.reasoning.RefutationModel'
predict(object, left, right, batch_size = 32L, ...)Arguments
- object
A matching model object.
- left
A data frame with the left records.
- right
A data frame with the right records.
- ...
Additional arguments passed to the tf.keras.predict method.
- batch_size
The batch size (integer).
Examples
smap <- SimilarityMap(
instructions = list(
`score` = list("gaussian", "euclidean"),
`platform` = list("osa", "indel")
)
)
model <- DLMatchingModel(smap)
compile(model, loss = tensorflow::tf$keras$losses$BinaryCrossentropy())
matching_data <- fuzzy_games_example_data()
fit(
model,
matching_data$left, matching_data$right, matching_data$matches,
epochs = 1L,
verbose = 0L
)
#> <keras.src.callbacks.history.History object at 0x7f309c2f0190>
predict(model, matching_data$left[1:2, ], matching_data$right[1:2, ])
#> [1] 0.4674354 0.4675773 0.4675773 0.4674354