Testing Scoring System

gistfile1.txt

from collections import Counter
import pandas as pd

# Define scoring variables
core_set_score = 5
second_tier_score = 3
third_tier_score = 1
grace_zone_boost = 1
entity_boost = 1
double_entity_boost = 2
tag_title_meta_score = 20
images_score = 5
completeness_bonus = 5
diversification_bonus = 5
max_string_score = 100 - tag_title_meta_score - images_score - completeness_bonus - diversification_bonus

# Simulate scenarios
def simulate_scenarios():
    scenarios = []
    for core_set_count in range(1, 11):
        for second_tier_count in range(1, 16):
            for third_tier_count in range(1, 21):
                score_from_strings = (core_set_count * core_set_score + 
                                      second_tier_count * second_tier_score + 
                                      third_tier_count * third_tier_score)
                
                diversification_penalty = 0
                if score_from_strings > max_string_score:
                    diversification_penalty = score_from_strings - max_string_score
                    score_from_strings = max_string_score
                
                final_score = (score_from_strings + tag_title_meta_score + 
                               images_score - diversification_penalty)
                
                is_complete = (core_set_count >= 5 and 
                               second_tier_count >= 10 and 
                               third_tier_count >= 15)
                
                is_diverse = (core_set_count + second_tier_count + third_tier_count) >= 30
                
                if is_complete:
                    final_score += completeness_bonus
                if is_diverse:
                    final_score += diversification_bonus

                scenarios.append({
                    'Core Set Count': core_set_count,
                    'Second Tier Count': second_tier_count,
                    'Third Tier Count': third_tier_count,
                    'Score from Strings': score_from_strings,
                    'Diversification Penalty': diversification_penalty,
                    'Final Score': final_score,
                    'Is Complete': is_complete,
                    'Is Diverse': is_diverse
                })
    
    return pd.DataFrame(scenarios)

# Run the simulation and filter results
scenarios_df = simulate_scenarios()
filtered_scenarios = scenarios_df[(scenarios_df['Final Score'] >= 90) & (scenarios_df['Final Score'] <= 100)]

The simulation evaluated a total of 3000 scenarios, considering various combinations of the usage of strings from the core set, second tier, and third tier. The code looped through each possible combination within reasonable limits to calculate the final score based on the rules we brainstormed.

Strings range is not taken into consideration