Fixes for evaluation

Project_Model/Libs/TransformerUtils/metrics.py

@@ -0,0 +1,99 @@
+import evaluate as eval
+
+BLEU = eval.load("bleu")
+ROUGE = eval.load("rouge")
+METEOR = eval.load("meteor")
+
+def precision(ref: list[int], pred: list[int]):
+    metric = eval.load("precision")
+    return metric.compute(predictions=pred, references=ref, average="weighted", zero_division=0)
+
+
+def recall(ref: list[int], pred: list[int]):
+    metric = eval.load("recall")
+    return metric.compute(predictions=pred, references=ref, average="weighted", zero_division=0)
+
+
+def accuracy(ref: list[int], pred: list[int]):
+    metric = eval.load("accuracy")
+    return metric.compute(predictions=pred, references=ref)
+
+
+def meteor(ref: list[str], pred: list[str]):
+    metric = METEOR
+    return metric.compute(predictions=pred, references=ref)
+
+
+def bleu(ref: list[str], pred: list[str]):
+    metric = BLEU
+    return metric.compute(predictions=pred, references=ref)
+
+
+def rouge(ref: list[str], pred: list[str]):
+    metric = ROUGE
+    return metric.compute(predictions=pred, references=ref)
+
+
+def f1(precision: float, recall: float):
+    return (2 * recall * precision) / (precision + recall)
+
+
+def average(array: list[float]):
+    return sum(array) / len(array)
+
+
+def rdf2txt(ref: list[str], pred: list[str]):
+
+    b_m = bleu(ref, pred)
+    r_m = rouge(ref, pred)
+    m_m = meteor(ref, pred)
+
+    return (b_m, r_m, m_m)
+
+def txt2rdf(ref: list[int], pred: list[int]):
+
+    p_m = precision(ref, pred)
+    r_m = recall(ref, pred)
+
+    return (p_m, r_m)
+
+def rdf_completion_1(ref: list[int], pred: list[int]):
+
+    a_m = accuracy(ref, pred)
+
+    return a_m
+
+
+def rdf_completion_2(ref: list[int], pred: list[int]):
+
+    p_m = precision(ref, pred)
+    r_m = recall(ref, pred)
+
+    return (p_m, r_m)
+
+
+def remove_padding(seq: list[int], pad_token: int, end_token: int):
+    clean_seq = list(filter(lambda x: x != pad_token, seq))
+
+    if clean_seq[-1] == end_token:
+        return clean_seq
+
+    clean_seq.append(
+        end_token
+    )
+
+    return clean_seq
+
+
+def balance_paddings(seq_1: list[int], seq_2: list[int], pad_token: int):
+    SEQ_1_LEN = len(seq_1)
+    SEQ_2_LEN = len(seq_2)
+
+    if SEQ_1_LEN > SEQ_2_LEN:
+        PAD = [pad_token] * (SEQ_1_LEN - SEQ_2_LEN)
+        seq_2.extend(PAD)
+
+    if SEQ_2_LEN > SEQ_1_LEN:
+        seq_2 = seq_2[:SEQ_1_LEN]
+
+    return (seq_1, seq_2)