update source code and pipeline
This commit is contained in:
560
filter/analyze_labels.py
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560
filter/analyze_labels.py
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@@ -0,0 +1,560 @@
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#!/usr/bin/env python3
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"""
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Analyze 'label' fields in a JSON dataset and produce summaries.
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- Handles entries where 'label' is either an object or a list of objects.
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- Computes distributions (is_bill, profession, currency, IDs presence, handwriting/rotation).
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- Computes numeric stats (total_billed, amount_paid, remaining_payment, coverages).
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- Parses dates and shows temporal distribution.
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- Analyzes items: count, sum of amounts and coverages, and mismatches vs total_billed.
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- Emits a concise stdout summary and writes CSVs and a Markdown report.
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Usage:
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python analyze_labels.py --input 008_label_data_sample_seed_1997.json --out-dir .
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"""
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from __future__ import annotations
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import argparse
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import csv
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import json
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import math
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import re
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from collections import Counter
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from datetime import datetime
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from pathlib import Path
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from statistics import mean, median
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from typing import Any, Dict, Iterable, List, Optional, Tuple
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NUMERIC_FIELDS = [
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"total_billed",
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"amount_paid",
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"remaining_payment",
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"client_part",
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"mandatory_coverage",
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"complementary_coverage",
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]
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def parse_args() -> argparse.Namespace:
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p = argparse.ArgumentParser(description="Analyze 'label' fields in JSON dataset")
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p.add_argument("--input", required=True, help="Path to JSON file (list of records)")
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p.add_argument(
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"--out-dir", default=None, help="Output directory for reports (default: alongside input)"
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)
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p.add_argument(
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"--max-professions", type=int, default=50, help="Max professions to list in report"
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)
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p.add_argument(
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"--no-plots",
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action="store_true",
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help="Disable generating plots (PNG) and embedding into report",
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)
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p.add_argument(
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"--plot-top-k",
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type=int,
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default=20,
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help="Top-K categories to visualize for profession/currency",
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)
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p.add_argument(
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"--plot-format",
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type=str,
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default="png",
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choices=["png", "jpg", "jpeg"],
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help="Image format for plots",
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)
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return p.parse_args()
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def load_json(path: Path) -> List[Dict[str, Any]]:
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with path.open("r", encoding="utf-8") as f:
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data = json.load(f)
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if not isinstance(data, list):
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raise ValueError("Top-level JSON must be a list of records")
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return data
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def to_bool(value: Any) -> Optional[bool]:
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if value is None:
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return None
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if isinstance(value, bool):
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return value
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if isinstance(value, (int, float)):
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return bool(value)
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if isinstance(value, str):
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v = value.strip().lower()
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if v in {"true", "t", "1", "yes", "y"}:
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return True
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if v in {"false", "f", "0", "no", "n"}:
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return False
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return None
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def to_float(value: Any) -> Optional[float]:
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if value is None or value == "":
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return None
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try:
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return float(value)
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except (TypeError, ValueError):
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return None
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def parse_date(value: Any) -> Optional[datetime]:
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if not value or not isinstance(value, str):
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return None
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s = value.strip()
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if not s:
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return None
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# Common patterns (day-first)
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fmts = [
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"%d-%m-%Y",
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"%d/%m/%Y",
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"%Y-%m-%d",
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"%d-%m-%y",
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"%d/%m/%y",
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]
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for fmt in fmts:
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try:
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return datetime.strptime(s, fmt)
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except ValueError:
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pass
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# Try to extract a date-like token using regex (e.g., 2025-02-07 or 07-02-2025)
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m = re.search(r"(\d{2}[/-]\d{2}[/-]\d{4}|\d{4}-\d{2}-\d{2})", s)
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if m:
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token = m.group(1)
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for fmt in fmts:
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try:
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return datetime.strptime(token, fmt)
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except ValueError:
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continue
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return None
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def safe_get(d: Dict[str, Any], key: str, default=None):
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return d.get(key, default)
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def flatten_labels(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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out: List[Dict[str, Any]] = []
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for rec in records:
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src_image = rec.get("image") or ",".join(rec.get("image_files", []) or [])
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label = rec.get("label")
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if label is None:
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continue
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if isinstance(label, list):
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for idx, lab in enumerate(label):
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if not isinstance(lab, dict):
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continue
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o = dict(lab)
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o["__source_image__"] = src_image
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o["__multi_index__"] = idx
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out.append(o)
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elif isinstance(label, dict):
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o = dict(label)
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o["__source_image__"] = src_image
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out.append(o)
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return out
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def presence_counts(labels: List[Dict[str, Any]], fields: Iterable[str]) -> Dict[str, int]:
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counts: Dict[str, int] = {}
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for field in fields:
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present = 0
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for lbl in labels:
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if safe_get(lbl, field) not in (None, ""):
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present += 1
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counts[field] = present
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return counts
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def numeric_summary(values: List[Optional[float]]) -> Dict[str, Any]:
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clean = [v for v in values if isinstance(v, (int, float)) and not math.isnan(v)]
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if not clean:
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return {"count": 0}
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return {
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"count": len(clean),
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"min": min(clean),
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"p25": percentile(clean, 25),
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"median": median(clean),
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"p75": percentile(clean, 75),
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"max": max(clean),
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"mean": mean(clean),
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"sum": sum(clean),
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"missing": len(values) - len(clean),
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}
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def percentile(arr: List[float], p: float) -> float:
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if not arr:
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return float("nan")
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a = sorted(arr)
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k = (len(a) - 1) * (p / 100.0)
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f = math.floor(k)
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c = math.ceil(k)
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if f == c:
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return a[int(k)]
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d0 = a[f] * (c - k)
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d1 = a[c] * (k - f)
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return d0 + d1
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def write_csv(path: Path, headers: List[str], rows: Iterable[Iterable[Any]]) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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with path.open("w", encoding="utf-8", newline="") as f:
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w = csv.writer(f)
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w.writerow(headers)
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for row in rows:
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w.writerow(row)
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def try_import_matplotlib():
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try:
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import matplotlib # type: ignore[import-not-found]
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matplotlib.use("Agg") # headless backend
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import matplotlib.pyplot as plt # type: ignore[import-not-found]
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return plt
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except Exception:
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return None
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def save_bar_plot(plt, x_labels: List[str], values: List[float], title: str, out_path: Path, rotation: int = 0):
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out_path.parent.mkdir(parents=True, exist_ok=True)
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fig, ax = plt.subplots(figsize=(max(6, min(14, 0.4 * len(x_labels) + 3)), 4))
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ax.bar(range(len(values)), values, color="#4C78A8")
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ax.set_title(title)
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ax.set_ylabel("count")
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ax.set_xticks(range(len(x_labels)))
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ax.set_xticklabels(x_labels, rotation=rotation, ha="right" if rotation else "center")
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fig.tight_layout()
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fig.savefig(out_path, dpi=150)
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plt.close(fig)
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def save_hist_plot(plt, values: List[float], title: str, out_path: Path, bins: int = 30):
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out_path.parent.mkdir(parents=True, exist_ok=True)
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fig, ax = plt.subplots(figsize=(7, 4))
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ax.hist(values, bins=bins, color="#72B7B2", edgecolor="white")
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ax.set_title(title)
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ax.set_ylabel("count")
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ax.set_xlabel("value")
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fig.tight_layout()
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fig.savefig(out_path, dpi=150)
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plt.close(fig)
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def produce_plots(
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out_dir: Path,
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args: argparse.Namespace,
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is_bill_counter: Counter,
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bill_paid_counter: Counter,
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handwriting_counter: Counter,
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rotation_counter: Counter,
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profession_counter: Counter,
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currency_counter: Counter,
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year_month_counter: Counter,
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numeric_data: Dict[str, List[Optional[float]]],
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items_per_label: List[int],
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) -> List[Path]:
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"""Generate plots and return list of created file paths."""
