model

Working Models/Linear_trained.py

@@ -51,14 +51,11 @@ def convert_time_period(value):
 
 
 class PiecewiseLinearPredictor:
-    """A simplified version of the model that only does predictions"""
-
     def __init__(self, model_params):
         self.segments = model_params["segments"]
         self.feature_count = model_params["feature_count"]
 
     def predict(self, X, segment_id):
-        """Predict using the specified segment"""
         segment = self.segments.get(str(segment_id))
         if segment is None:
             # If segment not found, use the first available segment
@@ -78,7 +75,6 @@ class PiecewiseLinearPredictor:
 
 
 def preprocess_data(data, target_col, feature_names):
-    """Preprocess the data similar to the training process"""
     if target_col in data.columns:
         X = data.drop(target_col, axis=1)
         y = data[target_col]
@@ -132,7 +128,6 @@ def preprocess_data(data, target_col, feature_names):
 
 
 def apply_scaler(X, scaler_params):
-    """Apply scaling using the parameters from training"""
     mean = np.array(scaler_params["mean"])
     scale = np.array(scaler_params["scale"])
 
@@ -142,8 +137,6 @@ def apply_scaler(X, scaler_params):
 
 
 def find_segment(X_scaled, all_segments):
-    """Find the most appropriate segment for each data point
-    This is a simplified approach since we don't have the tree model"""
     # For simplicity, we'll use the first segment
     # In a real implementation, you would use the tree model to determine the correct segment
     segment_id = list(all_segments.keys())[0]
@@ -159,19 +152,18 @@ def main():
     sys.stderr = null_file
 
     try:
-        # Load the data using absolute path
         file_path = f"{sys.argv[2]}"
         data = pd.read_csv(file_path)
 
-        # Hard-coded target column
         target_col = f"{sys.argv[1]}"
 
         # Load the model parameters
         input_dir = os.path.dirname(file_path) if os.path.dirname(file_path) else "."
         input_filename = os.path.basename(file_path)
         input_name = os.path.splitext(input_filename)[0]
+        script_dir = os.path.dirname(os.path.abspath(__file__))
 
-        model_path = os.path.join(input_dir, f"{input_name}_model.json")
+        model_path = os.path.join(script_dir, "Piecewise_Trained.json")
         with open(model_path, 'r') as f:
             model_data = json.load(f)
 
@@ -210,7 +202,6 @@ def main():
         # Restore original stderr before printing
         sys.stderr = original_stderr
 
-        print(f"Predictions saved to: {csv_output_path}")
 
     finally:
         # Restore original stderr and close the null file