Datasets:

Jyo-K
/

PopTurk

"""Embed a SINGLE-FIELD text view with e5-small, for the F4 and F5 rescue channels.

WHY SINGLE-FIELD. From the false-negative audit: of the copies that are never retrieved,
78% are outside the fused top-100 of every retriever, worth 0.416 val points. The reason is
structural, not a tuning problem - all three retrievers embed name AND address together, so a
copy whose name was scrambled or whose address was deleted is far away in the joint space
however good the encoder is. The losses line up exactly with that:

    by address: empty 0.172, Indian addresses with no parsable house number 0.138
    by name:    scrambled 0.127, partial 0.108, typo 0.073, native script 0.069

A channel that reads only the surviving field is the only thing that can reach them.

  --view name --side pool --only-empty-address   F4: name-only over the ~265k address-less
                                                 records. Their address is gone, so including
                                                 it is not merely useless, it is noise that
                                                 dominates a short name.
  --view addr                                    F5: address-only over the whole pool, which
                                                 reaches scrambled and partial names sitting
                                                 at a correct address.

The model is the same multilingual-e5-small already used for the main channel, with the same
"query: " prefix, so the output is directly comparable and needs no new download.
"""
from __future__ import annotations

import argparse
import json
import os
import sys
import time

import numpy as np
import pandas as pd

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from berx import paths as P  # noqa: E402

MODEL = "intfloat/multilingual-e5-small"
PREFIX = "query: "


def build_texts(df: pd.DataFrame, view: str) -> list[str]:
    if view == "name":
        src = df["business_name"].astype(str)
    elif view == "addr":
        src = df["business_address"].astype(str)
    else:
        raise ValueError(view)
    # An empty field would embed to whatever the prefix alone embeds to, which is the same
    # point for every such row - a dense cluster of mutually "identical" records. The country
    # keeps them apart and costs nothing.
    cc = df["country"].astype(str)
    return [PREFIX + (s.strip() or "?") + " | " + c for s, c in zip(src, cc)]


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--split", default="test")
    ap.add_argument("--view", required=True, choices=["name", "addr"])
    ap.add_argument("--side", required=True, choices=["s1", "pool"])
    ap.add_argument("--out-channel", required=True)
    ap.add_argument("--only-empty-address", action="store_true",
                    help="pool side only: restrict to records with no address (F4)")
    ap.add_argument("--batch", type=int, default=1024)
    ap.add_argument("--max-len", type=int, default=64)
    ap.add_argument("--device", default="cuda")
    a = ap.parse_args()

    import torch
    from transformers import AutoModel, AutoTokenizer

    d = os.path.dirname(P.work("prep", a.split, "_"))
    src = "source1.parquet" if a.side == "s1" else "pool.parquet"
    df = pd.read_parquet(os.path.join(d, src))

    keep = np.arange(len(df))
    if a.only_empty_address:
        assert a.side == "pool", "--only-empty-address applies to the pool side"
        keep = np.flatnonzero((df["business_address"].astype(str).str.strip() == "").to_numpy())
        df = df.iloc[keep].reset_index(drop=True)
        print(f"  restricted to {len(df):,} address-less records "
              f"({len(df)/len(keep) if len(keep) else 0:.0%} of the filter set)")

    texts = build_texts(df, a.view)
    print(f"{a.out_channel}/{a.split}_{a.side}: {len(texts):,} texts, view={a.view}")
    print(f"  e.g. {texts[0][:90]!r}")

    tok = AutoTokenizer.from_pretrained(MODEL)
    model = AutoModel.from_pretrained(MODEL, dtype=torch.float16).eval().to(a.device)

    # Length-sort so a batch pads to its own longest member. On single-field text most rows
    # are very short and the corpus maximum is not representative of anything.
    order = np.argsort([len(t) for t in texts], kind="stable")
    out = np.empty((len(texts), 384), dtype=np.float16)

