human
Can you provide a Python code to compare the result with the baseline log loss given the following:
I am working on the file "ML_lab4_ctr_student.ipynb".
The first task is:
(4e) Evaluate the model 
We are now ready to evaluate the quality of the model we trained in Part (4a). To do this, first write a general function that takes as input a model and data, and outputs the log loss.  Then run this function on the OHE training data, and compare the result with the baseline log loss.
Can you write Python code for it?
assistant
#Here is Python code to compare the result with the baseline log loss.

# TODO: Replace <FILL IN> with appropriate code
def evaluateResults(model, data):
    """Calculates the log loss for the data given the model.

    Args:
        model (LogisticRegressionModel): A trained logistic regression model.
        data (RDD of LabeledPoint): Labels and features for each observation.

    Returns:
        float: Log loss for the data.
    """
    
    def evaluateResult(model, point):
    # Calculates the log loss for the individual data points given the model.
        prob = getP(point.features, model.weights, model.intercept)
        return computeLogLoss(prob, point.label)
    
    return data.map(lambda x: evaluateResult(model, x)).mean()

logLossTrLR0 = evaluateResults(model0, OHETrainData)
print ('OHE Features Train Logloss:\n\tBaseline = {0:.3f}\n\tLogReg = {1:.3f}'
       .format(logLossTrBase, logLossTrLR0))

# TEST Evaluate the model (4e)
Test.assertTrue(np.allclose(logLossTrLR0, 0.456903), 'incorrect value for logLossTrLR0')