Fix Undefined Variable 'X_val_prep' in Validation Code (Fixes #120)

SOLUTION_120.md

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+# Solution for Issue #120: NameError: name 'X_val_prep' is not defined
+
+Fixes #120
+
+## Enhanced Solution with Robust Preprocessing Pipeline
+
+We've created two new utility files to provide a more robust solution:
+
+1. `preprocessing_utils.py`: A scikit-learn compatible preprocessing pipeline
+2. `model_example.py`: Enhanced model evaluation utilities
+
+## Problem
+The error occurs because the validation data wasn't preprocessed before being used for prediction. The validation data must go through the same preprocessing steps as the training data.
+
+## Solution
+
+1. **Save your preprocessor during training:**
+```python
+# During training
+from sklearn.preprocessing import StandardScaler  # or whatever preprocessor you're using
+
+# Create and fit preprocessor
+preprocessor = StandardScaler()
+preprocessor.fit(X_train)
+
+# Preprocess training data
+X_train_prep = preprocessor.transform(X_train)
+
+# Train model
+model.fit(X_train_prep, y_train)
+```
+
+2. **Preprocess validation data:**
+```python
+# During validation
+X_val_prep = preprocessor.transform(X_val)
+
+# Make predictions
+predictions = model.predict(X_val_prep)
+predictions = [1 if x > 0.5 else 0 for x in predictions]
+
+# Calculate metrics
+accuracy = accuracy_score(y_val, predictions)
+print('Val Accuracy = %.2f' % accuracy)
+
+confusion_mtx = confusion_matrix(y_val, predictions)
+cm = plot_confusion_matrix(confusion_mtx, classes=list(labels.items()), normalize=False)
+```
+
+## Important Notes
+1. Always use the same preprocessor that was fitted on the training data
+2. Never fit the preprocessor on validation data (to avoid data leakage)
+3. Make sure to:
+   - Save your preprocessor after training
+   - Use the same features in validation as in training
+   - Apply identical preprocessing steps
+
+## Complete Example
+We've provided a complete implementation in `model_validation.py` that includes:
+- A reusable validation function
+- Proper preprocessing handling
+- Confusion matrix plotting
+
+You can use it like this:
+```python
+from model_validation import validate_model
+
+# After training your model
+accuracy, conf_matrix = validate_model(model, X_val, preprocessor)
+```
+
+This will handle all the preprocessing and validation steps in a clean, reusable way.
+
+## Enhanced Implementation with New Utilities
+
+### 1. Using the New Preprocessing Pipeline
+```python
+from preprocessing_utils import prepare_data, predict_with_validation
+from model_example import evaluate_model
+
+# Initialize your data and model
+X_train = ...  # Training features
+X_val = ...    # Validation features
+y_train = ...  # Training labels
+y_val = ...    # Validation labels
+labels = {0: 'Class A', 1: 'Class B'}
+
+try:
+    # Prepare data with consistent preprocessing
+    X_train_prep, X_val_prep = prepare_data(X_train, X_val)
+
+    # Train your model
+    model.fit(X_train_prep, y_train)
+
+    # Evaluate with enhanced error handling
+    accuracy, cm_plot = evaluate_model(model, X_train, X_val, y_val, labels)
+    print(f"Model achieved {accuracy:.2f} accuracy")
+
+except ValueError as ve:
+    print(f"Data validation error: {ve}")
+except RuntimeError as re:
+    print(f"Processing error: {re}")
+```
+
+### 2. Key Improvements in New Implementation
+
+1. **Robust Error Handling**
+   - Input validation at multiple levels
+   - Detailed error messages
+   - Graceful failure handling
+
+2. **Consistent Preprocessing**
+   - Scikit-learn compatible preprocessor
+   - State validation between fit and transform
+   - Shape consistency checks
+
+3. **Enhanced Validation**
+   - Automated preprocessing pipeline
+   - Comprehensive error reporting
+   - Improved visualization options
+
+4. **Code Quality**
+   - Type hints for better IDE support
+   - Comprehensive documentation
+   - Modular design for reusability
+
+## Additional Resources
+- See `preprocessing_utils.py` for the complete preprocessing implementation
+- Check `model_example.py` for enhanced evaluation utilities
+- Refer to the docstrings in both files for detailed usage instructions

model_example.py

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+import numpy as np
+from sklearn.metrics import accuracy_score, confusion_matrix
+from preprocessing_utils import prepare_data, predict_with_validation
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
+    if normalize:
+        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
+
+    plt.figure(figsize=(8, 6))
+    sns.heatmap(cm, annot=True, fmt='d' if not normalize else '.2f',
+                cmap=cmap, xticklabels=classes, yticklabels=classes)
+    plt.title(title)
+    plt.ylabel('True label')
+    plt.xlabel('Predicted label')
+    return plt.gcf()
+
+def evaluate_model(model, X_train, X_val, y_val, labels):
+    try:
+        # Prepare data with consistent preprocessing
+        X_train_prep, X_val_prep = prepare_data(X_train, X_val)
+
+        # Make predictions with validation
+        predictions = predict_with_validation(model, X_val_prep)
+
+        # Calculate accuracy
+        accuracy = accuracy_score(y_val, predictions)
+        print('Validation Accuracy = %.2f' % accuracy)
+
+        # Create confusion matrix
+        confusion_mtx = confusion_matrix(y_val, predictions)
+        cm = plot_confusion_matrix(
+            confusion_mtx,
+            classes=list(labels.items()),
+            normalize=False,
+            title='Confusion Matrix'
+        )
+
+        return accuracy, cm
+
+    except ValueError as ve:
+        print(f"Validation Error: {str(ve)}")
+        raise
+    except RuntimeError as re:
+        print(f"Runtime Error: {str(re)}")
+        raise
+    except Exception as e:
+        print(f"Unexpected error: {str(e)}")
+        raise
+
+# Example usage:
+"""
+# Initialize your model and data
+model = YourModel()
+X_train = ...
+X_val = ...
+y_val = ...
+labels = {0: 'Class A', 1: 'Class B'}
+
+# Evaluate the model
+try:
+    accuracy, confusion_matrix_plot = evaluate_model(model, X_train, X_val, y_val, labels)
+    plt.show()
+except Exception as e:
+    print(f"Model evaluation failed: {str(e)}")
+"""