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if args.no_plots:
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return []
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plt = try_import_matplotlib()
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if plt is None:
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# matplotlib not available; skip plotting gracefully
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return []
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created: List[Path] = []
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plots_dir = out_dir / "plots"
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ext = args.plot_format
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# is_bill
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if is_bill_counter:
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labels = [str(k) for k, _ in is_bill_counter.items()]
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vals = [v for _, v in is_bill_counter.items()]
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p = plots_dir / f"is_bill.{ext}"
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save_bar_plot(plt, labels, vals, "is_bill distribution", p)
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created.append(p)
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# bill_paid
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if bill_paid_counter:
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labels = [str(k) for k, _ in bill_paid_counter.items()]
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vals = [v for _, v in bill_paid_counter.items()]
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p = plots_dir / f"bill_paid.{ext}"
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save_bar_plot(plt, labels, vals, "bill_paid distribution", p)
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created.append(p)
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# Flags
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if handwriting_counter:
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labels = [str(k) for k, _ in handwriting_counter.items()]
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vals = [v for _, v in handwriting_counter.items()]
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p = plots_dir / f"is_handwriting.{ext}"
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save_bar_plot(plt, labels, vals, "is_handwriting", p)
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created.append(p)
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if rotation_counter:
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labels = [str(k) for k, _ in rotation_counter.items()]
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vals = [v for _, v in rotation_counter.items()]
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p = plots_dir / f"is_rotated.{ext}"
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save_bar_plot(plt, labels, vals, "is_rotated", p)
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created.append(p)
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# Professions (top-K)
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if profession_counter:
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top = profession_counter.most_common(max(1, min(args.plot_top_k, len(profession_counter))))
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labels = [k if len(str(k)) <= 20 else str(k)[:17] + "…" for k, _ in top]
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vals = [v for _, v in top]
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p = plots_dir / f"professions_top{len(labels)}.{ext}"
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save_bar_plot(plt, labels, vals, f"Top {len(labels)} professions", p, rotation=45)
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created.append(p)
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# Currency
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if currency_counter:
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top = currency_counter.most_common(max(1, min(args.plot_top_k, len(currency_counter))))
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labels = [str(k) for k, _ in top]
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vals = [v for _, v in top]
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p = plots_dir / f"currency.{ext}"
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save_bar_plot(plt, labels, vals, "Currency distribution", p)
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created.append(p)
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# Year-month
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if year_month_counter:
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items = sorted(year_month_counter.items(), key=lambda x: (x[0][0], x[0][1]))
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labels = [f"{y:04d}-{m:02d}" for (y, m), _ in items]
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vals = [v for _, v in items]
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p = plots_dir / f"invoice_year_month.{ext}"
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save_bar_plot(plt, labels, vals, "Invoices by year-month", p, rotation=45)
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created.append(p)
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# Items per label
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if items_per_label:
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p = plots_dir / f"items_per_label.{ext}"
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save_hist_plot(plt, items_per_label, "Items per label (histogram)", p, bins=min(30, max(5, int(len(items_per_label) ** 0.5))))
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created.append(p)
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# Numeric fields histograms
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for k, vals_all in numeric_data.items():
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vals = [float(v) for v in vals_all if isinstance(v, (int, float)) and not math.isnan(v)]
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if not vals:
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continue
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p = plots_dir / f"hist_{k}.{ext}"
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save_hist_plot(plt, vals, f"{k} (histogram)", p)
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created.append(p)
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return created
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def main() -> None:
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args = parse_args()
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in_path = Path(args.input).resolve()
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out_dir = Path(args.out_dir).resolve() if args.out_dir else in_path.parent
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out_dir.mkdir(parents=True, exist_ok=True)
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records = load_json(in_path)
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labels = flatten_labels(records)
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n_total_rec = len(records)
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n_labels = len(labels)
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# Normalize some fields
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for lbl in labels:
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lbl["is_bill"] = to_bool(lbl.get("is_bill"))
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lbl["bill_paid"] = to_bool(lbl.get("bill_paid"))
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# Normalize numeric fields in-place for ease of stats
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for k in NUMERIC_FIELDS:
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lbl[k] = to_float(lbl.get(k))
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# Basic distributions
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is_bill_counter = Counter(lbl.get("is_bill") for lbl in labels)
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bill_paid_counter = Counter(lbl.get("bill_paid") for lbl in labels)
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currency_counter = Counter(lbl.get("currency") for lbl in labels if lbl.get("currency"))
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profession_counter = Counter((lbl.get("profession") or "").strip() or "(missing)" for lbl in labels)
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# Presence of identifiers and key fields
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id_presence = presence_counts(labels, [
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"adeli_number",
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"rpps_number",
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"finess_number",
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"prescripteur_finess_number",
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"doctor_name",
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"invoice_issuer",
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"insured_name",
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"beneficiary_name",
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"security_number",
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"currency",
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])
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# Handwriting/rotation flags