    # Checkpointing. This is a ~30 minute pass on a card shared with 25 other users, so it
    # will occasionally be killed. The counter is a position in the LENGTH-SORTED order,
    # which is the only order in which completed work forms a contiguous prefix.
    dest_early = os.path.join(P.EMB, a.out_channel, f"{a.split}_{a.side}")
    os.makedirs(dest_early, exist_ok=True)
    ck = os.path.join(dest_early, "partial")
    start = 0
    if os.path.exists(ck + ".json"):
        meta_ck = json.load(open(ck + ".json"))
        if meta_ck.get("n") == len(texts) and meta_ck.get("view") == a.view:
            start = int(meta_ck["done"])
            out = np.load(ck + ".npy")
            print(f"  resuming at {start:,}/{len(texts):,}")
        else:
            print("  checkpoint is for a different input; ignoring")
    t0 = time.time()
    with torch.inference_mode():
        for s in range(start, len(order), a.batch):
            idx = order[s:s + a.batch]
            enc = tok([texts[i] for i in idx], padding=True, truncation=True,
                      max_length=a.max_len, return_tensors="pt").to(a.device)
            h = model(**enc).last_hidden_state
            m = enc["attention_mask"].unsqueeze(-1).to(h.dtype)
            v = (h * m).sum(1) / m.sum(1).clamp(min=1e-6)          # mean pool, as e5 expects
            v = torch.nn.functional.normalize(v.float(), dim=1)
            out[idx] = v.half().cpu().numpy()
            if (s // a.batch) % 200 == 0 and s > start:
                np.save(ck + ".npy", out)
                json.dump({"done": int(s), "n": len(texts), "view": a.view},
                          open(ck + ".json", "w"))
            if (s // a.batch) % 200 == 0:
                done = (s + len(idx)) / len(order)
                el = time.time() - t0
                print(f"    {s+len(idx):>10,}/{len(order):,} ({done:6.1%})  "
                      f"{(s+len(idx))/max(el,1e-9):,.0f} rows/s  "
                      f"eta {el/max(done,1e-9)-el:6.0f}s", flush=True)

    dest = P.emb_dir(a.out_channel, a.split, a.side) \
        if a.out_channel in P.CHANNELS else \
        os.path.join(P.EMB, a.out_channel, f"{a.split}_{a.side}")
    os.makedirs(dest, exist_ok=True)
    np.save(os.path.join(dest, "matrix.npy"), out)
    for ext in (".npy", ".json"):                       # the run finished; drop the partial
        if os.path.exists(ck + ext):
            os.remove(ck + ext)
    with open(os.path.join(dest, "ids.txt"), "w") as fh:
        fh.write("\n".join(df["entity_id"].astype(str)) + "\n")
    # The row->original-index map. F4's pool matrix covers only address-less records, so its
    # row i is NOT pool row i; anything consuming it must remap or every candidate index is
    # wrong. Saving it here is what makes that remap possible at all.
    np.save(os.path.join(dest, "rows.npy"), keep.astype(np.int64))
    json.dump({"model": MODEL, "prefix": PREFIX, "view": a.view, "side": a.side,
               "n": len(df), "dim": 384, "subset": bool(a.only_empty_address),
               "seconds": round(time.time() - t0, 1)},
              open(os.path.join(dest, "meta.json"), "w"), indent=1)
    print(f"  wrote {dest}  {out.shape}  {time.time()-t0:.0f}s")


if __name__ == "__main__":
    main()

	"""Embed a SINGLE-FIELD text view with e5-small, for the F4 and F5 rescue channels.

	WHY SINGLE-FIELD. From the false-negative audit: of the copies that are never retrieved,
	78% are outside the fused top-100 of every retriever, worth 0.416 val points. The reason is
	structural, not a tuning problem - all three retrievers embed name AND address together, so a
	copy whose name was scrambled or whose address was deleted is far away in the joint space
	however good the encoder is. The losses line up exactly with that:

	by address: empty 0.172, Indian addresses with no parsable house number 0.138
	by name: scrambled 0.127, partial 0.108, typo 0.073, native script 0.069

	A channel that reads only the surviving field is the only thing that can reach them.

	--view name --side pool --only-empty-address F4: name-only over the ~265k address-less
	records. Their address is gone, so including
	it is not merely useless, it is noise that
	dominates a short name.
	--view addr F5: address-only over the whole pool, which
	reaches scrambled and partial names sitting
	at a correct address.

	The model is the same multilingual-e5-small already used for the main channel, with the same
	"query: " prefix, so the output is directly comparable and needs no new download.
	"""
	from __future__ import annotations

	import argparse
	import json
	import os
	import sys
	import time

	import numpy as np
	import pandas as pd

	sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
	from berx import paths as P # noqa: E402

	MODEL = "intfloat/multilingual-e5-small"
	PREFIX = "query: "


	def build_texts(df: pd.DataFrame, view: str) -> list[str]:
	if view == "name":
	src = df["business_name"].astype(str)
	elif view == "addr":
	src = df["business_address"].astype(str)
	else:
	raise ValueError(view)
	# An empty field would embed to whatever the prefix alone embeds to, which is the same
	# point for every such row - a dense cluster of mutually "identical" records. The country
	# keeps them apart and costs nothing.
	cc = df["country"].astype(str)
	return [PREFIX + (s.strip() or "?") + " \| " + c for s, c in zip(src, cc)]


	def main():
	ap = argparse.ArgumentParser()
	ap.add_argument("--split", default="test")
	ap.add_argument("--view", required=True, choices=["name", "addr"])
	ap.add_argument("--side", required=True, choices=["s1", "pool"])
	ap.add_argument("--out-channel", required=True)
	ap.add_argument("--only-empty-address", action="store_true",
	help="pool side only: restrict to records with no address (F4)")
	ap.add_argument("--batch", type=int, default=1024)
	ap.add_argument("--max-len", type=int, default=64)
	ap.add_argument("--device", default="cuda")
	a = ap.parse_args()

