chore: ml basic boilerplate

experiments/ml/arch.py

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+# this should retrun a model with exposed methods fit and transform method in an sklearn style
+from sklearn.base import BaseEstimator, TransformerMixin
+from procesing.context import PipelineContext
+from typing import Any
+
+TASK = 'classification'
+LABELS = ['agent', 'human']
+
+class BaseModel(BaseEstimator, TransformerMixin):
+    def __init__(self, context: PipelineContext):
+        self.context = context
+
+    def fit(self, X=None, y=None):
+        return self
+
+    def transform(self, X) -> Any:
+        pass

experiments/ml/evals.py

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+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
+from torch.utils.tensorboard import SummaryWriter
+from logging import getLogger
+logger = getLogger(__name__)
+
+def evaluate(perdicted_class, predicted_proba, true_class, writer: SummaryWriter, epoch: int):
+    accuracy = accuracy_score(true_class, perdicted_class)
+    precision = precision_score(true_class, perdicted_class)
+    recall = recall_score(true_class, perdicted_class)
+    f1 = f1_score(true_class, perdicted_class)
+    roc_auc = roc_auc_score(true_class, predicted_proba)
+
+    writer.add_scalar('Eval/Accuracy', accuracy, epoch)
+    writer.add_scalar('Eval/Precision', precision, epoch)
+    writer.add_scalar('Eval/Recall', recall, epoch)
+    writer.add_scalar('Eval/F1_Score', f1, epoch)
+    writer.add_scalar('Eval/ROC_AUC', roc_auc, epoch)
+
+    logger.info(f"Eval Metrics - Epoch {epoch}: Accuracy: {accuracy:.4f}, Precision: {precision:.4f}, Recall: {recall:.4f}, F1 Score: {f1:.4f}, ROC AUC: {roc_auc:.4f}")

experiments/ml/train.py

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+from torch.utils.tensorboard import SummaryWriter
+from logging import getLogger
+from evals import evaluate
+logger = getLogger(__name__)
+
+
+def train():
+    pass
+
+
+
+if __name__ == "__main__":
+    train()