import math

class Evaluator:
    """A class containing loss functions and percent accuracy function to
    evaluate performance of our model"""

    def __init__(self, classification_type):
        """Initializes the evaluator object with the classification type and performance attributes"""
        self.classification_type = classification_type
        self.performance = 0
        self.num_performances = 0


    def evaluate(self, test_set, predicted_values, certainty):
        """Runs different loss functions depending on if it is classifying or regressing"""

        if self.classification_type == "classification":
            self.classification_evaluation(test_set, predicted_values, certainty)
        elif self.classification_type == "regression":
            self.regression_evaluation(test_set, predicted_values)

    def classification_evaluation(self, test_set, predicted_values, certainty):
        """Runs all the classification log functions and prints out their results"""

        percent_accuracy = self.percent_accuracy(test_set, predicted_values)
        one_zero = self.one_zero_loss(test_set, predicted_values)
        log_loss = self.log_loss(test_set, predicted_values, certainty)
        print(f"Percent correct:\t{percent_accuracy * 100:.2f}%")
        print(f"1/0 Loss:\t\t\t{one_zero:.2f}")
        print("Log Loss: ", log_loss)

    def regression_evaluation(self, test_set, predicted_values):
        """Runs all of the regression loss functions and prints out their results"""

        MAE = self.mean_absolute_error(test_set, predicted_values)
        MSE = self.mean_square_error(test_set, predicted_values)
        print(f"Mean Percent Error:\t{MAE:.2f}")
        print(f"Mean Square Error:\t{MSE:.2f}")

    def percent_accuracy(self, test_set, predicted_values):
        """Returns percent accuracy given true and predicted values"""

        correct = 0
        for i in range(len(test_set)):
            if test_set[i].classification == predicted_values[i]:
                correct += 1
        return correct / len(test_set)

    # classification loss functions
    def one_zero_loss(self, test_set, predicted_values):
        """Returns one-zero loss score given true and predicted values"""

        incorrect=0
        for i in range(len(test_set)):
            if test_set[i].classification != predicted_values[i]:
                incorrect += 1
        self.performance += incorrect / len(test_set)
        self.num_performances += 1
        return incorrect / len(test_set)

    def log_loss(self, test_set, predicted_values, certainty):
        """Basic function that calculates log loss of a test set using a specific certainty value"""

        total = 0;
        for i in range(len(test_set)):
            if test_set[i].classification == predicted_values[i]:
                total += math.log(certainty[i])
            if test_set[i].classification != predicted_values[i]:
                if certainty[i] > .95:
                    certainty[i] = .95
                total += math.log(1-certainty[i])

        log_loss = -1*total/len(test_set)
        return log_loss

    # regression loss functions
    def mean_absolute_error(self, test_set, predicted_values):
        """Returns the mean absolute error for a given test set and their predicted values"""

        running_sum = 0
        for i in range(len(test_set)):
            running_sum += abs(test_set[i].classification - predicted_values[i])

        running_sum = running_sum / len(test_set)
        self.performance += running_sum
        self.num_performances += 1
        return running_sum

    def mean_square_error(self, test_set, predicted_values):
        """Returns the mean square error for a given test set and their predicted values"""

        running_sum = 0
        for i in range(len(test_set)):
            running_sum += (test_set[i].classification - predicted_values[i])**2
        running_sum = running_sum / len(test_set)
        return running_sum
f1d77fd611b4e45da776e92e8b6d137dfe8b1074
<mask token>


class Evaluator:
    <mask token>

    def __init__(self, classification_type):
        """Initializes the evaluator object with the classification type and performance attributes"""
        self.classification_type = classification_type
        self.performance = 0
        self.num_performances = 0

    def evaluate(self, test_set, predicted_values, certainty):
        """Runs different loss functions depending on if it is classifying or regressing"""
        if self.classification_type == 'classification':
            self.classification_evaluation(test_set, predicted_values,
                certainty)
        elif self.classification_type == 'regression':
            self.regression_evaluation(test_set, predicted_values)
    <mask token>
    <mask token>

    def percent_accuracy(self, test_set, predicted_values):
        """Returns percent accuracy given true and predicted values"""
        correct = 0
        for i in range(len(test_set)):
            if test_set[i].classification == predicted_values[i]:
                correct += 1
        return correct / len(test_set)
    <mask token>
    <mask token>
    <mask token>

    def mean_square_error(self, test_set, predicted_values):
        """Returns the mean square error for a given test set and their predicted values"""
        running_sum = 0
        for i in range(len(test_set)):
            running_sum += (test_set[i].classification - predicted_values[i]
                ) ** 2
        running_sum = running_sum / len(test_set)
        return running_sum

