| import random
|
| import logging
|
| from typing import List, Dict, Any, Optional
|
|
|
| logger = logging.getLogger(__name__)
|
|
|
| class ProfileGenerator:
|
| """
|
| Generate realistic agent profiles based on demographic data
|
| """
|
|
|
| AGE_DISTRIBUTION = {
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| (18, 24): 0.15,
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| (25, 34): 0.25,
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| (35, 44): 0.22,
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| (45, 54): 0.18,
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| (55, 64): 0.12,
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| (65, 80): 0.08
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| }
|
|
|
| LOCATION_DISTRIBUTION = {
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| "Colombo": 0.14,
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| "Dehiwala-Mount Lavinia": 0.06,
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| "Moratuwa": 0.05,
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| "Negombo": 0.04,
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| "Sri Jayawardenepura Kotte": 0.04,
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| "Kandy": 0.06,
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| "Galle": 0.04,
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| "Jaffna": 0.03,
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| "Trincomalee": 0.03,
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| "Batticaloa": 0.03,
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| "Anuradhapura": 0.04,
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| "Polonnaruwa": 0.02,
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| "Kurunegala": 0.04,
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| "Ratnapura": 0.03,
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| "Badulla": 0.03,
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| "Matara": 0.03,
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| "Hambantota": 0.02,
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| "Vavuniya": 0.02,
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| "Nuwara Eliya": 0.03,
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| "Kalmunai": 0.02,
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| "Ampara": 0.02,
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| "Kalutara": 0.03,
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| "Gampaha": 0.06,
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| "Puttalam": 0.02,
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| "Mannar": 0.01,
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| }
|
|
|
| BASE_COORDINATES = {
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| "Colombo": [6.9271, 79.8612],
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| "Dehiwala-Mount Lavinia": [6.8649, 79.8653],
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| "Moratuwa": [6.7731, 79.8816],
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| "Negombo": [7.2097, 79.8356],
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| "Sri Jayawardenepura Kotte": [6.8924, 79.9022],
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| "Kandy": [7.2906, 80.6337],
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| "Galle": [6.0328, 80.2150],
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| "Jaffna": [9.6615, 80.0255],
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| "Trincomalee": [8.5874, 81.2152],
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| "Batticaloa": [7.7170, 81.6924],
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| "Anuradhapura": [8.3114, 80.4037],
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| "Polonnaruwa": [7.9403, 81.0188],
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| "Kurunegala": [7.4863, 80.3647],
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| "Ratnapura": [6.6828, 80.3992],
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| "Badulla": [6.9934, 81.0550],
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| "Matara": [5.9496, 80.5353],
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| "Hambantota": [6.1241, 81.1185],