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handwriting_counter = Counter(to_bool(lbl.get("is_handwriting")) for lbl in labels)
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rotation_counter = Counter(to_bool(lbl.get("is_rotated")) for lbl in labels)
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# Numeric stats
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numeric_stats: Dict[str, Dict[str, Any]] = {}
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for k in NUMERIC_FIELDS:
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numeric_stats[k] = numeric_summary([lbl.get(k) for lbl in labels])
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# Keep raw numeric data for histograms
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numeric_raw: Dict[str, List[Optional[float]]] = {k: [lbl.get(k) for lbl in labels] for k in NUMERIC_FIELDS}
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# Dates
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invoice_dates = [parse_date(lbl.get("invoice_date")) for lbl in labels]
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invoice_dates_clean = [d for d in invoice_dates if d is not None]
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year_month_counter = Counter((d.year, d.month) for d in invoice_dates_clean)
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# Items analysis
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items_per_label: List[int] = []
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sum_item_amount: List[Optional[float]] = []
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sum_item_mandatory: List[Optional[float]] = []
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mismatch_records: List[Tuple[str, Optional[float], Optional[float], Optional[float]]] = []
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for lbl in labels:
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items = lbl.get("items") or []
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if not isinstance(items, list):
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items = []
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items_per_label.append(len(items))
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s_amount = None
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s_mand = None
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for it in items:
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if not isinstance(it, dict):
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continue
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a = to_float(it.get("amount"))
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m = to_float(it.get("mandatory_coverage"))
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s_amount = (s_amount or 0.0) + (a or 0.0)
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s_mand = (s_mand or 0.0) + (m or 0.0)
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sum_item_amount.append(s_amount)
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sum_item_mandatory.append(s_mand)
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total_billed = to_float(lbl.get("total_billed"))
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if total_billed is not None and s_amount is not None:
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diff = total_billed - s_amount
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if abs(diff) > 1e-6:
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mismatch_records.append((
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str(lbl.get("__source_image__")), total_billed, s_amount, diff
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))
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# Data quality issues
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issues: List[Dict[str, Any]] = []
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for lbl in labels:
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src = str(lbl.get("__source_image__"))
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# is_bill must be True/False or None (unknown); flag strings that could not be parsed
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if "is_bill" in lbl and not isinstance(lbl.get("is_bill"), (bool, type(None))):
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issues.append({"source": src, "issue": "is_bill not boolean"})
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# bill_paid True but amount_paid missing
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if lbl.get("bill_paid") is True and to_float(lbl.get("amount_paid")) is None:
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issues.append({"source": src, "issue": "bill_paid True but amount_paid missing"})
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# remaining_payment > 0 but bill_paid True
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rp = to_float(lbl.get("remaining_payment"))
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if lbl.get("bill_paid") is True and (rp or 0) > 0:
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issues.append({"source": src, "issue": "bill_paid True but remaining_payment > 0"})
|
||||
# Negative or zero amounts on items
|
||||
items = lbl.get("items") or []
|
||||
if isinstance(items, list):
|
||||
for idx, it in enumerate(items):
|
||||
if not isinstance(it, dict):
|
||||
continue
|
||||
a = to_float(it.get("amount"))
|
||||
if a is not None and a < 0:
|
||||
issues.append({"source": src, "issue": f"item[{idx}].amount negative: {a}"})
|
||||
q = to_float(it.get("quantity"))
|
||||
if q is None:
|
||||
# Not strictly an issue, but mark for completeness
|
||||
issues.append({"source": src, "issue": f"item[{idx}].quantity missing"})
|
||||
# Missing currency on bill
|
||||
if lbl.get("is_bill") is True and not lbl.get("currency"):
|
||||
issues.append({"source": src, "issue": "currency missing for bill"})
|
||||
|
||||
# Outputs
|
||||
# 1) CSVs
|
||||
write_csv(out_dir / "professions_counts.csv", ["profession", "count"], profession_counter.most_common())
|
||||
write_csv(out_dir / "currency_counts.csv", ["currency", "count"], currency_counter.most_common())
|
||||
write_csv(out_dir / "is_bill_counts.csv", ["is_bill", "count"], is_bill_counter.items())
|
||||
write_csv(out_dir / "bill_paid_counts.csv", ["bill_paid", "count"], bill_paid_counter.items())
|
||||
write_csv(out_dir / "id_presence.csv", ["field", "present_count"], id_presence.items())
|
||||
write_csv(out_dir / "item_total_billed_mismatches.csv", ["source_image", "total_billed", "sum_item_amount", "diff"], mismatch_records)
|
||||
write_csv(out_dir / "issues.csv", ["source", "issue"], ((i["source"], i["issue"]) for i in issues))
|
||||
|
||||
# 2) Markdown report
|
||||
md = []
|
||||
md.append("# Label Analysis Report\n")
|
||||
md.append(f"Input: `{in_path.name}`\n")
|
||||
md.append("")
|
||||
md.append("## Overview\n")
|
||||
md.append(f"- Total records: {n_total_rec}")
|
||||
md.append(f"- Total labels (flattened): {n_labels}")
|
||||
md.append(f"- is_bill distribution: {dict(is_bill_counter)}")
|
||||
md.append(f"- bill_paid distribution: {dict(bill_paid_counter)}")
|
||||
if invoice_dates_clean:
|
||||
md.append(
|
||||
f"- Invoice dates span: {min(invoice_dates_clean).date()} .. {max(invoice_dates_clean).date()}"
|
||||
)
|
||||
md.append(f"- Unique year-month pairs: {len(year_month_counter)}")
|
||||
else:
|
||||
md.append("- Invoice dates: none parseable")
|
||||
|
||||
md.append("\n## Professions (top)\n")
|
||||
for prof, cnt in profession_counter.most_common(args.max_professions):
|
||||
md.append(f"- {prof}: {cnt}")
|
||||
|
||||
md.append("\n## Currency distribution\n")
|
||||
for cur, cnt in currency_counter.most_common():
|
||||
md.append(f"- {cur}: {cnt}")
|
||||
|
||||
md.append("\n## Identifier and key field presence\n")
|
||||
for k, v in id_presence.items():
|
||||
md.append(f"- {k}: {v} present")
|
||||
|
||||
md.append("\n## Flags\n")
|
||||
md.append(f"- is_handwriting: {dict(handwriting_counter)}")
|
||||
md.append(f"- is_rotated: {dict(rotation_counter)}")
|
||||
|
||||
md.append("\n## Numeric summaries\n")
|
||||
for k, stats in numeric_stats.items():
|
||||
md.append(f"- {k}: {stats}")
|
||||
|
||||
if items_per_label:
|
||||
md.append("\n## Items analysis\n")
|
||||
md.append(f"- Items per label: count={len(items_per_label)}, min={min(items_per_label)}, max={max(items_per_label)}, mean={mean(items_per_label):.2f}")
|
||||
n_mismatch = len(mismatch_records)
|
||||
md.append(f"- total_billed vs sum(items.amount) mismatches: {n_mismatch}")
|
||||
|
||||
if issues:
|
||||
md.append("\n## Data quality issues (sample)\n")
|
||||
for row in issues[:50]:
|
||||
md.append(f"- {row['source']}: {row['issue']}")
|
||||
|
||||
# 3) Plots (if enabled)
|
||||
created_plots = produce_plots(
|
||||
out_dir=out_dir,
|
||||
args=args,
|
||||
is_bill_counter=is_bill_counter,
|
||||
bill_paid_counter=bill_paid_counter,
|
||||
handwriting_counter=handwriting_counter,
|
||||
rotation_counter=rotation_counter,
|
||||
profession_counter=profession_counter,
|
||||
currency_counter=currency_counter,
|
||||
year_month_counter=year_month_counter,
|
||||
numeric_data=numeric_raw,
|
||||
items_per_label=items_per_label,
|
||||
)
|
||||
if created_plots:
|
||||
md.append("\n## Plots\n")
|
||||
for p in created_plots:
|
||||
rel = p.relative_to(out_dir)
|
||||
md.append(f"- {p.stem}")
|
||||
md.append(f"})\n")
|
||||
elif not args.no_plots:
|
||||
md.append("\n## Plots\n")
|
||||
md.append("- matplotlib not available or no data to plot.")
