Issue #1 Spelling mistake in class name ClassficationMIEstimator to ClassificationMIEstimator

Author: prithagupta

README.md

@@ -60,7 +60,7 @@ You can find similar example code snippets in
 ```python
 from sklearn.metrics import accuracy_score
 from autoqild.dataset_readers.synthetic_data_generator import SyntheticDatasetGenerator
-from autoqild.mi_estimators.mi_estimator_classification import ClassficationMIEstimator
+from autoqild.mi_estimators.mi_estimator_classification import ClassificationMIEstimator
 from autoqild.utilities._constants import LOG_LOSS_MI_ESTIMATION, MID_POINT_MI_ESTIMATION
 
 # Step 1: Generate a Synthetic Dataset
@@ -83,7 +83,7 @@ X, y = dataset_generator.generate_dataset()
 print(f"Generated dataset X shape: {X.shape}, y shape: {y.shape}")
 
 # Step 2: Estimate Mutual Information using ClassficationMIEstimator
-mi_estimator = ClassficationMIEstimator(n_classes=n_classes, n_features=n_features, random_state=random_state)
+mi_estimator = ClassificationMIEstimator(n_classes=n_classes, n_features=n_features, random_state=random_state)
 
 # Fit the estimator on the synthetic dataset
 mi_estimator.fit(X, y)

autoqild/mi_estimators/__init__.py

@@ -7,7 +7,7 @@ class labels using different modeling techniques.
 
 from .auto_gluon_estimator import AutoMIGluonEstimator
 from .gmm_mi_estimator import GMMMIEstimator
-from .mi_estimator_classification import ClassficationMIEstimator
+from .mi_estimator_classification import ClassificationMIEstimator
 from .mine_estimator import MineMIEstimator
 from .mine_estimator_mse import MineMIEstimatorMSE
 from .pc_softmax_estimator import PCSoftmaxMIEstimator

autoqild/mi_estimators/auto_gluon_estimator.py

@@ -2,11 +2,11 @@
 (AutoML) to estimate MI with optimized hyperparameters."""
 
 from autoqild.automl import AutoGluonClassifier
-from autoqild.mi_estimators.mi_estimator_classification import ClassficationMIEstimator
+from autoqild.mi_estimators.mi_estimator_classification import ClassificationMIEstimator
 from autoqild.utilities._constants import LOG_LOSS_MI_ESTIMATION
 
 
-class AutoMIGluonEstimator(ClassficationMIEstimator):
+class AutoMIGluonEstimator(ClassificationMIEstimator):
     """AutoMIGluonEstimator integrates the AutoGluon framework into the Mutual
     Information (MI) estimation process for classification tasks. This class
     extends the `ClassficationMIEstimator` by using AutoGluon as the base

autoqild/mi_estimators/mi_estimator_classification.py

@@ -18,7 +18,7 @@
 from autoqild.utilities import *
 
 
-class ClassficationMIEstimator(MIEstimatorBase):
+class ClassificationMIEstimator(MIEstimatorBase):
     """Class to estimate Mutual Information (MI) using a classification model.
 
     This class leverages a classification model, such as `RandomForestClassifier`, to estimate the Mutual Information
@@ -85,7 +85,7 @@ def __init__(
     ):
         super().__init__(n_classes, n_features, random_state)
         self.random_state = check_random_state(random_state)
-        self.logger = logging.getLogger(ClassficationMIEstimator.__name__)
+        self.logger = logging.getLogger(ClassificationMIEstimator.__name__)
         self.base_estimator = base_estimator
         self.learner_params = learner_params
         self.base_learner = self.base_estimator(**self.learner_params)

autoqild/mi_estimators/tab_pfn_estimator.py

@@ -2,11 +2,11 @@
 datasets with efficient MI estimation."""
 
 from autoqild.automl import AutoTabPFNClassifier
-from autoqild.mi_estimators.mi_estimator_classification import ClassficationMIEstimator
+from autoqild.mi_estimators.mi_estimator_classification import ClassificationMIEstimator
 from autoqild.utilities._constants import LOG_LOSS_MI_ESTIMATION
 
 
-class TabPFNMIEstimator(ClassficationMIEstimator):
+class TabPFNMIEstimator(ClassificationMIEstimator):
     """TabPFNMIEstimator integrates the TabPFN framework into the Mutual
     Information (MI) estimation process for classification tasks.
 

docs/source/start.rst

@@ -16,7 +16,7 @@ You can find similar example code snippets in
 
     from sklearn.metrics import accuracy_score
     from autoqild.dataset_readers.synthetic_data_generator import SyntheticDatasetGenerator
-    from autoqild.mi_estimators.mi_estimator_classification import ClassficationMIEstimator
+    from autoqild.mi_estimators.mi_estimator_classification import ClassificationMIEstimator
     from autoqild.utilities.constants import LOG_LOSS_MI_ESTIMATION, MID_POINT_MI_ESTIMATION
 
     # Step 1: Generate a Synthetic Dataset