Upload SyntheticModelDriver.py

SyntheticModelDriver.py

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+import math
+from typing import Tuple, Union
+from huggingface_hub import hf_hub_download
+import torch
+import pickle
+import pandas as pd
+import importlib.util
+import sys
+
+
+'''
+This class must do two things
+1) The constructor must load the model
+2) This class must implement a method called `run_inference` that takes the input data and returns a tuple
+    of float, str representing the predicted sale price and the predicted sale date.
+'''
+class SyntheticModelDriver:
+
+
+    def __init__(self):
+        print(f"Loading model...")
+        self.model, self.scaler, self.label_encoders, self.min_date = self.load_model()
+        print(f"Model loaded.")
+
+
+    def run_inference(self, input_data: dict[str, Union[str, int, float]]) -> Tuple[float, str]:
+
+        categorical_columns = ['city', 'state']
+
+        # Encode categorical variables
+        for col in categorical_columns:
+            input_data[col] = self.label_encoders[col].transform([input_data[col]])[0]
+
+        # Normalize numerical variables
+        numerical_values = [[
+            input_data['price'],
+            input_data['beds'],
+            input_data['baths'],
+            input_data['sqft'],
+            input_data['lot_size'],
+            input_data['year_built'],
+            input_data['days_on_market'],
+            input_data['latitude'],
+            input_data['longitude'],
+            input_data['hoa_dues'],
+            input_data['property_type'],
+        ]]
+        numerical_values = self.scaler.transform(numerical_values)
+
+        # Convert dates to days since reference
+        if 'last_sale_date' in input_data and input_data['last_sale_date'] != None:
+            last_sale_date = pd.to_datetime(input_data['last_sale_date']).tz_localize(None)
+            input_data['last_sale_date'] = (last_sale_date - self.min_date).days
+
+        # Convert to tensors
+        x_cat = torch.tensor([[input_data[col] for col in categorical_columns]], dtype=torch.long)
+        x_num = torch.tensor(numerical_values, dtype=torch.float32)
+
+        # Model inference
+        with torch.no_grad():
+            price_pred, date_pred = self.model(x_cat, x_num)
+            predicted_sale_price = price_pred.item()
+            predicted_sale_date = torch.argmax(date_pred, dim=1).item()
+
+        # Convert sale_date back to a readable date
+        predicted_sale_date = (self.min_date + pd.Timedelta(days=predicted_sale_date)).strftime('%Y-%m-%d')
+
+        if math.isnan(predicted_sale_price): predicted_sale_price = 1.0
+
+        print(f"Predicted Sale Price: ${predicted_sale_price:,.2f}")
+        print(f"Predicted Sale Date: {predicted_sale_date}")
+        return predicted_sale_price, predicted_sale_date
+
+
+    def load_model(self):
+        model_file, scaler_file, label_encoders_file, min_date_file, model_class_file = self._download_model_files()
+
+        self._import_model_class(model_class_file)
+
+        # Load the model
+        model = torch.load(model_file, weights_only=False)
+        model.eval()
+
+        # Load additional artifacts
+        with open(scaler_file, 'rb') as f:
+            scaler = pickle.load(f)
+
+        with open(label_encoders_file, 'rb') as f:
+            label_encoders = pickle.load(f)
+
+        with open(min_date_file, 'rb') as f:
+            min_date = pickle.load(f)
+
+        return model, scaler, label_encoders, min_date
+
+
+    def _download_model_files(self):
+        model_path = "fivedollarwitch/synthetic-np"
+
+        # Download the model files from the Hugging Face Hub
+        model_file = hf_hub_download(repo_id=model_path, filename="model.pt")
+        scaler_file = hf_hub_download(repo_id=model_path, filename="scaler.pkl")
+        label_encoders_file = hf_hub_download(repo_id=model_path, filename="label_encoders.pkl")
+        min_date_file = hf_hub_download(repo_id=model_path, filename="min_date.pkl")
+        model_class_file = hf_hub_download(repo_id=model_path, filename="SyntheticModel.py")
+
+        # Load the model and artifacts
+        return model_file, scaler_file, label_encoders_file, min_date_file, model_class_file
+
+    def _import_model_class(self, model_class_file):
+        # Reference docs here: https://docs.python.org/3/library/importlib.html#importlib.util.spec_from_loader
+        module_name = "SyntheticModel"
+        spec = importlib.util.spec_from_file_location(module_name, model_class_file)
+        model_module = importlib.util.module_from_spec(spec)
+        sys.modules[module_name] = model_module
+        spec.loader.exec_module(model_module)