|
||||
|
||||
report_path = out_dir / "label_analysis_report.md"
|
||||
report_path.write_text("\n".join(md), encoding="utf-8")
|
||||
|
||||
# Console summary
|
||||
print("Label analysis complete.")
|
||||
print(f"- Records: {n_total_rec}, Labels: {n_labels}")
|
||||
print(f"- is_bill: {dict(is_bill_counter)} | bill_paid: {dict(bill_paid_counter)}")
|
||||
print(f"- Professions (top 10): {profession_counter.most_common(10)}")
|
||||
print(f"- Currency: {dict(currency_counter)}")
|
||||
print(f"Report written to: {report_path}")
|
||||
if created_plots:
|
||||
print(f"- Plots saved under: {(out_dir / 'plots').as_posix()} ({len(created_plots)} files)")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
4
filter/check_008/bill_paid_counts.csv
Normal file
4
filter/check_008/bill_paid_counts.csv
Normal file
@@ -0,0 +1,4 @@
|
||||
bill_paid,count
|
||||
False,68
|
||||
True,1015
|
||||
,180
|
||||
|
3
filter/check_008/currency_counts.csv
Normal file
3
filter/check_008/currency_counts.csv
Normal file
@@ -0,0 +1,3 @@
|
||||
currency,count
|
||||
EUR,1135
|
||||
F,2
|
||||
|
11
filter/check_008/id_presence.csv
Normal file
11
filter/check_008/id_presence.csv
Normal file
@@ -0,0 +1,11 @@
|
||||
field,present_count
|
||||
adeli_number,898
|
||||
rpps_number,182
|
||||
finess_number,139
|
||||
prescripteur_finess_number,41
|
||||
doctor_name,1040
|
||||
invoice_issuer,1120
|
||||
insured_name,208
|
||||
beneficiary_name,1093
|
||||
security_number,492
|
||||
currency,1137
|
||||
|
3
filter/check_008/is_bill_counts.csv
Normal file
3
filter/check_008/is_bill_counts.csv
Normal file
@@ -0,0 +1,3 @@
|
||||
is_bill,count
|
||||
True,1124
|
||||
False,139
|
||||
|
279
filter/check_008/issues.csv
Normal file
279
filter/check_008/issues.csv
Normal file
@@ -0,0 +1,279 @@
|
||||
source,issue
|
||||
Facture médecine douce-27776417,item[0].quantity missing
|
||||
551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024,item[0].quantity missing
|
||||
551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024,item[1].quantity missing
|
||||
551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024,item[2].quantity missing
|
||||
551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024,item[3].quantity missing
|
||||
551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024,item[4].quantity missing
|
||||
551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024,item[5].quantity missing
|
||||
Facture médecine douce-27640153,item[0].quantity missing
|
||||
Facture médecine douce-27640153,item[1].quantity missing
|
||||
1724397366229Facture_RIVALLANDChrystelle_20240413ICQO,bill_paid True but amount_paid missing
|
||||
Facture ostéopathe-25417095,bill_paid True but remaining_payment > 0
|
||||
Facture ostéopathe-GED N-R-2323015,item[0].quantity missing
|
||||
74441931-f8d0-4cb0-8eb5-df4147a72bbe_mgp,item[0].quantity missing
|
||||
74441931-f8d0-4cb0-8eb5-df4147a72bbe_mgp,currency missing for bill
|
||||
74441931-f8d0-4cb0-8eb5-df4147a72bbe_mgp,currency missing for bill
|
||||
1724658126141JACOB_Francois_2024-111,item[0].quantity missing
|
||||
Facture des lentilles de contact-27559701,item[5].amount negative: -6.67
|
||||
91c41171-ac88-4ce4-8f15-48c4ad2c5aa2_Devis_lunette,item[0].quantity missing
|
||||
0252b5fb-bc84-4fca-bfa7-483ac611885a_Scan_0115,item[0].quantity missing
|
||||
0252b5fb-bc84-4fca-bfa7-483ac611885a_Scan_0115,item[1].quantity missing
|
||||
7f084bf4-357d-452d-bb4c-76bff332ea0c_IMG_7791,item[0].quantity missing
|
||||
7f084bf4-357d-452d-bb4c-76bff332ea0c_IMG_7791,item[1].quantity missing
|
||||
70c3e7f5-405b-49d8-a73c-dae83beafb59_Facture_ophtalmo,item[0].quantity missing
|
||||
70c3e7f5-405b-49d8-a73c-dae83beafb59_Facture_ophtalmo,item[1].quantity missing
|
||||
70c3e7f5-405b-49d8-a73c-dae83beafb59_Facture_ophtalmo,item[2].quantity missing
|
||||
Facture médecine douce-27776613,item[0].quantity missing
|
||||
6eeacb70-27be-40dd-9511-522025b459a3_20241226_080214,item[0].quantity missing
|
||||
6eeacb70-27be-40dd-9511-522025b459a3_20241226_080214,item[1].quantity missing
|
||||
1724399050339FACTURE_132120424_MME_RONDIN_MAHEVA,item[0].quantity missing
|
||||
ec117b21-9348-4a76-9a22-2bae87639392_facture_du_14.02_Tony,item[0].quantity missing
|
||||
ec117b21-9348-4a76-9a22-2bae87639392_facture_du_14.02_Tony,item[1].quantity missing
|
||||
Facture ostéopathe-25979892,item[0].quantity missing
|
||||
bf09a877-0705-4c79-b908-6e8da44e44c7_17490641879424403676372514736136,item[0].quantity missing
|
||||
bf09a877-0705-4c79-b908-6e8da44e44c7_17490641879424403676372514736136,item[1].quantity missing
|
||||
d4f310c8-3bcf-4bb5-bf53-27ad74d66c1e_FACTURE_OPTICIEN_OPHTALMO,item[0].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26740934,item[0].quantity missing
