Exception Handling

In Machine Learning, things often go wrong: a dataset file is missing, a GPU runs out of memory, or a feature contains a NaN (Not a Number) that crashes a calculation. Exception Handling allows your program to "fail gracefully" rather than crashing completely.

1. The Try-Except Block

The basic tool for handling errors is the try...except block. You "try" a piece of code, and if it raises an error, the "except" block catches it.

try:
    # Attempting to load a large dataset
    data = load_dataset("huge_data.csv")
except FileNotFoundError:
    print("Error: The dataset file was not found. Please check the path.")

2. Handling Multiple Exceptions

Different operations can fail in different ways. You can catch specific errors to provide tailored solutions.

• ValueError: Raised when a function receives an argument of the right type but inappropriate value (e.g., trying to take the square root of a negative number).
• TypeError: Raised when an operation is applied to an object of inappropriate type.
• ZeroDivisionError: Common in manual normalization logic.

try:
    result = total_loss / num_samples
except ZeroDivisionError:
    result = 0
    print("Warning: num_samples was zero. Setting loss to 0.")
except TypeError:
    print("Error: Check if total_loss and num_samples are numbers.")

3. The Full Lifecycle: else and finally

To build truly robust pipelines (like those that open and close database connections), we use the extended syntax:

1. try: The code that might fail.
2. except: Code that runs only if an error occurs.
3. else: Code that runs only if no error occurs.
4. finally: Code that runs no matter what (perfect for closing files or releasing GPU memory).

try:
    file = open("model_weights.bin", "rb")
    weights = file.read()
except IOError:
    print("Could not read file.")
else:
    print("Weights loaded successfully.")
finally:
    file.close()
    print("File resource released.")

4. Raising Exceptions

Sometimes, you want to stop the program if a specific condition isn't met. For example, if a user provides a negative learning rate.

def set_learning_rate(lr):
    if lr <= 0:
        raise ValueError(f"Learning rate must be positive. Received: {lr}")
    return lr

5. Exceptions in ML Data Pipelines

In production ML, we use exceptions to ensure data quality.

6. Summary of Common ML Exceptions

Exception	When it happens in ML
IndexError	Trying to access a non-existent column or row index in an array.
KeyError	Looking for a hyperparameter in a config dictionary that doesn't exist.
AttributeError	Calling a method (like .predict()) on a model that hasn't been trained yet.
MemoryError	Loading a dataset that is larger than the available RAM.

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Handling errors ensures your code doesn't crash, but how do we organize our code so it's easy to read and maintain? Let's explore the world of Classes and Objects.