Create Enhanced_Business_Model_for_Collaborative_Predictive_Supply_Chain_model.v0.0.py

Enhanced_Business_Model_for_Collaborative_Predictive_Supply_Chain_model.v0.0.py

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+"""
+Enhanced_Business_Model_for_Collaborative_Predictive_Supply_Chain_model.py
+
+This script demonstrates a conceptual Enhanced Business Model for a Collaborative
+Predictive Supply Chain. It uses a custom Transformer-based model (represented
+by a placeholder `TransformerModel` class) and a custom tokenizer
+(`SupplyChainTokenizer` from `tokenizer.py`) with an industry-specific
+vocabulary loaded from `vocab.json`.
+
+This is a *demonstration* script and not a fully functional system.  It outlines
+the key steps involved in such a model:
+
+1.  **Loading Custom Vocabulary:** Loads an industry-specific vocabulary from
+    `vocab.json`.
+2.  **Initializing Custom Tokenizer:** Creates a `SupplyChainTokenizer` using
+    the loaded vocabulary.
+3.  **(Optional) Training BPE:**  Demonstrates how to train Byte-Pair Encoding
+    (BPE) on a text corpus to handle out-of-vocabulary words.
+4.  **Loading Supply Chain Data:**  Loads dummy supply chain data (in Pandas
+    DataFrame format). In a real system, this would come from databases, APIs,
+    etc.
+5.  **Tokenizing Data:**  Uses the `SupplyChainTokenizer` to preprocess and
+    tokenize the supply chain data, preparing it for the Transformer model.
+6.  **Placeholder Transformer Model:**  Uses a dummy `TransformerModel` class
+    to represent a Transformer-based forecasting model.  This class takes
+    tokenized input and attention masks and generates placeholder forecast
+    outputs.
+7.  **Model Prediction:**  Feeds the tokenized data to the dummy Transformer
+    model to generate (placeholder) forecasts.
+8.  **Outputting Forecasts:**  Prints the (placeholder) forecasts.
+
+To run this script:
+
+1.  Ensure you have `tokenizer.py`, `vocab.json`, and `training_data.txt`
+    in the same directory as this script (or adjust file paths accordingly).
+2.  Install required libraries: `pip install tokenizers pandas torch`.
+3.  Run from the command line: `python Enhanced_Business_Model_for_Collaborative_Predictive_Supply_Chain_model.py`
+
+Note: The `TransformerModel` is a simplified placeholder.  A real implementation
+would require a proper Transformer architecture (e.g., using PyTorch or
+TensorFlow), training data, and a more sophisticated training and prediction
+pipeline.
+"""
+import os
+import pandas as pd
+import torch  # Import PyTorch (required for dummy Transformer example)
+
+# Import the custom tokenizer from tokenizer.py (ensure tokenizer.py is in the same directory)
+from tokenizer import SupplyChainTokenizer
+
+
+# --- Define a placeholder Transformer Model ---
+class TransformerModel:
+    """
+    A placeholder for a real Transformer-based forecasting model.
+    In a real implementation, this would be a PyTorch/TensorFlow model.
+    This dummy model simply returns placeholder forecasts.
+    """
+
+    def __init__(self, vocab_size, embedding_dim=64, num_heads=2, num_layers=2, output_dim=1):
+        """
+        Args:
+            vocab_size (int): Vocabulary size of the tokenizer.
+            embedding_dim (int): Dimension of token embeddings.
+            num_heads (int): Number of attention heads.
+            num_layers (int): Number of Transformer layers.
+            output_dim (int): Dimension of the output (e.g., 1 for scalar forecast).
+        """
+        self.vocab_size = vocab_size
+        self.embedding_dim = embedding_dim
+        self.num_heads = num_heads
+        self.num_layers = num_layers
+        self.output_dim = output_dim
+
+        # In a real model, you would initialize layers here (Embedding, TransformerEncoder, Linear, etc.)
+        print(f"Dummy TransformerModel initialized with vocab_size: {vocab_size}")
+
+
+    def forward(self, input_ids, attention_mask):
+        """
+        Placeholder forward pass. In a real model, this would perform
+        Transformer encoding and prediction.
+
+        Args:
+            input_ids (torch.Tensor): Token IDs (batch_size, sequence_length).
+            attention_mask (torch.Tensor): Attention mask (batch_size, sequence_length).
+
+        Returns:
+            torch.Tensor: Placeholder forecast output (batch_size, sequence_length, output_dim).
+        """
+        batch_size, seq_len = input_ids.shape
+        # Dummy output - replace with actual Transformer forward pass
+        dummy_forecasts = torch.randn(batch_size, seq_len, self.output_dim)
+        return dummy_forecasts
+
+    def predict(self, input_ids, attention_mask):
+        """
+        Generates predictions.
