metric_f1_score {DeepPINCS} | R Documentation |
The F1-score is a metric combining precision and recall. It is typically used instead of accuracy in the case of severe class imbalance in the dataset. The higher the values of F1-score, the better the validation of the model.
Dongmin Jung
Kubben, P., Dumontier, M., & Dekker, A. (2019). Fundamentals of clinical data science. Springer.
Mishra, A., Suseendran, G., & Phung, T. N. (Eds.). (2020). Soft Computing Applications and Techniques in Healthcare. CRC Press.
keras::k_equal, keras::k_sum, tensorflow::tf
compound_length_seq <- 50 compound_embedding_dim <- 16 protein_embedding_dim <- 16 protein_length_seq <- 100 mlp_cnn_cpi <- fit_cpi( smiles = example_cpi[1:100, 1], AAseq = example_cpi[1:100, 2], outcome = example_cpi[1:100, 3], compound_type = "sequence", compound_length_seq = compound_length_seq, compound_embedding_dim = compound_embedding_dim, protein_length_seq = protein_length_seq, protein_embedding_dim = protein_embedding_dim, net_args = list( compound = "mlp_in_out", compound_args = list( fc_units = c(10), fc_activation = c("relu")), protein = "cnn_in_out", protein_args = list( cnn_filters = c(32), cnn_kernel_size = c(3), cnn_activation = c("relu"), fc_units = c(10), fc_activation = c("relu")), fc_units = c(1), fc_activation = c("sigmoid"), loss = "binary_crossentropy", optimizer = keras::optimizer_adam(), metrics = custom_metric("F1_score", metric_f1_score)), epochs = 2, batch_size = 16)