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| "Vavuniya": [8.7514, 80.4971],
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| "Nuwara Eliya": [6.9497, 80.7891],
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| "Kalmunai": [7.4148, 81.8262],
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| "Ampara": [7.2975, 81.6724],
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| "Kalutara": [6.5854, 79.9607],
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| "Gampaha": [7.0873, 79.9995],
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| "Puttalam": [8.0362, 79.8283],
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| "Mannar": [8.9810, 79.9044],
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| }
|
|
|
| VALUES = [
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| "family_oriented", "traditional", "modern", "environmentally_conscious",
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| "religious", "career_focused", "community_oriented", "individualistic",
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| "health_conscious", "tech_savvy", "budget_conscious", "luxury_oriented",
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| "socially_aware", "politically_active"
|
| ]
|
|
|
| PERSONALITY_TRAITS = [
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| "Analytical", "Empathetic", "Traditional", "Ambitious",
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| "Skeptical", "Optimistic", "Cautious", "Social",
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| "Independent", "Loyal", "Creative", "Pragmatic"
|
| ]
|
|
|
| INCOME_LEVELS = ["Below Poverty Line", "Lower Income", "Lower Middle Income", "Middle Income", "Upper Middle Income", "Upper Income"]
|
| SOCIAL_MEDIA_USAGE = ["Very High", "High", "Moderate", "Low", "None"]
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| POLITICAL_LEANING = ["Progressive", "Moderate", "Conservative", "Nationalist", "Apolitical"]
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|
|
| OCCUPATIONS_YOUNG = ["Student", "Junior Developer", "Marketing Associate", "Content Creator", "Freelancer"]
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| OCCUPATIONS_MID = ["Teacher", "Engineer", "Doctor", "Manager", "Business Owner", "Accountant", "Lawyer"]
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| OCCUPATIONS_SENIOR = ["Senior Manager", "Consultant", "Professor", "Retired", "Business Owner"]
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| EDUCATION_LEVELS = ["High School", "Bachelor's", "Master's", "PhD", "Professional Certification", "No Formal Education"]
|
|
|
| @classmethod
|
| def generate_profiles(
|
| cls,
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| n: int = 1000,
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| demographic_filter: Optional[Dict[str, Any]] = None
|
| ) -> List[Dict[str, Any]]:
|
| profiles = []
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| for i in range(n):
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| profiles.append(cls._generate_single_profile(i, demographic_filter))
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| return profiles
|
|
|
| @classmethod
|
| def _generate_single_profile(
|
| cls,
|
| index: int,
|
| demographic_filter: Optional[Dict[str, Any]] = None
|
| ) -> Dict[str, Any]:
|
|
|
| if demographic_filter and 'age_range' in demographic_filter and demographic_filter['age_range']:
|
| age_range = demographic_filter['age_range']
|
| age = random.randint(age_range[0], age_range[1])
|
| else:
|
| age_bracket = random.choices(list(cls.AGE_DISTRIBUTION.keys()), weights=list(cls.AGE_DISTRIBUTION.values()))[0]
|
| age = random.randint(*age_bracket)
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|
|
|
|
| if demographic_filter and 'gender' in demographic_filter and demographic_filter['gender'] and demographic_filter['gender'] != 'All':
|
| gender = demographic_filter['gender']
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| else:
|
| gender = random.choice(["Male", "Female"])
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|
|
|
|
| if demographic_filter and 'location' in demographic_filter and demographic_filter['location'] and demographic_filter['location'] != 'All':