<mask token>


class Evaluator:
    <mask token>

    def __init__(self, classification_type):
        """Initializes the evaluator object with the classification type and performance attributes"""
        self.classification_type = classification_type
        self.performance = 0
        self.num_performances = 0

    def evaluate(self, test_set, predicted_values, certainty):
        """Runs different loss functions depending on if it is classifying or regressing"""
        if self.classification_type == 'classification':
            self.classification_evaluation(test_set, predicted_values,
                certainty)
        elif self.classification_type == 'regression':
            self.regression_evaluation(test_set, predicted_values)
    <mask token>

    def regression_evaluation(self, test_set, predicted_values):
        """Runs all of the regression loss functions and prints out their results"""
        MAE = self.mean_absolute_error(test_set, predicted_values)
        MSE = self.mean_square_error(test_set, predicted_values)
        print(f'Mean Percent Error:\t{MAE:.2f}')
        print(f'Mean Square Error:\t{MSE:.2f}')

    def percent_accuracy(self, test_set, predicted_values):
        """Returns percent accuracy given true and predicted values"""
        correct = 0
        for i in range(len(test_set)):
            if test_set[i].classification == predicted_values[i]:
                correct += 1
        return correct / len(test_set)

    def one_zero_loss(self, test_set, predicted_values):
        """Returns one-zero loss score given true and predicted values"""
        incorrect = 0
        for i in range(len(test_set)):
            if test_set[i].classification != predicted_values[i]:
                incorrect += 1
        self.performance += incorrect / len(test_set)
        self.num_performances += 1
        return incorrect / len(test_set)

    def log_loss(self, test_set, predicted_values, certainty):
        """Basic function that calculates log loss of a test set using a specific certainty value"""
        total = 0
        for i in range(len(test_set)):
            if test_set[i].classification == predicted_values[i]:
                total += math.log(certainty[i])
            if test_set[i].classification != predicted_values[i]:
                if certainty[i] > 0.95:
                    certainty[i] = 0.95
                total += math.log(1 - certainty[i])
        log_loss = -1 * total / len(test_set)
        return log_loss
    <mask token>

    def mean_square_error(self, test_set, predicted_values):
        """Returns the mean square error for a given test set and their predicted values"""
        running_sum = 0
        for i in range(len(test_set)):
            running_sum += (test_set[i].classification - predicted_values[i]
                ) ** 2
        running_sum = running_sum / len(test_set)
        return running_sum

<mask token>


class Evaluator:
    <mask token>

    def __init__(self, classification_type):
        """Initializes the evaluator object with the classification type and performance attributes"""
        self.classification_type = classification_type
        self.performance = 0
        self.num_performances = 0

    def evaluate(self, test_set, predicted_values, certainty):
        """Runs different loss functions depending on if it is classifying or regressing"""
        if self.classification_type == 'classification':
            self.classification_evaluation(test_set, predicted_values,
                certainty)
        elif self.classification_type == 'regression':
            self.regression_evaluation(test_set, predicted_values)
    <mask token>

    def regression_evaluation(self, test_set, predicted_values):
        """Runs all of the regression loss functions and prints out their results"""
        MAE = self.mean_absolute_error(test_set, predicted_values)
        MSE = self.mean_square_error(test_set, predicted_values)
        print(f'Mean Percent Error:\t{MAE:.2f}')
        print(f'Mean Square Error:\t{MSE:.2f}')

    def percent_accuracy(self, test_set, predicted_values):
        """Returns percent accuracy given true and predicted values"""
        correct = 0
        for i in range(len(test_set)):
            if test_set[i].classification == predicted_values[i]:
                correct += 1
        return correct / len(test_set)

