We have already encountered and used the Pima Indian Diabetes dataset a number of times in the classroom. Here is a nice function that will fetch the dataset and format it in the correct way for further processing:

    import numpy as np
    import pandas as pd

    def fetch_pid(url="https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"):
        names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
        dataframe = pd.read_csv(url, names=names)
        array = dataframe.values
        X = array[:,0:8]
        y = array[:,8]
        return((X, y))

    X, y = fetch_pid()

And here is an example of how we can build a simple Logistic Regression model with a 10-fold cross-validation, and then compute the f1 score:

    from sklearn import model_selection
    from sklearn.linear_model import LogisticRegression
    from sklearn import metrics

    def pid_lr(seed=42, scoring='f1'):
        kfold = model_selection.KFold(n_splits=10, random_state=seed)
        model = LogisticRegression()
        scoring = 'f1'
        results = model_selection.cross_val_score(model, X, y, cv=kfold, scoring=scoring)

        print("f1 score: {:.3f} {:.3f}".format(results.mean(), results.std()))
        return(1)

    pid_lr()

Your task is to write a function called training_report() that

1. Accepts the following parameters:
   • X, y (Numpy arrays for training; any format acceptable by sklearn will work)
   • seed (a number; a subsequent call to the function with the same seed will reproduce the same results)
   • KFold (the number of k-folds to be used in cross-validation)
   • classifier (a sklearn binary classifier)
2. Should return
   • A dataframe with 4 columns, 1 each for
     • f1
     • accuracy
     • neg_log_loss
     • roc_auc
   • The dataframe should have KFlod (which was provided as input) number of rows, containing the values of the respective metrics from cross-validated training
3. It should test the following requirements, and raise an exception otherwise:
   • seed can be coerced to a python integer
   • KFold can be coerced to a python integer

It is entirely possible to do this task by manually checking the types of input parameters, but this would be a good occassion to learn about Exceptions and see them in action. You can see how this is a useful trick in the final example of this section.

1. What are exception?
   • errors detected during execution are called exceptions
2. Read more about errors, exceptions, and handling them
3. When an exception happens, leading to an error, the type of exception can be from one of the built-in exception types. Some examples:
   • ZeroDivisionError: Raised when the second argument of a division or modulo operation is zero.
   • NameError: Raised when a local or global name is not found.
   • TypeError: Raised when an operation or function is applied to an object of inappropriate type.
   • ValueError: Raised when a built-in operation or function receives an argument that has the right type but an inappropriate value.
4. In the task above, we are concerned with ValueError

Here is an example of a function that accepts 2 parameters, a and b, and raises an exception when the following requirements are not met:

1. a is a python integer

2. b is a python integer

    def example(a, b):
        try:
            a = int(a)
        except ValueError:
            print("Please ensure the value of a is a Python integer.", "You entered: ", a)
   
        try:
            b = int(b)
        except ValueError:
            print("Please ensure the value of b is a Python integer.", "You entered: ", b)
   
        return(a+b)