metric_f1_score {DeepPINCS} | R Documentation

## F1-score

### Description

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.

### Author(s)

Dongmin Jung

### References

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.

### See Also

keras::k_equal, keras::k_sum, tensorflow::tf

### Examples

    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)

[Package DeepPINCS version 1.0.2 Index]