    def one_zero_loss(self, test_set, predicted_values):
        """Returns one-zero loss score given true and predicted values"""
        incorrect = 0
        for i in range(len(test_set)):
            if test_set[i].classification != predicted_values[i]:
                incorrect += 1
        self.performance += incorrect / len(test_set)
        self.num_performances += 1
        return incorrect / len(test_set)

    def log_loss(self, test_set, predicted_values, certainty):
        """Basic function that calculates log loss of a test set using a specific certainty value"""
        total = 0
        for i in range(len(test_set)):
            if test_set[i].classification == predicted_values[i]:
                total += math.log(certainty[i])
            if test_set[i].classification != predicted_values[i]:
                if certainty[i] > 0.95:
                    certainty[i] = 0.95
                total += math.log(1 - certainty[i])
        log_loss = -1 * total / len(test_set)
        return log_loss

    def mean_absolute_error(self, test_set, predicted_values):
        """Returns the mean absolute error for a given test set and their predicted values"""
        running_sum = 0
        for i in range(len(test_set)):
            running_sum += abs(test_set[i].classification - predicted_values[i]
                )
        running_sum = running_sum / len(test_set)
        self.performance += running_sum
        self.num_performances += 1
        return running_sum

    def mean_square_error(self, test_set, predicted_values):
        """Returns the mean square error for a given test set and their predicted values"""
        running_sum = 0
        for i in range(len(test_set)):
            running_sum += (test_set[i].classification - predicted_values[i]
                ) ** 2
        running_sum = running_sum / len(test_set)
        return running_sum

<mask token>


class Evaluator:
    """A class containing loss functions and percent accuracy function to
    evaluate performance of our model"""

    def __init__(self, classification_type):
        """Initializes the evaluator object with the classification type and performance attributes"""
        self.classification_type = classification_type
        self.performance = 0
        self.num_performances = 0

    def evaluate(self, test_set, predicted_values, certainty):
        """Runs different loss functions depending on if it is classifying or regressing"""
        if self.classification_type == 'classification':
            self.classification_evaluation(test_set, predicted_values,
                certainty)
        elif self.classification_type == 'regression':
            self.regression_evaluation(test_set, predicted_values)

    def classification_evaluation(self, test_set, predicted_values, certainty):
        """Runs all the classification log functions and prints out their results"""
        percent_accuracy = self.percent_accuracy(test_set, predicted_values)
        one_zero = self.one_zero_loss(test_set, predicted_values)
        log_loss = self.log_loss(test_set, predicted_values, certainty)
        print(f'Percent correct:\t{percent_accuracy * 100:.2f}%')
        print(f'1/0 Loss:\t\t\t{one_zero:.2f}')
        print('Log Loss: ', log_loss)

    def regression_evaluation(self, test_set, predicted_values):
        """Runs all of the regression loss functions and prints out their results"""
        MAE = self.mean_absolute_error(test_set, predicted_values)
        MSE = self.mean_square_error(test_set, predicted_values)
        print(f'Mean Percent Error:\t{MAE:.2f}')
        print(f'Mean Square Error:\t{MSE:.2f}')

    def percent_accuracy(self, test_set, predicted_values):
        """Returns percent accuracy given true and predicted values"""
        correct = 0
        for i in range(len(test_set)):
            if test_set[i].classification == predicted_values[i]:
                correct += 1
        return correct / len(test_set)

    def one_zero_loss(self, test_set, predicted_values):
        """Returns one-zero loss score given true and predicted values"""
        incorrect = 0
        for i in range(len(test_set)):
            if test_set[i].classification != predicted_values[i]:
                incorrect += 1
        self.performance += incorrect / len(test_set)
        self.num_performances += 1
        return incorrect / len(test_set)

    def log_loss(self, test_set, predicted_values, certainty):
        """Basic function that calculates log loss of a test set using a specific certainty value"""
        total = 0
        for i in range(len(test_set)):
            if test_set[i].classification == predicted_values[i]:
                total += math.log(certainty[i])
            if test_set[i].classification != predicted_values[i]:
                if certainty[i] > 0.95:
                    certainty[i] = 0.95
                total += math.log(1 - certainty[i])
        log_loss = -1 * total / len(test_set)
        return log_loss