+
+        Args:
+            input_ids (List[List[int]]): Token IDs (list of lists).
+            attention_mask (List[List[int]]): Attention masks (list of lists).
+
+        Returns:
+            torch.Tensor: Placeholder forecast output.
+        """
+        # Convert lists to PyTorch tensors
+        input_ids_tensor = torch.tensor(input_ids)
+        attention_mask_tensor = torch.tensor(attention_mask)
+
+        # Call the forward method
+        forecasts = self.forward(input_ids_tensor, attention_mask_tensor)
+        return forecasts
+
+
+if __name__ == "__main__":
+    # --- 0.  Prepare Vocabulary and Training Data (if not already present) ---
+    if not os.path.exists("vocab.json"):
+        print("Creating vocab.json...")
+        vocab = {
+            "[UNK]": 0,
+            "[CLS]": 1,
+            "[SEP]": 2,
+            "[PAD]": 3,
+            "[MASK]": 4,
+            "timestamp:": 5,
+            "sku:": 6,
+            "store_id:": 7,
+            "quantity:": 8,
+            "price:": 9,
+            "discount:": 10,
+            "promotion_id:": 11,
+            "product_category:": 12,
+            "SKU123": 13,  # Example SKU
+            "SKU123-RED": 14, # Example SKU variant
+            "SKU123-BLUE": 15,
+            "STORE456": 16,  # Example store ID
+            "PLANT789": 17, # Example plant ID
+            "WHOLESALER001": 18, # Example Wholesaler
+            "RETAILER002": 19, # Example Retailer
+            "BOGO": 20,
+            "DISCOUNT":21,
+        }
+        with open("vocab.json", "w") as f:
+            json.dump(vocab, f, indent=4)
+
+    if not os.path.exists("training_data.txt"):
+        print("Creating training_data.txt...")
+        with open("training_data.txt", "w", encoding="utf-8") as f:
+            f.write("This is some example text for training the BPE model.\n")
+            f.write("SKU123 is a product. STORE456 is another. plant789 is, too.\n")
+            f.write("This file contains words not in the initial vocabulary.\n")
+
+    # --- 1. Load Vocabulary and Initialize Tokenizer ---
+    print("Loading vocabulary and initializing tokenizer...")
+    tokenizer = SupplyChainTokenizer(vocab_path="vocab.json")
+
+    # --- 2. (Optional) Train BPE ---
+    print("Training BPE tokenizer on training_data.txt...")
+    tokenizer.train_bpe("training_data.txt", vocab_size=50)  # Small vocab for example
+
+    # --- 3. Load Dummy Supply Chain Data ---
+    print("Loading dummy supply chain data...")
+    data = {
+        'timestamp': ['2024-07-03 10:00:00', '2024-07-03 11:00:00', '2024-07-03 12:00:00'],
+        'sku': ['SKU123', 'SKU123-RED', 'SKU123-BLUE'],
+        'store_id': ['STORE456', 'STORE456', 'STORE456'],
+        'quantity': [2, 1, 3],
+        'price': [10.99, 12.99, 9.99],
+        'discount': [0.0, 1.0, 0.5],
+        'promotion_id': ['BOGO', None, 'DISCOUNT'],
+        'product_category': ['Electronics', 'Electronics', 'Electronics']
+    }
+    df = pd.DataFrame(data)
+
+    # --- 4. Tokenize the Data ---
+    print("Tokenizing supply chain data...")
+    input_ids, attention_masks = tokenizer.prepare_for_model(df)
+    print("Tokenized Input IDs (first example):", input_ids[0])
+    print("Attention Mask (first example):", attention_masks[0])
+
+    # --- 5. Initialize Dummy Transformer Model ---
+    print("Initializing dummy Transformer model...")
+    vocab_size = tokenizer.get_vocab_size()
+    dummy_model = TransformerModel(vocab_size=vocab_size)
+
+    # --- 6. Make Predictions with Dummy Model ---
+    print("Making predictions with dummy Transformer model...")
+    forecasts = dummy_model.predict(input_ids, attention_masks)
+
+    # --- 7. Output Forecasts (Placeholder Output) ---
+    print("\n--- Placeholder Forecast Outputs ---")
+    for i in range(len(df)):
+        print(f"Data Row {i+1}:")
+        print(df.iloc[i]) # Print the original data row
+        print(f"  Placeholder Forecasts: {forecasts[i].tolist()}") # Print dummy forecasts
+        print("-" * 30)
+
+    print("\n--- Script Completed ---")
+
+    # --- (Optional) Clean up example files (comment out if you want to keep them) ---
+    # os.remove("vocab.json")
+    # os.remove("training_data.txt")