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import numpy as np
import pandas as pd
from generate_reports import get_report_reliability
DAMAGE_STATES = ['none', 'minor', 'severe', 'collapse']
def compute_likelihood(reported_state, true_state, reliability):
if reported_state == true_state:
return reliability
reported_idx = DAMAGE_STATES.index(reported_state)
true_idx = DAMAGE_STATES.index(true_state)
distance = abs(reported_idx - true_idx)
base_error_prob = (1 - reliability) / 3.0
if distance == 1:
return base_error_prob * 2.0
elif distance == 2:
return base_error_prob * 1.0
else:
return base_error_prob * 0.5
def bayesian_update(prior, reported_state, reliability):
likelihood = np.array([
compute_likelihood(reported_state, state, reliability)
for state in DAMAGE_STATES
])
numerator = likelihood * prior
denominator = np.sum(numerator)
if denominator < 1e-10:
return prior
posterior = numerator / denominator
return posterior
def entropy(probs):
probs = np.array(probs)
probs = probs[probs > 0]
return -np.sum(probs * np.log2(probs))
def bootstrap_beliefs(reports, prior, n_bootstrap=100):
if len(reports) == 0:
return {
'mean': prior,
'std_dev': np.zeros_like(prior)
}
bootstrap_posteriors = []
for _ in range(n_bootstrap):
resampled_reports = [reports[i] for i in np.random.choice(
len(reports), size=len(reports), replace=True
)]
belief = prior.copy()
for report in resampled_reports:
reliability = get_report_reliability(report['source'])
belief = bayesian_update(belief, report['reported_state'], reliability)
bootstrap_posteriors.append(belief)
bootstrap_posteriors = np.array(bootstrap_posteriors)
return {
'mean': np.mean(bootstrap_posteriors, axis=0),
'std_dev': np.std(bootstrap_posteriors, axis=0)
}
def process_building(building, all_reports, prior):
building_id = building['building_id']
building_reports = all_reports[all_reports['building_id'] == building_id]
building_reports = building_reports.sort_values('time_minutes')
current_belief = prior.copy()
report_list = []
for _, report in building_reports.iterrows():
reliability = get_report_reliability(report['source'])
current_belief = bayesian_update(
current_belief,
report['reported_state'],
reliability
)
report_list.append(report.to_dict())
bootstrap_result = bootstrap_beliefs(report_list, prior, n_bootstrap=50)
return {
'building_id': building_id,
'p_none': current_belief[0],
'p_minor': current_belief[1],
'p_severe': current_belief[2],
'p_collapse': current_belief[3],
'entropy': entropy(current_belief),
'num_reports': len(building_reports)
}
def run_inference(buildings_df, reports_df, n_samples=5000):
results = []
for _, building in buildings_df.iterrows():
prior = np.array([
building['p_none'],
building['p_minor'],
building['p_severe'],
building['p_collapse']
])
result = process_building(building, reports_df, prior)
# Normalize posterior before sampling
posterior = np.array([
result['p_none'],
result['p_minor'],
result['p_severe'],
result['p_collapse']
])
posterior = posterior / posterior.sum()
samples = np.random.choice([0,1,2,3], size=n_samples, p=posterior)
collapse_samples = (samples == 3).astype(float)
result['p_collapse_std'] = collapse_samples.std()
results.append(result)
return pd.DataFrame(results)
def compute_decision_metrics(buildings_df, beliefs_df, n_teams):
merged = buildings_df[['building_id', 'true_damage', 'occupancy', 'p_none', 'p_minor', 'p_severe', 'p_collapse']].merge(
beliefs_df[['building_id', 'p_none', 'p_minor', 'p_severe', 'p_collapse']],
on='building_id',
suffixes=('_prior', '_posterior')
)
merged['at_risk_true'] = merged.apply(lambda row: {
'collapse': 0.9 * row['occupancy'],
'severe': 0.4 * row['occupancy'],
'minor': 0.05 * row['occupancy'],
'none': 0
}[row['true_damage']], axis=1)
merged['expected_at_risk'] = (
merged['p_collapse_posterior'] * 0.9 * merged['occupancy'] +
merged['p_severe_posterior'] * 0.4 * merged['occupancy'] +
merged['p_minor_posterior'] * 0.05 * merged['occupancy']
)
bayesian_top = merged.nlargest(n_teams, 'expected_at_risk')
bayesian_saved = bayesian_top['at_risk_true'].sum()
naive_top = merged.nlargest(n_teams, 'p_collapse_prior')
naive_saved = naive_top['at_risk_true'].sum()
return {
'bayesian_lives_saved': int(bayesian_saved),
'naive_lives_saved': int(naive_saved),
'improvement': int(bayesian_saved - naive_saved),
'improvement_pct': (bayesian_saved - naive_saved) / naive_saved * 100 if naive_saved > 0 else 0
} |