|
| location = demographic_filter['location']
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| else:
|
| location = random.choices(list(cls.LOCATION_DISTRIBUTION.keys()), weights=list(cls.LOCATION_DISTRIBUTION.values()))[0]
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|
|
|
|
| if age < 25:
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| occupation = random.choice(cls.OCCUPATIONS_YOUNG)
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| elif age < 55:
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| occupation = random.choice(cls.OCCUPATIONS_MID)
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| else:
|
| occupation = random.choice(cls.OCCUPATIONS_SENIOR)
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|
|
|
|
| if demographic_filter and 'education' in demographic_filter and demographic_filter['education']:
|
| education = random.choice(demographic_filter['education'])
|
| elif age < 22:
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| education = "High School"
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| else:
|
| education = random.choices(cls.EDUCATION_LEVELS, weights=[0.2, 0.40, 0.25, 0.05, 0.05, 0.05])[0]
|
|
|
|
|
| if demographic_filter and 'values' in demographic_filter and demographic_filter['values']:
|
| required_values = demographic_filter['values']
|
| other_values = [v for v in cls.VALUES if v not in required_values]
|
| extra_values = random.sample(other_values, min(2, len(other_values)))
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| values = required_values + extra_values
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| else:
|
| num_values = random.randint(2, 4)
|
| values = random.sample(cls.VALUES, num_values)
|
|
|
|
|
| base_coords = cls.BASE_COORDINATES.get(location, [7.8731, 80.7718])
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|
|
|
|
| lat_min, lat_max = -0.05, 0.05
|
| lng_min, lng_max = -0.05, 0.05
|
|
|
|
|
| west_coast_cities = ["Colombo", "Dehiwala-Mount Lavinia", "Moratuwa", "Negombo", "Kalutara", "Puttalam", "Mannar"]
|
| east_coast_cities = ["Trincomalee", "Batticaloa", "Kalmunai"]
|
| south_coast_cities = ["Galle", "Matara", "Hambantota"]
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| north_coast_cities = ["Jaffna"]
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|
|
| if location in west_coast_cities:
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| lng_min = 0.0
|
| elif location in east_coast_cities:
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| lng_max = 0.0
|
|
|
| if location in south_coast_cities:
|
| lat_min = 0.0
|
| elif location in north_coast_cities:
|
| lat_max = 0.0
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|
|
| lat = base_coords[0] + random.uniform(lat_min, lat_max)
|
| lng = base_coords[1] + random.uniform(lng_min, lng_max)
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|
|
|
|
| if demographic_filter and 'income_level' in demographic_filter and demographic_filter['income_level']:
|
| income_level = random.choice(demographic_filter['income_level'])
|
| elif occupation in ["Student", "Retired"] or age < 22:
|
| income_level = random.choices(cls.INCOME_LEVELS, weights=[0.1, 0.4, 0.3, 0.15, 0.05, 0.0])[0]
|
| elif occupation in ["Business Owner", "Senior Manager", "Doctor", "Lawyer"]:
|
| income_level = random.choices(cls.INCOME_LEVELS, weights=[0.0, 0.0, 0.1, 0.3, 0.4, 0.2])[0]
|
| else:
|
| income_level = random.choices(cls.INCOME_LEVELS, weights=[0.05, 0.2, 0.3, 0.3, 0.1, 0.05])[0]
|
|
|
|
|
| if location in ["Jaffna", "Vavuniya", "Mannar", "Batticaloa", "Trincomalee"]:
|
| religions = ["Hindu", "Christian", "Muslim", "Buddhist"]
|
| rel_weights = [0.65, 0.15, 0.15, 0.05]
|
| ethnicities = ["Tamil", "Moor", "Sinhalese", "Burgher"]
|
| eth_weights = [0.80, 0.10, 0.05, 0.05]
|
| elif location in ["Kalmunai", "Ampara"]:
|
| religions = ["Muslim", "Buddhist", "Hindu", "Christian"]
|
| rel_weights = [0.55, 0.30, 0.10, 0.05]
|
| ethnicities = ["Moor", "Sinhalese", "Tamil", "Burgher"]
|
| eth_weights = [0.55, 0.30, 0.10, 0.05]
|
| else:
|
| religions = ["Buddhist", "Hindu", "Muslim", "Christian"]
|
| rel_weights = [0.70, 0.13, 0.10, 0.07]
