logging refactor, minor fixes

Author: SamoraHunter

ml_grid/model_classes/NeuralNetworkClassifier_class.py

@@ -7,8 +7,9 @@
 
 # from ml_grid.model_classes.nni_sklearn_wrapper import *
 from ml_grid.model_classes.nni_sklearn_wrapper import NeuralNetworkClassifier
+import logging
 
-print("Imported NeuralNetworkClassifier class")
+logging.getLogger('ml_grid').debug("Imported NeuralNetworkClassifier class")
 
 
 class NeuralNetworkClassifier_class:

ml_grid/model_classes/adaboost_classifier_class.py

@@ -5,8 +5,9 @@
 from ml_grid.util import param_space
 from ml_grid.util.global_params import global_parameters
 from skopt.space import Categorical, Real, Integer
+import logging
 
-print("Imported AdaBoostClassifier class")
+logging.getLogger('ml_grid').debug("Imported AdaBoostClassifier class")
 
 class adaboost_class:
     """AdaBoostClassifier with support for both Bayesian and non-Bayesian parameter spaces."""

ml_grid/model_classes/catboost_classifier_class.py

@@ -6,6 +6,7 @@
 from skopt.space import Categorical, Real, Integer
 from ml_grid.util import param_space
 from ml_grid.util.global_params import global_parameters
+import logging
 
 class CatBoost_class:
     """CatBoost Classifier with hyperparameter tuning."""
@@ -61,7 +62,7 @@ def __init__(
                 "verbose": Categorical([0]),
                 "allow_const_label": Categorical([True]),
             }
-            print(f"Bayesian Parameter Space: {self.parameter_space}")
+            logging.getLogger('ml_grid').debug(f"Bayesian Parameter Space for CatBoost: {self.parameter_space}")
         else:
             self.parameter_space = {
                 "iterations": [100, 200, 500, 1000],
@@ -85,6 +86,6 @@ def __init__(
                 "verbose": [0],
                 "allow_const_label": [True],
             }
-            print(f"Traditional Parameter Space: {self.parameter_space}")
+            logging.getLogger('ml_grid').debug(f"Traditional Parameter Space for CatBoost: {self.parameter_space}")
 
         return None

ml_grid/model_classes/gaussiannb_class.py

@@ -2,14 +2,15 @@
 
 from typing import Any, Dict, List, Optional
 
+import logging
 import numpy as np
 import pandas as pd
 from ml_grid.util import param_space
 from ml_grid.util.global_params import global_parameters
 from sklearn.naive_bayes import GaussianNB
 from skopt.space import Categorical
 
-print("Imported gaussiannb class")
+logging.getLogger('ml_grid').debug("Imported gaussiannb class")
 
 
 class GaussianNBWrapper(GaussianNB):
@@ -91,7 +92,7 @@ def __init__(
             }
 
             # Log parameter space for verification
-            print(f"Parameter Space: {self.parameter_space}")
+            logging.getLogger('ml_grid').debug(f"Parameter Space: {self.parameter_space}")
 
         else:
             # For traditional grid search, use lists

ml_grid/model_classes/h2o_classifier_class.py

@@ -6,8 +6,9 @@
 from ml_grid.util.global_params import global_parameters
 from skopt.space import Categorical, Real, Integer
 # from h2o.sklearn import H2OAutoMLClassifier
+import logging
 
-print("Imported h2o_classifier_class")
+logging.getLogger('ml_grid').debug("Imported h2o_classifier_class")
 
 class h2o_classifier_class:
     """H2OAutoMLClassifier with support for both Bayesian and non-Bayesian parameter spaces."""
@@ -29,7 +30,7 @@ def __init__(
                 optimization. Defaults to None.
         """
         global_params = global_parameters
-        print("init h2o_classifier_class")
+        logging.getLogger('ml_grid').debug("init h2o_classifier_class")
         self.X = X
         self.y = y
 

ml_grid/model_classes/knn_classifier_class.py

@@ -5,8 +5,9 @@
 from ml_grid.util.global_params import global_parameters
 from sklearn.neighbors import KNeighborsClassifier
 from skopt.space import Real, Integer, Categorical
+import logging
 
-print("Imported KNeighborsClassifier class")
+logging.getLogger('ml_grid').debug("Imported KNeighborsClassifier class")
 
 class knn_classifiers_class:
     """KNeighborsClassifier with support for both Bayesian and non-Bayesian parameter spaces."""

ml_grid/model_classes/knn_gpu_classifier_class.py

@@ -5,8 +5,9 @@
 from ml_grid.util import param_space
 from ml_grid.util.global_params import global_parameters
 from skopt.space import Categorical, Integer
+import logging
 
