The “IndexError” that you are facing especially “only integer, slices (:), ellipsis (...), numpy.newaxis (None) and integer or boolean arrays are valid indices” indicates that there is a problem with how indices are being applied in your program. More often than not, this error occurs when one tries to index a NumPy Array with a floating value or an object which is not compatible for indexing. Let’s discuss the main ways to resolve those issues.
Main Challenges
Repositional Indexing: In your code, there is a statement that uses a float index e.g. i = 0.0 and numericals index integer for indexing purposes.
Updating KFold Usage: The way you are doing KFold is a bit old. Sklearn nowadays insists on the number of splits where the former was n_folds and random state issues are more straightforward.
Suggested Fixes
Thus, in order to rectify these problems and have your code working, do the following:
Use Integer Indexing: Rather than starting i off with the value 0.0, initialize i as an integer. Also, use enumerate() in your loop to avoid using ugly indexing.
Update KFold Setup: It is advised that the latest version of the syntax for KFold is used.
Here’s a new edition of your code which should work:
from sklearn.model_selection import KFold # Updated import for KFold
# Updated KFold initialization
kf = KFold(n_splits=3, random_state=1, shuffle=True)
# Replace accuracy calculation loop with:
correct_predictions = np.sum(predictions == heart["heartpred"].values) # Corrected comparison
accuracy = correct_predictions / len(heart)
# Remove the old accuracy calculation loop
Summary of Key Changes
Integer Indexing: Changed i to an integer, and added enumeration for cleaner loop structure.
Updated KFold Syntax: Used n_splits instead of the older n_folds.
Shuffed Data: Also enabled shuffle=True to allow splits in randomized order which can assist in model training.
Accuracy Calculation: Implemented np.sum for taking into account the predictions made and the target column for better accuracy.
This should correct the mistake and enhance the performance of your code.
3) Python void return type annotation
The wording is odd, particularly when it comes to the functions that do not have a return parameter. While one could argue that None is the most appropriate option due to the general portrayal of both its essence and functionality, there are also other ways to show that a function doesn’t return anything.
Annotated Versions of Functions That Do Not Return a Value
def foo() -> None: I think this is the simplest and the most common way of annotating the function which returns nothing. As for defines with no return, in Python – None means absence of value hence -> None means very clearly that function has nothing to return.
Code
def foo() -> None:
print("This function does not return a value.")
def foo() -> type(None): While this works in practice, few will find it comfortable with the use of type(None). Most people would understand where using -> None is coming from rather than -> type(None) because None is the default already.
Code
def foo() -> type(None):
print("This function does not return a value.")
def foo(): (no annotation) Similarly, if there is no need to specify types in all places, there is no issue in going without the return type. However, if return type annotations are already used, it is typically clearer and more consistent to specify a return type of -> None for functions which return no value.
Code
def foo():
print("This function does not return a value.")
Conclusion
The recommended, most readable option is:
Code
def foo() -> None:
It is easy to employ the use of -> None, it is in line with the rules of python and it indicates to the user that the function does not return any value worth nothing. In addition, it helps the reader of the code to understand the code better, especially when tyaxe hints are being used within the whole codebase.
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