|
| ethnicities = ["Sinhalese", "Tamil", "Moor", "Burgher"]
|
| eth_weights = [0.74, 0.15, 0.09, 0.02]
|
|
|
| if demographic_filter and 'religion' in demographic_filter and demographic_filter['religion']:
|
| religion = random.choice(demographic_filter['religion'])
|
| else:
|
| religion = random.choices(religions, weights=rel_weights)[0]
|
|
|
| if demographic_filter and 'ethnicity' in demographic_filter and demographic_filter['ethnicity']:
|
| ethnicity = random.choice(demographic_filter['ethnicity'])
|
| else:
|
| ethnicity = random.choices(ethnicities, weights=eth_weights)[0]
|
|
|
|
|
| if ethnicity == "Sinhalese":
|
| first_names = ["Nuwan", "Chamara", "Dilanka", "Sachini", "Nimasha", "Kasun", "Tharaka", "Malsha", "Dinuka", "Sandali"]
|
| surnames = ["Perera", "Silva", "Fernando", "Jayasinghe", "Wickramasinghe", "Gunasekara", "Rajapaksa", "Dissanayake", "Bandara"]
|
| elif ethnicity == "Tamil":
|
| first_names = ["Arjun", "Priya", "Kavitha", "Suresh", "Anitha", "Rajan", "Meena", "Vijay", "Lakshmi", "Krishnan"]
|
| surnames = ["Nair", "Pillai", "Shankar", "Murugan", "Selvam", "Balasingham", "Ratnasingham", "Thambipillai"]
|
| elif ethnicity == "Moor":
|
| first_names = ["Mohamed", "Fathima", "Hassan", "Ayesha", "Ibrahim", "Zainab", "Rashid", "Nusrath", "Farhan", "Shifana"]
|
| surnames = ["Marikar", "Lafir", "Cader", "Zarook", "Ismail", "Saheed"]
|
| else:
|
| first_names = ["Jerome", "Michelle", "Kevin", "Sandra", "Brian", "Karen"]
|
| surnames = ["de Silva", "van Dort", "Ondaatje", "Grenier"]
|
|
|
| name = f"{random.choice(first_names)} {random.choice(surnames)}"
|
|
|
|
|
| if demographic_filter and 'social_media_usage' in demographic_filter and demographic_filter['social_media_usage']:
|
| social_media_usage = random.choice(demographic_filter['social_media_usage'])
|
| elif age < 30:
|
| social_media_usage = random.choices(cls.SOCIAL_MEDIA_USAGE, weights=[0.4, 0.4, 0.15, 0.05, 0.0])[0]
|
| elif age < 50:
|
| social_media_usage = random.choices(cls.SOCIAL_MEDIA_USAGE, weights=[0.1, 0.3, 0.4, 0.15, 0.05])[0]
|
| else:
|
| social_media_usage = random.choices(cls.SOCIAL_MEDIA_USAGE, weights=[0.0, 0.1, 0.3, 0.4, 0.2])[0]
|
|
|
|
|
| if demographic_filter and 'political_leaning' in demographic_filter and demographic_filter['political_leaning']:
|
| political_leaning = random.choice(demographic_filter['political_leaning'])
|
| else:
|
| political_leaning = random.choice(cls.POLITICAL_LEANING)
|
|
|
|
|
| personality_traits = random.sample(cls.PERSONALITY_TRAITS, random.randint(2, 3))
|
|
|
| return {
|
| "agent_id": f"agent_{index:04d}",
|
| "name": name,
|
| "age": age,
|
| "gender": gender,
|
| "location": location,
|
| "coordinates": [lat, lng],
|
| "occupation": occupation,
|
| "education": education,
|
| "income_level": income_level,
|
| "religion": religion,
|
| "ethnicity": ethnicity,
|
| "social_media_usage": social_media_usage,
|
| "political_leaning": political_leaning,
|
| "personality_traits": personality_traits,
|
| "values": values,
|
| "bio": ""
|
| }
|
|
|
| @classmethod
|
| def generate_social_network(
|
| cls,
|
| profiles: List[Dict[str, Any]],
|
| avg_friends: int = 10
|
| ) -> Dict[str, List[str]]:
|
| network = {p['agent_id']: [] for p in profiles}
|
| for profile in profiles:
|
| agent_id = profile['agent_id']
|
| candidates = []
|
| for other in profiles:
|
| if other['agent_id'] == agent_id:
|
| continue
|
| score = 0
|
| if other['location'] == profile['location']:
|
| score += 3
|
| shared_values = set(profile['values']) & set(other['values'])
|
| score += len(shared_values) * 2
|
| age_diff = abs(profile['age'] - other['age'])
|
| if age_diff <= 10:
|
| score += 2
|
| elif age_diff <= 20:
|
| score += 1
|
| if score > 0:
|
| candidates.append((other['agent_id'], score))
|
| candidates.sort(key=lambda x: x[1], reverse=True)
|
| num_friends = max(1, int(random.gauss(avg_friends, 3)))
|
| num_friends = min(num_friends, len(candidates))
|
| if candidates:
|
| selected = []
|
| for cand_id, score in candidates[:num_friends * 2]:
|
| if len(selected) >= num_friends:
|
| break
|
| if random.random() < (score / 10):
|
| selected.append(cand_id)
|
| network[agent_id] = selected
|
| return network
|
|
|
