diff --git a/Working Models/Linear_trained.py b/Working Models/Linear_trained.py
index 507d3c6ce0dd957e13bc7140fcdb71651f11e102..27f87c9bb8a8a956c7728e5427a860209ff0828c 100644
--- a/Working Models/Linear_trained.py
+++ b/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
diff --git a/Working Models/Piecewise_Trained.json b/Working Models/Piecewise_Trained.json
new file mode 100644
index 0000000000000000000000000000000000000000..8ba87ac711b161dfb793a2a02f60028d69f24eaf
--- /dev/null
+++ b/Working Models/Piecewise_Trained.json
@@ -0,0 +1,1037 @@
+{
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