|
||||
45ccffcd-5fe8-418b-80c2-894086de9087_facture_ophtalmo_Fred_,item[0].quantity missing
|
||||
45ccffcd-5fe8-418b-80c2-894086de9087_facture_ophtalmo_Fred_,item[1].quantity missing
|
||||
Facture ostéopathe-26626551,item[0].quantity missing
|
||||
27ddc814-9239-4944-951a-fdd8a48c5926_IMG_20240922_0001,item[0].quantity missing
|
||||
27ddc814-9239-4944-951a-fdd8a48c5926_IMG_20240922_0001,item[1].quantity missing
|
||||
27ddc814-9239-4944-951a-fdd8a48c5926_IMG_20240922_0001,item[2].quantity missing
|
||||
27ddc814-9239-4944-951a-fdd8a48c5926_IMG_20240922_0001,item[3].quantity missing
|
||||
8eceb56a-b571-4ab6-a64e-668db6bf4ad5_chiropracteur_fevrier_25,item[0].quantity missing
|
||||
Facture des lentilles de contact-27576495,item[2].quantity missing
|
||||
32ffb8dc-fbd0-4edf-94b2-7b784cc72ff2_17479876010256548167621008372337,item[0].quantity missing
|
||||
32ffb8dc-fbd0-4edf-94b2-7b784cc72ff2_17479876010256548167621008372337,item[1].quantity missing
|
||||
32ffb8dc-fbd0-4edf-94b2-7b784cc72ff2_17479876010256548167621008372337,item[2].quantity missing
|
||||
a5bd8b97-a2a8-49ae-900d-0673d2f96637_quittance_optique_,item[0].quantity missing
|
||||
e997eddb-05a4-49f9-a7fd-82b48c2694b3_IMG_OPHTALMOLOGIE_20250117_0001,item[0].quantity missing
|
||||
Facture ostéopathe-26300731,item[0].quantity missing
|
||||
Facture ostéopathe-26300731,item[1].quantity missing
|
||||
Facture ostéopathe-26300731,item[2].quantity missing
|
||||
64fa4c19-efa4-48f1-acf8-8c2e8f573b8b_Facture_osteopathe_,item[0].quantity missing
|
||||
c7704de1-820a-44bf-aef9-f7b185f94a26_facture_ophtalmo,item[0].quantity missing
|
||||
c7704de1-820a-44bf-aef9-f7b185f94a26_facture_ophtalmo,item[1].quantity missing
|
||||
c7704de1-820a-44bf-aef9-f7b185f94a26_facture_ophtalmo,item[0].quantity missing
|
||||
1724658376419facture_ostheo_avril_24,item[0].quantity missing
|
||||
66de7232-78a6-46a0-8392-47ec4630ad31_image,item[0].quantity missing
|
||||
66de7232-78a6-46a0-8392-47ec4630ad31_image,item[1].quantity missing
|
||||
66de7232-78a6-46a0-8392-47ec4630ad31_image,item[2].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26772554,item[0].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26772554,item[1].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26705902,item[3].amount negative: -3.0
|
||||
c2009b55-58d4-411f-a25a-3dc1e32e4a89_facture_lentille,item[3].amount negative: -3.0
|
||||
Facture ostéopathe-26112640,item[0].quantity missing
|
||||
915b97eb-e7d5-415f-ae89-823f08ae3bc0_ACfacture_opthalmo,item[0].quantity missing
|
||||
915b97eb-e7d5-415f-ae89-823f08ae3bc0_ACfacture_opthalmo,item[1].quantity missing
|
||||
fa25423e-b79c-48f9-89ae-32ffd95e3101_Facture_Ophtalmologie,item[0].quantity missing
|
||||
Facture ostéopathe-25073562,item[0].quantity missing
|
||||
14a6fe3e-c49c-4544-b960-43def15eaf83_doc00563220250401124926,item[0].quantity missing
|
||||
1724399050374facture_podologue,item[0].quantity missing
|
||||
Facture des lentilles de contact-27680116,item[0].quantity missing
|
||||
Facture des lentilles de contact-27680116,item[1].quantity missing
|
||||
Facture des lentilles de contact-27551187,item[0].quantity missing
|
||||
46ddeec5-97bf-4175-b214-b7bd74c4fa24_Renouvin_Ferlicot_Andre,item[0].quantity missing
|
||||
46ddeec5-97bf-4175-b214-b7bd74c4fa24_Renouvin_Ferlicot_Andre,item[1].quantity missing
|
||||
cbc7ddff-2ed1-441b-85cb-5c1079588a52_Mes_des_factures,item[0].quantity missing
|
||||
cbc7ddff-2ed1-441b-85cb-5c1079588a52_Mes_des_factures,item[1].quantity missing
|
||||
1724658298461facture_podologue_alicia,item[0].quantity missing
|
||||
1724397366268IMG_20240417_143900,item[0].quantity missing
|
||||
b410345a-31de-4397-a896-bc11d97e524c_FACTURE_OPHTALMOLOGISTE,item[0].quantity missing
|
||||
Facture ostéopathe-26972680,item[0].quantity missing
|
||||
Facture ostéopathe-25936311,item[0].quantity missing
|
||||
b96f6ca3-3175-4cd8-a827-e886b5799867_Facture_ophtalmologue_24022025,item[0].quantity missing
|
||||
9494858a-65e3-425d-a452-17d4300f0115_demande_de_remboursement_mgp_mutuelle_001,bill_paid True but amount_paid missing
|
||||
895777f3-4478-4e8d-a616-d498850d1523_factures-lunettes_Tess,bill_paid True but remaining_payment > 0
|
||||
Facture ostéopathe-25152747,bill_paid True but amount_paid missing
|
||||
Facture ostéopathe-25332784,item[0].quantity missing
|
||||
Facture ostéopathe-1216961,bill_paid True but amount_paid missing
|
||||
Facture ostéopathe-1216961,item[0].quantity missing
|
||||
6f544990-74c6-4c7e-8a25-08eb3f9138c8_RemboursementSoins01082024_1,item[0].quantity missing
|
||||
