Upload 2 files

config.py

@@ -0,0 +1,69 @@
+# config.py
+
+import torch
+import os
+
+# --- Paths ---
+# Adjust DATA_PATH to your actual data location
+DATA_PATH = './data/synthetic_transactions_samples_5000.csv'
+TOKENIZER_PATH = './tokenizer/'
+LABEL_ENCODERS_PATH = './label_encoders.pkl'
+MODEL_SAVE_DIR = './saved_models/'
+PREDICTIONS_SAVE_DIR = './predictions/' # To save predictions for voting ensemble
+
+# --- Data Columns ---
+TEXT_COLUMN = "Sanction_Context"
+# Define all your target label columns
+LABEL_COLUMNS = [
+    "Red_Flag_Reason",
+    "Maker_Action",
+    "Escalation_Level",
+    "Risk_Category",
+    "Risk_Drivers",
+    "Investigation_Outcome"
+]  
+# Example metadata columns. Add actual numerical/categorical metadata if available in your CSV.
+# For now, it's an empty list. If you add metadata, ensure these columns exist and are numeric or can be encoded.
+METADATA_COLUMNS = [] # e.g., ["Risk_Score", "Transaction_Amount"]
+
+# --- Model Hyperparameters ---
+MAX_LEN = 128       # Maximum sequence length for transformer tokenizers
+BATCH_SIZE = 16     # Batch size for training and evaluation
+LEARNING_RATE = 2e-5 # Learning rate for AdamW optimizer
+NUM_EPOCHS = 3      # Number of training epochs. Adjust based on convergence.
+DROPOUT_RATE = 0.3  # Dropout rate for regularization
+
+# --- Device Configuration ---
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# --- Specific Model Configurations ---
+BERT_MODEL_NAME = 'bert-base-uncased'
+ROBERTA_MODEL_NAME = 'roberta-base'
+DEBERTA_MODEL_NAME = 'microsoft/deberta-base'
+
+# TF-IDF
+TFIDF_MAX_FEATURES = 5000 # Max features for TF-IDF vectorizer
+
+# --- Field-Specific Strategy (Conceptual) ---
+# This dictionary provides conceptual strategies for enhancing specific fields.
+# Actual implementation requires adapting the models (e.g., custom loss functions, metadata integration).
+FIELD_STRATEGIES = {
+    "Maker_Action": {
+        "loss": "focal_loss", # Requires custom Focal Loss implementation
+        "enhancements": ["action_templates", "context_prompt_tuning"] # Advanced NLP concepts
+    },
+    "Risk_Category": {
+        "enhancements": ["numerical_metadata", "transaction_patterns"] # Integrate METADATA_COLUMNS
+    },
+    "Escalation_Level": {
+        "enhancements": ["class_balancing", "policy_keyword_patterns"] # Handled by class weights/metadata
+    },
+    "Investigation_Outcome": {
+        "type": "classification_or_generation" # If generation, T5/BART would be needed.
+    }
+}
+
+# Ensure model save and predictions directories exist
+os.makedirs(MODEL_SAVE_DIR, exist_ok=True)
+os.makedirs(PREDICTIONS_SAVE_DIR, exist_ok=True)
+os.makedirs(TOKENIZER_PATH, exist_ok=True)