	import torch
	from transformers import AutoModel, AutoTokenizer

	d = os.path.dirname(P.work("prep", a.split, "_"))
	src = "source1.parquet" if a.side == "s1" else "pool.parquet"
	df = pd.read_parquet(os.path.join(d, src))

	keep = np.arange(len(df))
	if a.only_empty_address:
	assert a.side == "pool", "--only-empty-address applies to the pool side"
	keep = np.flatnonzero((df["business_address"].astype(str).str.strip() == "").to_numpy())
	df = df.iloc[keep].reset_index(drop=True)
	print(f" restricted to {len(df):,} address-less records "
	f"({len(df)/len(keep) if len(keep) else 0:.0%} of the filter set)")

	texts = build_texts(df, a.view)
	print(f"{a.out_channel}/{a.split}_{a.side}: {len(texts):,} texts, view={a.view}")
	print(f" e.g. {texts[0][:90]!r}")

	tok = AutoTokenizer.from_pretrained(MODEL)
	model = AutoModel.from_pretrained(MODEL, dtype=torch.float16).eval().to(a.device)

	# Length-sort so a batch pads to its own longest member. On single-field text most rows
	# are very short and the corpus maximum is not representative of anything.
	order = np.argsort([len(t) for t in texts], kind="stable")
	out = np.empty((len(texts), 384), dtype=np.float16)

	# Checkpointing. This is a ~30 minute pass on a card shared with 25 other users, so it
	# will occasionally be killed. The counter is a position in the LENGTH-SORTED order,
	# which is the only order in which completed work forms a contiguous prefix.
	dest_early = os.path.join(P.EMB, a.out_channel, f"{a.split}_{a.side}")
	os.makedirs(dest_early, exist_ok=True)
	ck = os.path.join(dest_early, "partial")
	start = 0
	if os.path.exists(ck + ".json"):
	meta_ck = json.load(open(ck + ".json"))
	if meta_ck.get("n") == len(texts) and meta_ck.get("view") == a.view:
	start = int(meta_ck["done"])
	out = np.load(ck + ".npy")
	print(f" resuming at {start:,}/{len(texts):,}")
	else:
	print(" checkpoint is for a different input; ignoring")
	t0 = time.time()
	with torch.inference_mode():
	for s in range(start, len(order), a.batch):
	idx = order[s:s + a.batch]
	enc = tok([texts[i] for i in idx], padding=True, truncation=True,
	max_length=a.max_len, return_tensors="pt").to(a.device)
	h = model(**enc).last_hidden_state
	m = enc["attention_mask"].unsqueeze(-1).to(h.dtype)
	v = (h * m).sum(1) / m.sum(1).clamp(min=1e-6) # mean pool, as e5 expects
	v = torch.nn.functional.normalize(v.float(), dim=1)
	out[idx] = v.half().cpu().numpy()
	if (s // a.batch) % 200 == 0 and s > start:
	np.save(ck + ".npy", out)
	json.dump({"done": int(s), "n": len(texts), "view": a.view},
	open(ck + ".json", "w"))
	if (s // a.batch) % 200 == 0:
	done = (s + len(idx)) / len(order)
	el = time.time() - t0
	print(f" {s+len(idx):>10,}/{len(order):,} ({done:6.1%}) "
	f"{(s+len(idx))/max(el,1e-9):,.0f} rows/s "
	f"eta {el/max(done,1e-9)-el:6.0f}s", flush=True)

	dest = P.emb_dir(a.out_channel, a.split, a.side) \
	if a.out_channel in P.CHANNELS else \
	os.path.join(P.EMB, a.out_channel, f"{a.split}_{a.side}")
	os.makedirs(dest, exist_ok=True)
	np.save(os.path.join(dest, "matrix.npy"), out)
	for ext in (".npy", ".json"): # the run finished; drop the partial
	if os.path.exists(ck + ext):
	os.remove(ck + ext)
	with open(os.path.join(dest, "ids.txt"), "w") as fh:
	fh.write("\n".join(df["entity_id"].astype(str)) + "\n")
	# The row->original-index map. F4's pool matrix covers only address-less records, so its
	# row i is NOT pool row i; anything consuming it must remap or every candidate index is
	# wrong. Saving it here is what makes that remap possible at all.
	np.save(os.path.join(dest, "rows.npy"), keep.astype(np.int64))
	json.dump({"model": MODEL, "prefix": PREFIX, "view": a.view, "side": a.side,
	"n": len(df), "dim": 384, "subset": bool(a.only_empty_address),
	"seconds": round(time.time() - t0, 1)},
	open(os.path.join(dest, "meta.json"), "w"), indent=1)
	print(f" wrote {dest} {out.shape} {time.time()-t0:.0f}s")


	if __name__ == "__main__":
	main()