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0a0d5dc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 | #!/usr/bin/env python3
"""Run real-data experiments."""
import os
import sys
import json
import time
import argparse
import yaml
import numpy as np
from datetime import datetime
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from src.data import load_lastfm_data, load_movielens_data, sample_deletions
from src.model import PoissonGammaVI
from src.graph_utils import build_adjacency, compute_graph_stats
from src.metrics import (compute_all_metrics, compute_deletion_influence_by_distance,
fit_exponential_decay, compute_local_error, compute_chi_poisson_gamma,
compute_gradient_interference)
from src.unlearning import one_step_downdate_poisson_gamma
from src.utils import generate_run_id, generate_config_id, save_jsonl, ensure_dir
def run_real_dataset(dataset_name, edges, N, M, preprocessing, config):
"""Run deletion experiments on a real dataset."""
K_values = config.get('K_values', [5, 10])
num_deletions = config.get('num_deletions', 50)
radii = config.get('radii', [1, 2, 3, 4])
prior = config.get('prior', {})
a0 = prior.get('a0', 0.3)
b0 = prior.get('b0', 1.0)
c0 = prior.get('c0', 0.3)
d0 = prior.get('d0', 1.0)
max_iter = config.get('max_iter', 300)
tol = config.get('tol', 1e-4)
seed = config.get('seed', 42)
all_records = []
for K in K_values:
print(f"\n K={K}")
run_id = generate_run_id()
config_id = generate_config_id({**config, 'K': K, 'dataset': dataset_name})
model = PoissonGammaVI(N, M, K, a0, b0, c0, d0, max_iter=max_iter, tol=tol, seed=seed)
print(f" Fitting full model...")
t0 = time.time()
full_result = model.fit_full(edges)
t_full = time.time() - t0
full_params = full_result.params
print(f" Full fit: {full_result.n_iterations} iters, {t_full:.1f}s")
user_to_items, item_to_users, edge_dict = build_adjacency(edges, N, M)
deletion_samples = sample_deletions(edges, user_to_items, item_to_users, num_deletions, seed=seed)
print(f" Running {len(deletion_samples)} deletions...")
for del_idx, (edge_to_del, del_type) in enumerate(deletion_samples):
if del_idx % 10 == 0:
print(f" Deletion {del_idx+1}/{len(deletion_samples)}")
i_del, j_del, x_del = edge_to_del
# Exact
exact_result = model.fit_without_edge(edges, edge_to_del, init_params=full_params)
exact_params = exact_result.params
# Local
local_results = {}
local_params = {}
for R in radii:
lr = model.fit_local(edges, edge_to_del, R, init_params=full_params)
local_results[R] = lr
local_params[R] = lr.params
# Warm-start
ws_result = model.fit_warm_start_global(edges, edge_to_del, init_params=full_params)
# One-step
os_result = one_step_downdate_poisson_gamma(
edges, edge_to_del, full_params, N, M, K, a0, b0, c0, d0)
# Metrics
model_kwargs = {'a0': a0, 'b0': b0, 'c0': c0, 'd0': d0}
metrics = compute_all_metrics(
full_params, exact_params, local_params,
ws_result.params, os_result.params,
edge_to_del, edges, N, M, K,
'poisson_gamma', model=model, radii=radii,
model_kwargs=model_kwargs)
record = {
'run_id': run_id,
'config_id': config_id,
'dataset_type': 'real',
'dataset_name': dataset_name,
'model_family': 'poisson_gamma',
'inference_type': 'vi',
'likelihood': 'poisson',
'prior': 'gamma',
'N': N, 'M': M, 'K': K,
'n_edges': len(edges),
'deletion_edge': [int(i_del), int(j_del), float(x_del)],
'deletion_type': del_type,
'deletion_index': del_idx,
'runtime_full': t_full,
'runtime_exact': exact_result.runtime_sec,
'runtime_warm_start': ws_result.runtime_sec,
'runtime_one_step': os_result.runtime_sec,
'exact_converged': exact_result.converged,
'a0': a0, 'b0': b0, 'c0': c0, 'd0': d0,
}
for R in radii:
record[f'runtime_local_R{R}'] = local_results[R].runtime_sec
record[f'local_R{R}_converged'] = local_results[R].converged
record.update(metrics)
if 'influence_by_distance' in record:
for d_str, val in record['influence_by_distance'].items():
record[f'influence_d{d_str}'] = val
all_records.append(record)
return all_records
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='config/real_data.yaml')
parser.add_argument('--datasets', nargs='*', default=None)
args = parser.parse_args()
with open(args.config) as f:
real_cfg = yaml.safe_load(f)
output_dir = ensure_dir('results/raw')
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
output_file = os.path.join(output_dir, f'real_{timestamp}.jsonl')
datasets_to_run = args.datasets or list(real_cfg.keys())
for ds_name in datasets_to_run:
if ds_name not in real_cfg:
print(f"Unknown dataset config: {ds_name}")
continue
ds_cfg = real_cfg[ds_name]
print(f"\n{'='*60}")
print(f"Dataset: {ds_name}")
print(f"{'='*60}")
# Load data
if 'lastfm' in ds_name:
edges, N, M, preproc = load_lastfm_data(
max_users=ds_cfg.get('max_users', 1000),
max_items=ds_cfg.get('max_items', 1000),
max_edges=ds_cfg.get('max_edges', 50000),
min_user_degree=ds_cfg.get('min_user_degree', 5),
min_item_degree=ds_cfg.get('min_item_degree', 5),
max_count=ds_cfg.get('max_count', 100),
seed=ds_cfg.get('seed', 42))
elif 'movielens' in ds_name:
mode = ds_cfg.get('mode', 'rating_count')
edges, N, M, preproc = load_movielens_data(
mode=mode,
max_users=ds_cfg.get('max_users', 1000),
max_items=ds_cfg.get('max_items', 1000),
max_edges=ds_cfg.get('max_edges', 50000),
min_user_degree=ds_cfg.get('min_user_degree', 5),
min_item_degree=ds_cfg.get('min_item_degree', 5),
seed=ds_cfg.get('seed', 42))
else:
print(f" Unsupported dataset: {ds_name}")
continue
# Save preprocessing
preproc_dir = ensure_dir('results/reports')
with open(os.path.join(preproc_dir, f'dataset_card_{ds_name}.json'), 'w') as f:
json.dump(preproc, f, indent=2)
graph_stats = compute_graph_stats([(e[0], e[1]) for e in edges], N, M)
print(f" Graph stats: {json.dumps(graph_stats, indent=2)}")
records = run_real_dataset(ds_name, edges, N, M, preproc, ds_cfg)
save_jsonl(records, output_file)
print(f" Saved {len(records)} records for {ds_name}")
print(f"\nOutput: {output_file}")
if __name__ == '__main__':
main()
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