dataset_utils.py

@@ -0,0 +1,165 @@
+# dataset_utils.py
+
+import pandas as pd
+import torch
+from torch.utils.data import Dataset, DataLoader
+from sklearn.preprocessing import LabelEncoder
+from transformers import BertTokenizer, RobertaTokenizer, DebertaTokenizer
+import pickle
+import os
+
+from config import TEXT_COLUMN, LABEL_COLUMNS, MAX_LEN, TOKENIZER_PATH, LABEL_ENCODERS_PATH, METADATA_COLUMNS
+
+class ComplianceDataset(Dataset):
+    """
+    Custom Dataset class for handling text and multi-output labels for PyTorch models.
+    """
+    def __init__(self, texts, labels, tokenizer, max_len):
+        self.texts = texts
+        self.labels = labels
+        self.tokenizer = tokenizer
+        self.max_len = max_len
+
+    def __len__(self):
+        """Returns the total number of samples in the dataset."""
+        return len(self.texts)
+
+    def __getitem__(self, idx):
+        """
+        Retrieves a sample from the dataset at the given index.
+        Tokenizes the text and converts labels to a PyTorch tensor.
+        """
+        text = str(self.texts[idx])
+        # Tokenize the text, padding to max_length and truncating if longer.
+        # return_tensors="pt" ensures PyTorch tensors are returned.
+        inputs = self.tokenizer(
+            text,
+            padding='max_length',
+            truncation=True,
+            max_length=self.max_len,
+            return_tensors="pt"
+        )
+        # Squeeze removes the batch dimension (which is 1 here because we process one sample at a time)
+        inputs = {key: val.squeeze(0) for key, val in inputs.items()}
+        # Convert labels to a PyTorch long tensor
+        labels = torch.tensor(self.labels[idx], dtype=torch.long)
+        return inputs, labels
+
+class ComplianceDatasetWithMetadata(Dataset):
+    """
+    Custom Dataset class for handling text, additional numerical metadata, and multi-output labels.
+    Used for hybrid models combining text and tabular features.
+    """
+    def __init__(self, texts, metadata, labels, tokenizer, max_len):
+        self.texts = texts
+        self.metadata = metadata # Expects metadata as a NumPy array or list of lists
+        self.labels = labels
+        self.tokenizer = tokenizer
+        self.max_len = max_len
+
+    def __len__(self):
+        """Returns the total number of samples in the dataset."""
+        return len(self.texts)
+
+    def __getitem__(self, idx):
+        """
+        Retrieves a sample, its metadata, and labels from the dataset at the given index.
+        Tokenizes text, converts metadata and labels to PyTorch tensors.
+        """
+        text = str(self.texts[idx])
+        inputs = self.tokenizer(
+            text,
+            padding='max_length',
+            truncation=True,
+            max_length=self.max_len,
+            return_tensors="pt"
+        )
+        inputs = {key: val.squeeze(0) for key, val in inputs.items()}
+        # Convert metadata for the current sample to a float tensor
+        metadata = torch.tensor(self.metadata[idx], dtype=torch.float)
+        labels = torch.tensor(self.labels[idx], dtype=torch.long)
+        return inputs, metadata, labels
+
+def load_and_preprocess_data(data_path):
+    """
+    Loads data from a CSV, fills missing values, and encodes categorical labels.
+    Also handles converting specified METADATA_COLUMNS to numeric.
+
+    Args:
+        data_path (str): Path to the CSV data file.
+
+    Returns:
+        tuple: A tuple containing:
+            - data (pd.DataFrame): The preprocessed DataFrame.
+            - label_encoders (dict): A dictionary of LabelEncoder objects for each label column.
+    """
+    data = pd.read_csv(data_path)
+    data.fillna("Unknown", inplace=True) # Fill any missing text values with "Unknown"
+
+    # Convert metadata columns to numeric, coercing errors and filling NaNs with 0
+    # This ensures metadata is suitable for neural networks.
+    for col in METADATA_COLUMNS:
+        if col in data.columns:
+            data[col] = pd.to_numeric(data[col], errors='coerce').fillna(0) # Fill NaN with 0 or a suitable value
+
+    label_encoders = {col: LabelEncoder() for col in LABEL_COLUMNS}
+    for col in LABEL_COLUMNS:
+        # Fit and transform each label column using its respective LabelEncoder
+        data[col] = label_encoders[col].fit_transform(data[col])
+    return data, label_encoders
+
+def get_tokenizer(model_name):
+    """
+    Returns the appropriate Hugging Face tokenizer based on the model name.
+
+    Args:
+        model_name (str): The name of the pre-trained model (e.g., 'bert-base-uncased').
+
+    Returns:
+        transformers.PreTrainedTokenizer: The initialized tokenizer.
+    """
+    if "bert" in model_name.lower():
+        return BertTokenizer.from_pretrained(model_name)
+    elif "roberta" in model_name.lower():
+        return RobertaTokenizer.from_pretrained(model_name)
+    elif "deberta" in model_name.lower():
+        return DebertaTokenizer.from_pretrained(model_name)
+    else:
+        raise ValueError(f"Unsupported tokenizer for model: {model_name}")
+
+def save_label_encoders(label_encoders):
+    """
+    Saves a dictionary of label encoders to a pickle file.
+    This is crucial for decoding predictions back to original labels.
+
+    Args:
+        label_encoders (dict): Dictionary of LabelEncoder objects.
+    """
+    with open(LABEL_ENCODERS_PATH, "wb") as f:
+        pickle.dump(label_encoders, f)
+    print(f"Label encoders saved to {LABEL_ENCODERS_PATH}")
+
+def load_label_encoders():
+    """
+    Loads a dictionary of label encoders from a pickle file.
+
+    Returns:
+        dict: Loaded dictionary of LabelEncoder objects.
+    """
+    with open(LABEL_ENCODERS_PATH, "rb") as f:
+        return pickle.load(f)
+    print(f"Label encoders loaded from {LABEL_ENCODERS_PATH}")
+
+
+def get_num_labels(label_encoders):
+    """
+    Returns a list containing the number of unique classes for each label column.
+    This list is used to define the output dimensions of the model's classification heads.
+
+    Args:
+        label_encoders (dict): Dictionary of LabelEncoder objects.
+
+    Returns:
+        list: A list of integers, where each integer is the number of classes for a label.
+    """
+    return [len(label_encoders[col].classes_) for col in LABEL_COLUMNS]