6f544990-74c6-4c7e-8a25-08eb3f9138c8_RemboursementSoins01082024_1,item[1].quantity missing
|
||||
9901adc0-96d4-4cfd-a028-8286fd1f7841_Fre_Ophtalmo,item[0].quantity missing
|
||||
9901adc0-96d4-4cfd-a028-8286fd1f7841_Fre_Ophtalmo,item[1].quantity missing
|
||||
141f7ccc-37ab-4da5-9788-80a27e819b8b_Feuilles_de_soins_Psychologue_x2,bill_paid True but remaining_payment > 0
|
||||
598221c3-d0b3-403a-a261-c68cbe56b453_facture_ophtalmo,item[0].quantity missing
|
||||
598221c3-d0b3-403a-a261-c68cbe56b453_facture_ophtalmo,item[1].quantity missing
|
||||
70e53730-c41c-402b-a227-2262cdd9fa7b_osteo_Laurette,item[0].quantity missing
|
||||
39384a4b-4117-4f56-8cca-55fd51e5b062_image,item[0].quantity missing
|
||||
39384a4b-4117-4f56-8cca-55fd51e5b062_image,item[1].quantity missing
|
||||
39384a4b-4117-4f56-8cca-55fd51e5b062_image,item[2].quantity missing
|
||||
d9e1cb2a-b44b-4cc8-b07a-030ddf3acd28_Ordonnance_lentilles_Mary,bill_paid True but amount_paid missing
|
||||
Facture ostéopathe-25417096,item[0].quantity missing
|
||||
Facture ostéopathe-26585743,item[0].quantity missing
|
||||
05c2ef83-54cb-419e-949f-74f898b459bd_osteo_13112024,item[0].quantity missing
|
||||
Facture ostéopathe-26077624,item[0].quantity missing
|
||||
e405dba0-fd0b-48a0-9e3f-576398fa3318_Scan2025-02-28_143544_1,item[0].quantity missing
|
||||
Facture ostéopathe-25847017,bill_paid True but remaining_payment > 0
|
||||
8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes,item[0].quantity missing
|
||||
8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes,item[1].quantity missing
|
||||
8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes,item[2].quantity missing
|
||||
8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes,item[3].quantity missing
|
||||
8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes,item[4].quantity missing
|
||||
47399c82-030f-4904-b8f4-2c3dc7c46723_image,item[0].quantity missing
|
||||
47399c82-030f-4904-b8f4-2c3dc7c46723_image,item[1].quantity missing
|
||||
Facture des lentilles de contact-27708923,item[0].quantity missing
|
||||
Facture-quittance toutes spécialités-27235184,item[0].quantity missing
|
||||
Facture-quittance toutes spécialités-27235184,item[1].quantity missing
|
||||
Facture-quittance toutes spécialités-27235184,item[2].quantity missing
|
||||
Facture-quittance toutes spécialités-27235184,item[3].quantity missing
|
||||
1724399050377psy_rachel_4,item[0].quantity missing
|
||||
e0f0eab0-f98c-4b74-95f2-bfc61ad76d28_Ophtalmo_Isa_01-24,item[0].quantity missing
|
||||
e0f0eab0-f98c-4b74-95f2-bfc61ad76d28_Ophtalmo_Isa_01-24,item[1].quantity missing
|
||||
172439712563420240417_105010,item[0].quantity missing
|
||||
172439712563420240417_105010,item[1].quantity missing
|
||||
Facture ostéopathe-26750142,item[0].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26723200,item[0].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26736761,item[0].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26736761,item[1].quantity missing
|
||||
28f8623a-8962-4ad1-88bb-4914109e1d42_20241118103848034,item[3].amount negative: -4.08
|
||||
28f8623a-8962-4ad1-88bb-4914109e1d42_20241118103848034,item[0].quantity missing
|
||||
28f8623a-8962-4ad1-88bb-4914109e1d42_20241118103848034,item[1].quantity missing
|
||||
0a26adda-beef-4977-966c-12efc73f1d15_20250129_190625,item[0].quantity missing
|
||||
0a26adda-beef-4977-966c-12efc73f1d15_20250129_190625,item[1].quantity missing
|
||||
b442261a-d504-4cda-98cd-e22a6875bfd0_M._ZIMMER_2401,item[0].quantity missing
|
||||
b8083bbb-9489-4706-af99-69f625bbc530_facture_OPHALMOLOGIE,item[0].quantity missing
|
||||
b8083bbb-9489-4706-af99-69f625bbc530_facture_OPHALMOLOGIE,item[1].quantity missing
|
||||
b8083bbb-9489-4706-af99-69f625bbc530_facture_OPHALMOLOGIE,item[2].quantity missing
|
||||
bedf599f-99db-4613-aa22-17d52690c34c_doc00194520240521104544,item[0].quantity missing
|
||||
bedf599f-99db-4613-aa22-17d52690c34c_doc00194520240521104544,item[1].quantity missing
|
||||
8f8adcc8-9b8f-48e0-94d5-d8cfb9d84a1f_Scanned_20250212_154615,item[0].quantity missing
|
||||
Facture des lentilles de contact-27647140,item[4].amount negative: -2.9
|
||||
Facture des lentilles de contact-27647140,item[5].amount negative: -3.0
|
||||
8de5aacb-e142-4321-aaaf-c4179f031d7a_20240810152224_001,item[0].quantity missing
|
||||
8de5aacb-e142-4321-aaaf-c4179f031d7a_20240810152224_001,item[1].quantity missing
|
||||
8de5aacb-e142-4321-aaaf-c4179f031d7a_20240810152224_001,item[2].quantity missing
|
||||
8de6df48-c40d-4e21-ad21-edc9a95e3bfb_image,item[0].quantity missing
|
||||
8de6df48-c40d-4e21-ad21-edc9a95e3bfb_image,item[1].quantity missing
|
||||
