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"""Natural-data relation fields under the Tier 0 recipe.
Each side is encoded into its own discovered factor coordinates -- text
word classes from distributional induction, vision segment classes from
feature clustering -- and the two coordinate systems are paired by joint
structure, never by a declared lexicon. Scene states are the resulting
sets of segment or phrase codes; relations are moment-kernel similarities
between scenes.
The field correlation at the hidden pairing is the go/no-go statistic:
polynomial recovery needs roughly 0.9, and the synthetic world showed
nothing works below it. Hidden pairs are read only to compute it.
"""
from __future__ import annotations
import argparse
import json
import re
from collections import Counter, defaultdict
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
from scipy.optimize import linear_sum_assignment
from sklearn.cluster import KMeans
from .common import read_json, seed_everything, write_json
from .natural_families import distributional_families, load_phrases, statistics
from .synth_cc_battery import moment_field
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--vg-dir", default="artifacts/vg_5k")
parser.add_argument("--objects", default="artifacts/vg_5k/natural_objects.pt")
parser.add_argument("--samples", type=int, default=256)
parser.add_argument("--fit-nodes", type=int, default=2000)
parser.add_argument("--vision-classes", type=int, default=24)
parser.add_argument("--text-classes", type=int, default=24)
parser.add_argument("--clusters", type=int, default=24)
parser.add_argument("--min-count", type=int, default=200)
parser.add_argument("--max-vocabulary", type=int, default=300)
parser.add_argument("--context-window", type=int, default=2)
parser.add_argument("--min-family", type=int, default=3)
parser.add_argument("--components", type=int, default=64)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--output", default="artifacts/vg_5k/natural_fields.pt")
return parser.parse_args()
def text_scene_codes(
records: list[dict], word_class: dict[str, int], classes: int
) -> dict[str, torch.Tensor]:
"""One code vector per region description: its word-class profile."""
codes: dict[str, list[torch.Tensor]] = {}
for record in records:
vectors = []
for phrase in record["region_closed"]:
vector = torch.zeros(classes)
for token in re.findall(r"[a-z]+", phrase.lower()):
if token in word_class:
vector[word_class[token]] += 1.0
if vector.sum() > 0:
vectors.append(vector)
if vectors:
codes[record["node_id"]] = F.normalize(torch.stack(vectors), dim=-1)
return codes
def vision_scene_codes(
state: dict, model: KMeans, classes: int
) -> dict[str, torch.Tensor]:
"""One code vector per segment: its feature-class assignment."""
codes: dict[str, torch.Tensor] = {}
for node, segments in zip(state["node_ids"], state["segments"]):
if not segments:
continue
labels = model.predict(
np.stack([segment["feature"] for segment in segments]).astype(np.float64)
)
vectors = torch.zeros(len(segments), classes)
for row, label in enumerate(labels):
vectors[row, int(label)] = 1.0
codes[node] = F.normalize(vectors, dim=-1)
return codes
def align_classes(
text_codes: dict[str, torch.Tensor],
vision_codes: dict[str, torch.Tensor],
classes: int,
) -> np.ndarray:
"""Pair vision classes to text classes by marginal frequency rank.
Within-scene co-occurrence is the stronger signal but needs a second
factor to condition on; frequency is the available unimodal statistic
at this stage and is reported as the first pass.
"""
text_mass = torch.zeros(classes)
for code in text_codes.values():
text_mass += code.sum(0)
vision_mass = torch.zeros(classes)
for code in vision_codes.values():
vision_mass += code.sum(0)
text_order = torch.argsort(text_mass, descending=True).numpy()
vision_order = torch.argsort(vision_mass, descending=True).numpy()
mapping = np.empty(classes, dtype=int)
mapping[vision_order] = text_order
return mapping
def moment_states(codes: torch.Tensor) -> torch.Tensor:
first = codes.mean(0)
second = (codes[:, :, None] * codes[:, None, :]).mean(0).flatten()
return torch.cat([first, second])
def main() -> None:
args = parse_args()
seed_everything(args.seed)
vg_dir = Path(args.vg_dir)
text_records = [
json.loads(line)
for line in (vg_dir / "text_nodes.jsonl").read_text(encoding="utf-8").splitlines()
if line.strip()
]
truth = [
json.loads(line)
for line in (vg_dir / "ground_truth.private.jsonl")
.read_text(encoding="utf-8")
.splitlines()
if line.strip()
]
state = torch.load(args.objects, map_location="cpu", weights_only=False)
phrases = load_phrases(vg_dir / "text_nodes.jsonl", "region_closed")
counts = Counter(token for tokens in phrases for token in set(tokens))
vocabulary = [
word
for word, count in counts.most_common(args.max_vocabulary)
if count >= args.min_count
]
_, _, context = statistics(phrases, set(vocabulary), args.context_window)
args.clusters = args.text_classes
families = distributional_families(vocabulary, context, args)
word_class = {
word: index for index, family in enumerate(families) for word in family
}
text_classes = len(families)
features = np.stack(
[
segment["feature"]
for segments in state["segments"][: args.fit_nodes]
for segment in segments
]
).astype(np.float64)
vision_model = KMeans(
args.vision_classes, n_init=10, random_state=args.seed
).fit(features)
text_codes = text_scene_codes(text_records, word_class, text_classes)
vision_codes = vision_scene_codes(state, vision_model, args.vision_classes)
shared = min(text_classes, args.vision_classes)
mapping = align_classes(
{k: v[:, :shared] for k, v in text_codes.items()},
{k: v[:, :shared] for k, v in vision_codes.items()},
shared,
)
pairs = [
pair
for pair in truth
if pair["vision_node_id"] in vision_codes and pair["text_node_id"] in text_codes
][: args.samples]
vision_sets, text_sets = [], []
for pair in pairs:
vision = vision_codes[pair["vision_node_id"]][:, :shared]
remapped = torch.zeros_like(vision)
for source in range(shared):
remapped[:, mapping[source]] = vision[:, source]
vision_sets.append(F.normalize(remapped, dim=-1))
text_sets.append(
F.normalize(text_codes[pair["text_node_id"]][:, :shared], dim=-1)
)
visual_field = moment_field(vision_sets)
text_field = moment_field(text_sets)
size = len(pairs)
mask = ~np.eye(size, dtype=bool)
correlation = float(
np.corrcoef(
visual_field.double().numpy()[mask], text_field.double().numpy()[mask]
)[0, 1]
)
torch.save(
{
"visual_field": visual_field,
"text_field": text_field,
"pairs": [p["vision_node_id"] for p in pairs],
},
args.output,
)
summary = {
"protocol": (
"Text classes from distributional induction, vision classes "
"from segment-feature clustering, paired by marginal frequency "
"rank. Hidden pairs are read only for the correlation."
),
"samples": size,
"text_classes": text_classes,
"vision_classes": args.vision_classes,
"field_correlation_at_truth": correlation,
"go_no_go": "recovery needs about 0.9",
}
print(json.dumps(summary))
write_json(str(args.output).replace(".pt", ".json"), summary)
if __name__ == "__main__":
main()
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