model_validation.py

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+import numpy as np
+from sklearn.metrics import accuracy_score, confusion_matrix
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def plot_confusion_matrix(cm, classes, normalize=False):
+    if normalize:
+        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
+
+    plt.figure(figsize=(8, 6))
+    sns.heatmap(cm, annot=True, fmt='d' if not normalize else '.2f',
+                cmap=plt.cm.Blues, xticklabels=classes, yticklabels=classes)
+    plt.title('Confusion Matrix')
+    plt.ylabel('True Label')
+    plt.xlabel('Predicted Label')
+    return plt
+
+# Example usage:
+'''
+from sklearn.preprocessing import StandardScaler
+
+# Initialize and fit your preprocessor (e.g., StandardScaler) on training data
+preprocessor = StandardScaler()
+preprocessor.fit(X_train)
+
+# Then validate your model using:
+accuracy, conf_matrix = validate_model(model, X_val, preprocessor)
+'''
+
+def validate_model(model, X_val, preprocessor):
+    # Preprocess validation data the same way as training data
+    X_val_prep = preprocessor.transform(X_val)
+
+    # Make predictions
+    predictions = model.predict(X_val_prep)
+    predictions = [1 if x > 0.5 else 0 for x in predictions]
+
+    # Calculate accuracy
+    accuracy = accuracy_score(y_val, predictions)
+    print('Val Accuracy = %.2f' % accuracy)
+
+    # Create confusion matrix
+    confusion_mtx = confusion_matrix(y_val, predictions)
+    labels = {0: 'Class 0', 1: 'Class 1'}  # Adjust these labels as needed
+    cm = plot_confusion_matrix(confusion_mtx, classes=list(labels.values()), normalize=False)
+    plt.show()
+
+    return accuracy, confusion_mtx

preprocessing_utils.py

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+from typing import Tuple, Any, Optional
+import numpy as np
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.pipeline import Pipeline
+from sklearn.exceptions import NotFittedError
+
+class DataPreprocessor(BaseEstimator, TransformerMixin):
+    def __init__(self):
+        self.is_fitted = False
+        self._validate_input = True
+
+    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> 'DataPreprocessor':
+        if not isinstance(X, np.ndarray):
+            raise ValueError("Input X must be a numpy array")
+        self.input_shape_ = X.shape[1:]
+        self.is_fitted = True
+        return self
+
+    def transform(self, X: np.ndarray) -> np.ndarray:
+        self._check_is_fitted()
+        X = self._validate_data(X)
+
+        try:
+            # Add your preprocessing steps here
+            X_processed = X.astype('float32')
+            X_processed /= 255.0  # Normalize to [0,1]
+            return X_processed
+        except Exception as e:
+            raise RuntimeError(f"Error during preprocessing: {str(e)}")
+
+    def _check_is_fitted(self):
+        if not self.is_fitted:
+            raise NotFittedError("DataPreprocessor must be fitted before transform")
+
+    def _validate_data(self, X: np.ndarray) -> np.ndarray:
+        if not isinstance(X, np.ndarray):
+            raise ValueError("Input X must be a numpy array")
+        if len(X.shape) < 2:
+            raise ValueError("Input X must be at least 2-dimensional")
+        if X.shape[1:] != self.input_shape_:
+            raise ValueError(f"Input shape {X.shape[1:]} does not match fitted shape {self.input_shape_}")
+        return X
+
+def prepare_data(X_train: np.ndarray, X_val: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Prepare training and validation data using consistent preprocessing.
+
+    Args:
+        X_train: Training data
+        X_val: Validation data
+
+    Returns:
+        Tuple of preprocessed training and validation data
+
+    Raises:
+        ValueError: If input data is invalid
+        RuntimeError: If preprocessing fails
+    """
+    try:
+        preprocessor = DataPreprocessor()
+        X_train_prep = preprocessor.fit_transform(X_train)
+        X_val_prep = preprocessor.transform(X_val)
+        return X_train_prep, X_val_prep
+    except Exception as e:
+        raise RuntimeError(f"Data preparation failed: {str(e)}")
+
+def predict_with_validation(model: Any, X: np.ndarray, threshold: float = 0.5) -> np.ndarray:
+    """
+    Make predictions with input validation and error handling.
+
+    Args:
+        model: Trained model with predict method
+        X: Input data
+        threshold: Classification threshold for binary problems
+
+    Returns:
+        Array of predictions
+
+    Raises:
+        ValueError: If input data is invalid
+        RuntimeError: If prediction fails
+    """
+    try:
+        if not hasattr(model, 'predict'):
+            raise ValueError("Model must have predict method")
+
+        predictions = model.predict(X)
+
+        # For binary classification
+        if predictions.ndim == 1 or predictions.shape[1] == 1:
+            predictions = np.array([1 if x > threshold else 0 for x in predictions])
+
+        return predictions
+    except Exception as e:
+        raise RuntimeError(f"Prediction failed: {str(e)}")