-print("Imported knn__gpu class")
+logging.getLogger('ml_grid').debug("Imported knn__gpu class")
 
 class knn__gpu_wrapper_class:
     """KNN with GPU support, including Bayesian and non-Bayesian parameter space."""

ml_grid/model_classes/knn_wrapper_class.py

@@ -6,6 +6,7 @@
 import torch
 from simbsig.neighbors import KNeighborsClassifier
 from sklearn import metrics
+import logging
 
 class KNNWrapper:
     """A scikit-learn compatible wrapper for the GPU-accelerated KNN from simbsig.
@@ -51,7 +52,7 @@ def __init__(
         # Auto-detect device
         gpu_available = torch.cuda.is_available()
         if device == "gpu" and not gpu_available:
-            print("Warning: GPU requested for KNNWrapper, but torch.cuda.is_available() is False. Falling back to CPU.")
+            logging.getLogger('ml_grid').warning("GPU requested for KNNWrapper, but torch.cuda.is_available() is False. Falling back to CPU.")
             self.device = "cpu"
         elif device:
             self.device = device

ml_grid/model_classes/light_gbm_class.py

@@ -8,6 +8,7 @@
 
 from ml_grid.model_classes.lightgbm_class import LightGBMClassifier
 from ml_grid.util.global_params import global_parameters
+import logging
 
 
 class LightGBMClassifierWrapper:
@@ -85,6 +86,5 @@ def __init__(
 
 
 
-
-print("Imported LightGBM classifier wrapper class")
+logging.getLogger('ml_grid').debug("Imported LightGBM classifier wrapper class")
 # light_gbm_class LightGBMClassifierWrapper

ml_grid/model_classes/lightgbm_class.py

@@ -89,20 +89,22 @@ def fit(
             # early_stopping_rounds=self.early_stopping_rounds,
             verbose=self.verbosity,
         )
-        # X.columns = X.columns.str.replace('[^a-zA-Z0-9_]', '', regex=True)
-        # Change columns names ([LightGBM] Do not support special JSON characters in feature name.)
-        new_names = {col: re.sub(r"[^A-Za-z0-9_]+", "", col) for col in X.columns}
-        new_n_list = list(new_names.values())
-        # [LightGBM] Feature appears more than one time.
-        new_names = {
-            col: f"{new_col}_{i}" if new_col in new_n_list[:i] else new_col
-            for i, (col, new_col) in enumerate(new_names.items())
-        }
-        X = X.rename(columns=new_names)
+        
+        X_fit = X
+        if isinstance(X, pd.DataFrame):
+            # Change columns names ([LightGBM] Do not support special JSON characters in feature name.)
+            new_names = {col: re.sub(r"[^A-Za-z0-9_]+", "", col) for col in X.columns}
+            new_n_list = list(new_names.values())
+            # [LightGBM] Feature appears more than one time.
+            new_names = {
+                col: f"{new_col}_{i}" if new_col in new_n_list[:i] else new_col
+                for i, (col, new_col) in enumerate(new_names.items())
+            }
+            X_fit = X.rename(columns=new_names)
 
         y = np.ravel(y)
 
-        self.model.fit(X, y)
+        self.model.fit(X_fit, y)
         if self.objective == "binary":
             self.classes_ = np.unique(y)
         return self
@@ -127,18 +129,20 @@ def predict(self, X: pd.DataFrame) -> np.ndarray:
                 "Model has not been fitted yet. Call 'fit' before 'predict'."
             )
 
-        # Change columns names ([LightGBM] Do not support special JSON characters in feature name.)
-        new_names = {
-            col: re.sub(r"[^A-Za-z0-9_]+", "", col) for col in X.columns
-        }
-        new_n_list = list(new_names.values())
-        # [LightGBM] Feature appears more than one time.
-        new_names = {
-            col: f"{new_col}_{i}" if new_col in new_n_list[:i] else new_col
-            for i, (col, new_col) in enumerate(new_names.items())
-        }
-        X = X.rename(columns=new_names)
-        return self.model.predict(X)
+        X_pred = X
+        if isinstance(X, pd.DataFrame):
+            # Change columns names ([LightGBM] Do not support special JSON characters in feature name.)
+            new_names = {
+                col: re.sub(r"[^A-Za-z0-9_]+", "", col) for col in X.columns
+            }
+            new_n_list = list(new_names.values())
+            # [LightGBM] Feature appears more than one time.
+            new_names = {
+                col: f"{new_col}_{i}" if new_col in new_n_list[:i] else new_col
+                for i, (col, new_col) in enumerate(new_names.items())
+            }
+            X_pred = X.rename(columns=new_names)
+        return self.model.predict(X_pred)
 
     def score(self, X: pd.DataFrame, y: Union[pd.Series, np.ndarray]) -> float:
         """Returns the mean accuracy on the given test data and labels.