8de6df48-c40d-4e21-ad21-edc9a95e3bfb_image,item[2].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26542194,item[0].quantity missing
|
||||
Facture des lentilles de contact-27654407,bill_paid True but amount_paid missing
|
||||
fd8187e1-b9eb-4727-8041-9fc9e6f1affb_0556_001,item[0].quantity missing
|
||||
Facture des lentilles de contact-27572543,item[0].quantity missing
|
||||
c6013936-5850-4027-9f61-32c30486e6d5_optique,item[0].quantity missing
|
||||
c6013936-5850-4027-9f61-32c30486e6d5_optique,item[1].quantity missing
|
||||
2c75bd51-e61c-452e-b19b-494fa70e9cc9_Facture_lentilles_janvier_2024,item[4].amount negative: -2.9
|
||||
2c75bd51-e61c-452e-b19b-494fa70e9cc9_Facture_lentilles_janvier_2024,item[5].amount negative: -3.0
|
||||
Facture des lentilles de contact-27601451,item[0].quantity missing
|
||||
Facture des lentilles de contact-27601451,item[1].quantity missing
|
||||
Facture ostéopathe-26660518,item[0].quantity missing
|
||||
Facture ostéopathe-26660518,item[0].quantity missing
|
||||
Facture ostéopathe-26479073,item[0].quantity missing
|
||||
c4930995-bbbe-45dd-9afe-800005fb5890_Facture_lunettes_,item[0].quantity missing
|
||||
c4930995-bbbe-45dd-9afe-800005fb5890_Facture_lunettes_,item[1].quantity missing
|
||||
c4930995-bbbe-45dd-9afe-800005fb5890_Facture_lunettes_,item[2].quantity missing
|
||||
511ba153-1eae-4682-ade0-01217ff522bf_Facture_consultation_130125,item[0].quantity missing
|
||||
8019accc-33b4-4f20-95eb-3ff1e1d4db0b_nnn,item[0].quantity missing
|
||||
8019accc-33b4-4f20-95eb-3ff1e1d4db0b_nnn,item[1].quantity missing
|
||||
Facture ostéopathe-1119174,item[0].quantity missing
|
||||
17b264a2-4881-49a4-8326-48ace17dd86f_factures_ophtalmo,item[0].quantity missing
|
||||
17b264a2-4881-49a4-8326-48ace17dd86f_factures_ophtalmo,item[1].quantity missing
|
||||
17b264a2-4881-49a4-8326-48ace17dd86f_factures_ophtalmo,item[0].quantity missing
|
||||
eedda33e-049b-4ab8-aee8-6f5d0c88b500_IMG_0358,item[0].quantity missing
|
||||
eedda33e-049b-4ab8-aee8-6f5d0c88b500_IMG_0358,item[1].quantity missing
|
||||
Facture des lentilles de contact-27646193,item[4].amount negative: -4.9
|
||||
Facture des lentilles de contact-27646193,item[5].amount negative: -13.8
|
||||
f339c5ad-7df8-4f3e-a5fa-9dc7616abc3e_IMG_20250327_165906,item[0].quantity missing
|
||||
f339c5ad-7df8-4f3e-a5fa-9dc7616abc3e_IMG_20250327_165906,item[1].quantity missing
|
||||
4f09f855-c993-42ea-97b1-3dd728b53e16_devis_ophtalmo,item[0].quantity missing
|
||||
Facture des lentilles de contact-27665791,bill_paid True but amount_paid missing
|
||||
Facture des lentilles de contact-27665791,item[0].quantity missing
|
||||
Facture des lentilles de contact-27665791,item[1].quantity missing
|
||||
Facture des lentilles de contact-27665791,item[2].quantity missing
|
||||
252c4ad4-8fa3-4ed1-9508-ee77f6dce6fe_img20250206_20040851,item[0].quantity missing
|
||||
Facture ostéopathe-25145711,item[0].quantity missing
|
||||
Facture ostéopathe-26626836,item[0].quantity missing
|
||||
Facture ostéopathe-26626836,item[0].quantity missing
|
||||
Facture ostéopathe-26991829,item[0].quantity missing
|
||||
Facture ostéopathe-26991829,item[1].quantity missing
|
||||
f6db258d-f400-41b5-a5ca-7878010f0fd7_doc00945920250206091811,item[0].quantity missing
|
||||
Facture médecine douce-27784833,item[0].quantity missing
|
||||
abff81b4-d895-47d5-b3d4-93da48f5c1e6_20241212_172044,item[0].quantity missing
|
||||
abff81b4-d895-47d5-b3d4-93da48f5c1e6_20241212_172044,item[1].quantity missing
|
||||
1724397366294irm,item[0].quantity missing
|
||||
Facture ostéopathe-25233929,item[0].quantity missing
|
||||
8b6539db-21b4-41dc-a27f-db2cd351fe5f_Doc_34,item[0].quantity missing
|
||||
819f6faf-d9f3-4668-9000-e414d92c7f41_ROYER_LUDIVINE_FACTURE_OPHTALMO,item[0].quantity missing
|
||||
819f6faf-d9f3-4668-9000-e414d92c7f41_ROYER_LUDIVINE_FACTURE_OPHTALMO,item[1].quantity missing
|
||||
209e4abe-4973-4fd3-83cc-64f940fb7372_facture_ophtalmologue,item[0].quantity missing
|
||||
209e4abe-4973-4fd3-83cc-64f940fb7372_facture_ophtalmologue,item[1].quantity missing
|
||||
719ba06c-91ee-44dc-b7b6-4a3132f733af_FievreVillaniCarlaLunettes,item[0].quantity missing
|
||||
719ba06c-91ee-44dc-b7b6-4a3132f733af_FievreVillaniCarlaLunettes,item[1].quantity missing
|
||||
e07571f8-3ba4-46fc-a37e-53bd6dd965c7_IMG20250123190152,item[0].quantity missing
|
||||
e07571f8-3ba4-46fc-a37e-53bd6dd965c7_IMG20250123190152,item[1].quantity missing
|
||||
e07571f8-3ba4-46fc-a37e-53bd6dd965c7_IMG20250123190152,item[2].quantity missing
|
||||
e07571f8-3ba4-46fc-a37e-53bd6dd965c7_IMG20250123190152,item[3].quantity missing
|
||||
e07571f8-3ba4-46fc-a37e-53bd6dd965c7_IMG20250123190152,item[4].quantity missing
|
||||
