add visualization for training curves, pr, roc

models/__init__.py

@@ -1,3 +1,6 @@
+import keras.backend as K
+
+import dataset
 from . import pauls_networks
 from . import renes_networks
 
@@ -6,7 +9,7 @@ def get_models_by_params(params: dict):
     # decomposing param section
     # mainly embedding model
     network_type = params.get("type")
-    vocab_size = params.get("vocab_size")
+    vocab_size = len(dataset.get_character_dict()) + 1
     embedding_size = params.get("embedding_size")
     input_length = params.get("input_length")
     filter_embedding = params.get("filter_embedding")
@@ -30,3 +33,51 @@ def get_models_by_params(params: dict):
                                        filter_main, kernel_main, dense_dim, embedding_model)
 
     return embedding_model, predict_model
+
+
+def get_metrics():
+    return dict([
+        ("precision", precision),
+        ("recall", recall),
+        ("f1_score", f1_score),
+    ])
+
+
+def get_metric_functions():
+    return [precision, recall, f1_score]
+
+
+def precision(y_true, y_pred):
+    # Count positive samples.
+    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
+    return true_positives / (predicted_positives + K.epsilon())
+
+
+def recall(y_true, y_pred):
+    # Count positive samples.
+    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
+    return true_positives / (possible_positives + K.epsilon())
+
+
+def f1_score(y_true, y_pred):
+    return f_score(1)(y_true, y_pred)
+
+
+def f05_score(y_true, y_pred):
+    return f_score(0.5)(y_true, y_pred)
+
+
+def f_score(beta):
+    def _f(y_true, y_pred):
+        p = precision(y_true, y_pred)
+        r = recall(y_true, y_pred)
+
+        bb = beta ** 2
+
+        fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon())
+
+        return fbeta_score
+
+    return _f