    def mean_absolute_error(self, test_set, predicted_values):
        """Returns the mean absolute error for a given test set and their predicted values"""
        running_sum = 0
        for i in range(len(test_set)):
            running_sum += abs(test_set[i].classification - predicted_values[i]
                )
        running_sum = running_sum / len(test_set)
        self.performance += running_sum
        self.num_performances += 1
        return running_sum

    def mean_square_error(self, test_set, predicted_values):
        """Returns the mean square error for a given test set and their predicted values"""
        running_sum = 0
        for i in range(len(test_set)):
            running_sum += (test_set[i].classification - predicted_values[i]
                ) ** 2
        running_sum = running_sum / len(test_set)
        return running_sum

import math

class Evaluator:
    """A class containing loss functions and percent accuracy function to
    evaluate performance of our model"""

    def __init__(self, classification_type):
        """Initializes the evaluator object with the classification type and performance attributes"""
        self.classification_type = classification_type
        self.performance = 0
        self.num_performances = 0


    def evaluate(self, test_set, predicted_values, certainty):
        """Runs different loss functions depending on if it is classifying or regressing"""

        if self.classification_type == "classification":
            self.classification_evaluation(test_set, predicted_values, certainty)
        elif self.classification_type == "regression":
            self.regression_evaluation(test_set, predicted_values)

    def classification_evaluation(self, test_set, predicted_values, certainty):
        """Runs all the classification log functions and prints out their results"""

        percent_accuracy = self.percent_accuracy(test_set, predicted_values)
        one_zero = self.one_zero_loss(test_set, predicted_values)
        log_loss = self.log_loss(test_set, predicted_values, certainty)
        print(f"Percent correct:\t{percent_accuracy * 100:.2f}%")
        print(f"1/0 Loss:\t\t\t{one_zero:.2f}")
        print("Log Loss: ", log_loss)

    def regression_evaluation(self, test_set, predicted_values):
        """Runs all of the regression loss functions and prints out their results"""

        MAE = self.mean_absolute_error(test_set, predicted_values)
        MSE = self.mean_square_error(test_set, predicted_values)
        print(f"Mean Percent Error:\t{MAE:.2f}")
        print(f"Mean Square Error:\t{MSE:.2f}")

    def percent_accuracy(self, test_set, predicted_values):
        """Returns percent accuracy given true and predicted values"""

        correct = 0
        for i in range(len(test_set)):
            if test_set[i].classification == predicted_values[i]:
                correct += 1
        return correct / len(test_set)

    # classification loss functions
    def one_zero_loss(self, test_set, predicted_values):
        """Returns one-zero loss score given true and predicted values"""

        incorrect=0
        for i in range(len(test_set)):
            if test_set[i].classification != predicted_values[i]:
                incorrect += 1
        self.performance += incorrect / len(test_set)
        self.num_performances += 1
        return incorrect / len(test_set)

    def log_loss(self, test_set, predicted_values, certainty):
        """Basic function that calculates log loss of a test set using a specific certainty value"""

        total = 0;
        for i in range(len(test_set)):
            if test_set[i].classification == predicted_values[i]:
                total += math.log(certainty[i])
            if test_set[i].classification != predicted_values[i]:
                if certainty[i] > .95:
                    certainty[i] = .95
                total += math.log(1-certainty[i])

        log_loss = -1*total/len(test_set)
        return log_loss

    # regression loss functions
    def mean_absolute_error(self, test_set, predicted_values):
        """Returns the mean absolute error for a given test set and their predicted values"""

        running_sum = 0
        for i in range(len(test_set)):
            running_sum += abs(test_set[i].classification - predicted_values[i])

        running_sum = running_sum / len(test_set)
        self.performance += running_sum
        self.num_performances += 1
        return running_sum

    def mean_square_error(self, test_set, predicted_values):
        """Returns the mean square error for a given test set and their predicted values"""

        running_sum = 0
        for i in range(len(test_set)):
            running_sum += (test_set[i].classification - predicted_values[i])**2
        running_sum = running_sum / len(test_set)
        return running_sum