Unverified Commit 4ffedd7c authored by Claude's avatar Claude
Browse files

Implement proper MAPE and accuracy calculation methods 

- Created calculate_mape() function using proper MAPE formula:
  MAPE = (1/n) * Σ(|actual - predicted| / |actual|) * 100

- Created calculate_accuracy() function using total error ratio:
  Accuracy = (1 - Σ|actual - predicted| / Σactual) * 100

- Updated calculate_results() to store raw actual and predicted values
- Use proper calculation methods instead of shortcuts
- Updated template to accurately describe the metrics
parent b34b8ff6

forecast_app/app.py

+71 −18
Original line number Diff line number Diff line
@@ -39,6 +39,60 @@ def load_test_data(): 
        return json.load(f)





def calculate_mape(actual_values, predicted_values):

    """

    Calculate Mean Absolute Percentage Error (MAPE)



    MAPE = (1/n) * Σ(|actual - predicted| / |actual|) * 100



    Args:

        actual_values: List of actual values

        predicted_values: List of predicted values



    Returns:

        MAPE as percentage

    """

    if len(actual_values) == 0 or len(predicted_values) == 0:

        return 0.0



    n = len(actual_values)

    total_percentage_error = 0.0



    for actual, predicted in zip(actual_values, predicted_values):

        if actual != 0:

            total_percentage_error += abs(actual - predicted) / abs(actual)



    mape = (total_percentage_error / n) * 100

    return mape





def calculate_accuracy(actual_values, predicted_values):

    """

    Calculate prediction accuracy as the complement of total error ratio



    Accuracy = (1 - Σ|actual - predicted| / Σactual) * 100



    Args:

        actual_values: List of actual values

        predicted_values: List of predicted values



    Returns:

        Accuracy as percentage

    """

    if len(actual_values) == 0 or len(predicted_values) == 0:

        return 0.0



    total_actual = sum(actual_values)

    if total_actual == 0:

        return 0.0



    total_absolute_error = sum(abs(actual - predicted)

                               for actual, predicted in zip(actual_values, predicted_values))



    accuracy = (1 - (total_absolute_error / total_actual)) * 100

    return accuracy





def calculate_results(predictions, test_data):

    """

    Calculate financial results comparing predictions to actual data
@@ -161,7 +215,8 @@ def calculate_results(predictions, test_data): 
                    'total_stockout': 0,

                    'total_revenue': 0,

                    'total_profit': 0,

                    'errors': []

                    'actual_values': [],

                    'predicted_values': []

                }



            perf = results['watch_performance'][watch_id]
@@ -171,7 +226,8 @@ def calculate_results(predictions, test_data): 
            perf['total_stockout'] += stockout

            perf['total_revenue'] += revenue

            perf['total_profit'] += profit

            perf['errors'].append(error_pct)

            perf['actual_values'].append(actual_demand)

            perf['predicted_values'].append(predicted_demand)



        results['monthly_comparison'].append(month_comparison)
@@ -179,27 +235,24 @@ def calculate_results(predictions, test_data): 
    for key in results['financial_summary']:

        results['financial_summary'][key] = round(results['financial_summary'][key], 2)



    # Calculate overall prediction accuracy

    all_errors = []

    accurate_predictions = 0  # Count predictions within 20% of actual

    total_predictions = 0

    # Calculate prediction metrics using proper formulas

    all_actual_values = []

    all_predicted_values = []



    for watch_id, perf in results['watch_performance'].items():

        # MAPE for this watch

        avg_error = sum(perf['errors']) / len(perf['errors'])

        perf['mape'] = round(avg_error, 1)

        # Calculate MAPE for this watch using the proper formula

        perf['mape'] = round(calculate_mape(perf['actual_values'], perf['predicted_values']), 1)



        # Prediction accuracy: % of predictions within 20% of actual

        accurate_count = sum(1 for err in perf['errors'] if err <= 20)

        perf['accuracy'] = round((accurate_count / len(perf['errors']) * 100), 1)

        # Calculate accuracy for this watch using the proper formula

        perf['accuracy'] = round(calculate_accuracy(perf['actual_values'], perf['predicted_values']), 1)



        all_errors.extend(perf['errors'])

        accurate_predictions += accurate_count

        total_predictions += len(perf['errors'])

        # Accumulate for overall metrics

        all_actual_values.extend(perf['actual_values'])

        all_predicted_values.extend(perf['predicted_values'])



    # Overall metrics

    overall_mape = sum(all_errors) / len(all_errors)

    overall_accuracy = (accurate_predictions / total_predictions * 100) if total_predictions > 0 else 0

    # Calculate overall metrics using proper formulas

    overall_mape = calculate_mape(all_actual_values, all_predicted_values)

    overall_accuracy = calculate_accuracy(all_actual_values, all_predicted_values)



    results['prediction_accuracy'] = {

        'mape': round(overall_mape, 1),

forecast_app/templates/results.html

+4 −4
Original line number Diff line number Diff line
@@ -27,7 +27,7 @@ 
            <div class="column is-6">

                <div class="box has-text-centered" style="background: linear-gradient(135deg, #764ba2 0%, #667eea 100%); color: white;">

                    <h3 class="title is-4" style="color: white;">Prediction Accuracy</h3>

                    <p class="subtitle is-6" style="color: white; margin-bottom: 0.5rem;">% within 20% of actual</p>

                    <p class="subtitle is-6" style="color: white; margin-bottom: 0.5rem;">1 - (Total Error / Total Actual)</p>

                    <div class="score-display">{{ results.prediction_accuracy.accuracy }}%</div>

                    <p class="subtitle is-6" style="color: white; margin-top: 0.5rem;">Higher is better</p>

                </div>
@@ -204,17 +204,17 @@ 
            <div class="content">

                {% if results.prediction_accuracy.accuracy >= 90 %}

                <div class="notification is-success">

                    <p><strong>Excellent work!</strong> Your predictions were highly accurate ({{ results.prediction_accuracy.accuracy }}% within 20% of actual, MAPE: {{ results.prediction_accuracy.mape }}%).</p>

                    <p><strong>Excellent work!</strong> Your predictions were highly accurate ({{ results.prediction_accuracy.accuracy }}% accuracy, {{ results.prediction_accuracy.mape }}% MAPE).</p>

                    <p>You demonstrated strong understanding of demand patterns and seasonality.</p>

                </div>

                {% elif results.prediction_accuracy.accuracy >= 75 %}

                <div class="notification is-info">

                    <p><strong>Good job!</strong> Your predictions were reasonably accurate ({{ results.prediction_accuracy.accuracy }}% within 20% of actual, MAPE: {{ results.prediction_accuracy.mape }}%).</p>

                    <p><strong>Good job!</strong> Your predictions were reasonably accurate ({{ results.prediction_accuracy.accuracy }}% accuracy, {{ results.prediction_accuracy.mape }}% MAPE).</p>

                    <p>There's room for improvement - review the monthly patterns to identify where you can refine your forecasts.</p>

                </div>

                {% else %}

                <div class="notification is-warning">

                    <p><strong>Keep learning!</strong> Your predictions had significant variance from actual demand ({{ results.prediction_accuracy.accuracy }}% within 20% of actual, MAPE: {{ results.prediction_accuracy.mape }}%).</p>

                    <p><strong>Keep learning!</strong> Your predictions had significant variance from actual demand ({{ results.prediction_accuracy.accuracy }}% accuracy, {{ results.prediction_accuracy.mape }}% MAPE).</p>

                    <p>Consider: Did you account for seasonal patterns? Did you follow the growth trends?</p>

                </div>

                {% endif %}