Fixes for evaluation

Project_Model/Libs/TransformerUtils/__init__.py

@@ -1,8 +1,14 @@
-from .model_utils import decompose_nano_socrates, create_standalone_model
+from .model_utils import decompose_nano_socrates, create_standalone_model, train2inference
 from .ModelType import ModelType
+from .decode_batch import decode_batch
+from .metrics import precision, recall, accuracy, f1, meteor, bleu, rouge, average, rdf2txt, txt2rdf, rdf_completion_1, rdf_completion_2, remove_padding, balance_paddings
 
 __all__ = [
     "ModelType",
     "decompose_nano_socrates",
-    "create_standalone_model"
+    "create_standalone_model",
+    "decode_batch",
+    "train2inference",
+    "precision", "recall", "accuracy", "f1", "meteor", "bleu", "rouge", "average",
+    "rdf2txt", "txt2rdf", "rdf_completion_1", "rdf_completion_2", "remove_padding", "balance_paddings"
 ]

Project_Model/Libs/TransformerUtils/decode_batch.py

@@ -0,0 +1,16 @@
+import torch
+import Project_Model.Libs.BPE as BPE
+
+def decode_batch(batch: torch.Tensor, tokenizer: BPE.TokeNanoCore ,uknonw_token: int) -> list[str]:
+
+    strings = []
+
+    BATCH, _ = batch.shape
+
+    for i in range(0, BATCH):
+
+        tokens: list[int] = batch.tolist()[i]
+        tokens = list(map(lambda x: uknonw_token if x > tokenizer.vocabulary_size else x, tokens))
+        strings.append(tokenizer.decode(tokens))
+
+    return strings

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)

Project_Model/Libs/TransformerUtils/model_utils.py

@@ -1,13 +1,13 @@
 import torch
 from Project_Model.Libs.Embedder import NanoSocratesEmbedder
-from Project_Model.Libs.Transformer import TrainingModel, NanoSocraDecoder, NanoSocratEncoder, DeToken, Encoder, Decoder
+from Project_Model.Libs.Transformer import TrainingModel,NanoSocratesCore,  NanoSocraDecoder, NanoSocratEncoder, DeToken, Encoder, Decoder
 from .ModelType import ModelType
 
 
 
 def decompose_nano_socrates(
-    model: TrainingModel, vocabulary_size: int, embedding_size: int
-) -> tuple[TrainingModel, NanoSocratEncoder, NanoSocraDecoder]:
+    model: TrainingModel | NanoSocratesCore , vocabulary_size: int, embedding_size: int
+) -> tuple[TrainingModel | NanoSocratesCore, NanoSocratEncoder, NanoSocraDecoder]:
 
     encoder_pieces, decoder_pieces = model.take_pieces()
     encoder_embedder, encoder, encoder_detokener = encoder_pieces
@@ -19,6 +19,26 @@ def decompose_nano_socrates(
         NanoSocraDecoder(decoder_embedder, decoder, decoder_detokener),
     )
 
+def train2inference(
+    train_model: TrainingModel,
+    inference_model: NanoSocratesCore
+) -> NanoSocratesCore:
+
+    encoder_pieces, decoder_pieces = train_model.take_pieces()
+    enc_emb, encoder, enc_det = encoder_pieces
+    dec_emb, decoder, dec_det = decoder_pieces
+    inference_model.load_pieces(
+        enc_emb,
+        dec_emb,
+        encoder,
+        decoder,
+        enc_det,
+        dec_det
+    )
+
+    return inference_model
+
+
 
 def create_standalone_model(
     model_type: ModelType,