import fixed: find the last occurence of views_pipeline

.github/workflows/check_if_new_model_added.yml

@@ -49,7 +49,7 @@ jobs:
           else
             echo "No new or removed folders detected."
           fi
-        
+          git status
 
       - name: Configure Git
         run: |

documentation/catalogs/generate_model_catalog.py

@@ -9,7 +9,8 @@
 from pathlib import Path
 
 PATH = Path(__file__).resolve()
-PATH_ROOT = PATH.parent.parent.parent
+indices = [i for i, x in enumerate(PATH.parts) if x == "views_pipeline"]
+PATH_ROOT = Path(*PATH.parts[:indices[-1] + 1])
 
 sys.path.insert(0, str(PATH_ROOT))
 sys.path.insert(0, str(PATH_ROOT/"common_utils"))

models/caring_fish/README.md

@@ -0,0 +1,3 @@
+# Model README
+## Model name: caring_fish
+## Created on: 2024-10-29 10:51:00.014047

models/caring_fish/configs/config_deployment.py

@@ -0,0 +1,20 @@
+"""
+Deployment Configuration Script
+
+This script defines the deployment configuration settings for the application. 
+It includes the deployment status and any additional settings specified.
+
+Deployment Status:
+- shadow: The deployment is shadowed and not yet active.
+- deployed: The deployment is active and in use.
+- baseline: The deployment is in a baseline state, for reference or comparison.
+- deprecated: The deployment is deprecated and no longer supported.
+
+Additional settings can be included in the configuration dictionary as needed.
+
+"""
+
+def get_deployment_config():
+    # Deployment settings
+    deployment_config = {'deployment_status': 'shadow'}
+    return deployment_config

models/caring_fish/configs/config_hyperparameters.py

@@ -0,0 +1,14 @@
+def get_hp_config():
+    """
+    Contains the hyperparameter configurations for model training.
+    This configuration is "operational" so modifying these settings will impact the model's behavior during the training.
+
+    Returns:
+    - hyperparameters (dict): A dictionary containing hyperparameters for training the model, which determine the model's behavior during the training phase.
+    """
+
+    hyperparameters = {
+        'model': 'XGBoost',  # The model algorithm used. Eg. "LSTM", "CNN", "Transformer"
+        # Add more hyperparameters as needed
+    }
+    return hyperparameters

models/caring_fish/configs/config_meta.py

@@ -0,0 +1,19 @@
+def get_meta_config():
+    """
+    Contains the meta data for the model (model algorithm, name, target variable, and level of analysis).
+    This config is for documentation purposes only, and modifying it will not affect the model, the training, or the evaluation.
+
+    Returns:
+    - meta_config (dict): A dictionary containing model meta configuration.
+    """
+
+    meta_config = {
+        "name": "caring_fish", # Eg. happy_kitten
+        "algorithm": "XGBoost", # Eg. "LSTM", "CNN", "Transformer"
+        # Uncomment and modify the following lines as needed for additional metadata:
+        # "target(S)": ["ln_sb_best", "ln_ns_best", "ln_os_best", "ln_sb_best_binarized", "ln_ns_best_binarized", "ln_os_best_binarized"],
+        "queryset": "escwa001_cflong"
+        # "level": "pgm",
+        # "creator": "Your name here"
+    }
+    return meta_config

models/caring_fish/configs/config_sweep.py

@@ -0,0 +1,29 @@
+def get_sweep_config():
+    """
+    Contains the configuration for hyperparameter sweeps using WandB.
+    This configuration is "operational" so modifying it will change the search strategy, parameter ranges, and other settings for hyperparameter tuning aimed at optimizing model performance.
+
+    Returns:
+    - sweep_config (dict): A dictionary containing the configuration for hyperparameter sweeps, defining the methods and parameter ranges used to search for optimal hyperparameters.
+    """
+
+    sweep_config = {
+        'method': 'grid',
+    }
+
+    # Example metric setup:
+    metric = {
+        'name': 'MSE',
+        'goal': 'minimize'
+    }
+    sweep_config['metric'] = metric
+
+    # Example parameters setup:
+    parameters_dict = {
+        'model': {
+            'value': 'XGBoost' # Eg. "LSTM", "CNN", "Transformer"
+        },
+    }
+    sweep_config['parameters'] = parameters_dict
+
+    return sweep_config

models/caring_fish/main.py

@@ -0,0 +1,39 @@
+import time
+import wandb
+import sys
+import logging
+logging.basicConfig(filename='run.log', encoding='utf-8', level=logging.INFO,
+                    format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+from pathlib import Path
+# Set up the path to include common_utils module
+PATH = Path(__file__)
+sys.path.insert(0, str(Path(
+    *[i for i in PATH.parts[:PATH.parts.index("views_pipeline") + 1]]) / "common_utils"))  # PATH_COMMON_UTILS
+# Import necessary functions for project setup and model execution
+from set_path import setup_project_paths
+setup_project_paths(PATH)
+from utils_cli_parser import parse_args, validate_arguments
+from execute_model_runs import execute_sweep_run, execute_single_run
+
+if __name__ == "__main__":
+    # Parse command-line arguments
+    args = parse_args()
+
+    # Validate the arguments to ensure they are correct
+    validate_arguments(args)
+    # Log in to Weights & Biases (wandb)
+    wandb.login()
+    # Record the start time
+    start_t = time.time()
+    # Execute the model run based on the sweep flag
+    if args.sweep:
+        execute_sweep_run(args)  # Execute sweep run
+    else:
+        execute_single_run(args)  # Execute single run
+    # Record the end time
+    end_t = time.time()
+
+    # Calculate and print the runtime in minutes
+    minutes = (end_t - start_t) / 60
+    logger.info(f'Done. Runtime: {minutes:.3f} minutes')

models/caring_fish/requirements.txt

@@ -0,0 +1 @@
+# Requirements