e07571f8-3ba4-46fc-a37e-53bd6dd965c7_IMG20250123190152,item[5].quantity missing
|
||||
e07571f8-3ba4-46fc-a37e-53bd6dd965c7_IMG20250123190152,item[6].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26772555,item[0].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26772555,item[1].quantity missing
|
||||
5ff72751-5d92-463c-a272-97fda77c34f8_2025-01-31-13187-Luc_Lisa,item[0].quantity missing
|
||||
Facture ostéopathe-26744045,item[0].quantity missing
|
||||
Facture ostéopathe-26699212,item[0].quantity missing
|
||||
Facture médecine douce-27781814,item[0].quantity missing
|
||||
Facture ostéopathe-26699279,item[0].quantity missing
|
||||
1724399050308image,item[0].quantity missing
|
||||
44e87f9e-a4e2-4851-b383-e1401b36e853_image,item[0].quantity missing
|
||||
44e87f9e-a4e2-4851-b383-e1401b36e853_image,item[1].quantity missing
|
||||
c53ffdcc-0ee6-4bf2-8c43-923c5471310f_IMG_7285,item[0].quantity missing
|
||||
c53ffdcc-0ee6-4bf2-8c43-923c5471310f_IMG_7285,item[1].quantity missing
|
||||
8605959d-bf31-4ea3-a820-bde824a1a4de_20241226_080232,item[0].quantity missing
|
||||
3ee0ac3b-4d77-4692-80fa-15004f8a3499_facture_ophta,item[0].quantity missing
|
||||
3ee0ac3b-4d77-4692-80fa-15004f8a3499_facture_ophta,item[1].quantity missing
|
||||
29d72de2-da83-483a-8702-939fbe4addfa_20250207_112449,item[0].quantity missing
|
||||
1724425079263JACOB_Francois_2024-111,item[0].quantity missing
|
||||
1724425079184Facture_Osteopathe_Brousseau_sader_17avr24,item[0].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26685394,item[0].quantity missing
|
||||
Facture ostéopathe-26447822,bill_paid True but amount_paid missing
|
||||
DDE prestation Facture des lentilles de contact-26705131,item[2].amount negative: -5.0
|
||||
3fe044b5-fd6a-4f5c-accc-ce7b987575a8_IMG_6817,item[0].quantity missing
|
||||
3fe044b5-fd6a-4f5c-accc-ce7b987575a8_IMG_6817,item[1].quantity missing
|
||||
Facture ostéopathe-25187936,item[0].quantity missing
|
||||
Facture des lentilles de contact-27545715,item[1].quantity missing
|
||||
82ac654c-54d4-401f-a006-96d9e46531fe_Facture_ostheo_JEAN,item[0].quantity missing
|
||||
Facture des lentilles de contact-27653858,item[4].amount negative: -2.9
|
||||
Facture des lentilles de contact-27653858,item[5].amount negative: -3.0
|
||||
3d4c57ed-eecc-4557-81c9-9e46e46b5a82_IMG_8797,item[0].quantity missing
|
||||
3d4c57ed-eecc-4557-81c9-9e46e46b5a82_IMG_8797,item[1].quantity missing
|
||||
3d4c57ed-eecc-4557-81c9-9e46e46b5a82_IMG_8797,item[2].quantity missing
|
||||
3d4c57ed-eecc-4557-81c9-9e46e46b5a82_IMG_8797,item[3].quantity missing
|
||||
3d4c57ed-eecc-4557-81c9-9e46e46b5a82_IMG_8797,item[4].quantity missing
|
||||
3d4c57ed-eecc-4557-81c9-9e46e46b5a82_IMG_8797,item[5].quantity missing
|
||||
Facture ostéopathe-26675471,item[0].quantity missing
|
||||
38b89b50-a6b6-4816-a495-ce2d9e71082f_Kine-28-11-2024,item[0].quantity missing
|
||||
43666807-3eb2-40b9-bdf6-94d4fe31b71e_factures,item[0].quantity missing
|
||||
43666807-3eb2-40b9-bdf6-94d4fe31b71e_factures,item[1].quantity missing
|
||||
43666807-3eb2-40b9-bdf6-94d4fe31b71e_factures,item[2].quantity missing
|
||||
9c1cc24a-5f7e-42ef-998d-c813a686a7b5_549D8459-524A-4A5C-9612-09EEA1ADDB58,item[0].quantity missing
|
||||
9c1cc24a-5f7e-42ef-998d-c813a686a7b5_549D8459-524A-4A5C-9612-09EEA1ADDB58,item[1].quantity missing
|
||||
c052b9e0-c0d4-473f-93a9-a5dc396a0d86_Facture_ophtalmologique_,item[0].quantity missing
|
||||
c052b9e0-c0d4-473f-93a9-a5dc396a0d86_Facture_ophtalmologique_,item[1].quantity missing
|
||||
ec6baa8c-a189-4850-b310-c90bab372cc2_Facture_Krys,bill_paid True but remaining_payment > 0
|
||||
Facture ostéopathe-26539711,item[0].quantity missing
|
||||
0bfb5041-2751-421d-b5d5-60d696c361c1_17391960081576654684571490136238,item[0].quantity missing
|
||||
Facture ostéopathe-26605349,item[0].quantity missing
|
||||
de6a66cd-d729-479c-a119-8b9e442702db_Screenshot_20250227_112521_Gallery,item[0].quantity missing
|
||||
de6a66cd-d729-479c-a119-8b9e442702db_Screenshot_20250227_112521_Gallery,item[1].quantity missing
|
||||
1d4346b3-1322-457d-9436-ea1d82f74cea_Facture_ophtalmo_enfants,item[0].quantity missing
|
||||
1d4346b3-1322-457d-9436-ea1d82f74cea_Facture_ophtalmo_enfants,item[0].quantity missing
|
||||
1d4346b3-1322-457d-9436-ea1d82f74cea_Facture_ophtalmo_enfants,item[1].quantity missing
|
||||
Facture ostéopathe-25358482,item[0].quantity missing
|
||||
34f7e622-ffd8-493d-b955-10a692290a75_image,item[0].quantity missing
|
||||
Facture ostéopathe-26595219,item[0].quantity missing
|
||||
Facture des lentilles de contact-27716093,item[0].quantity missing
|
||||
6d9a0fdf-c334-43b6-946b-45b1b1ab83d7_Facture_osteo_FEV_25,item[0].quantity missing
|
||||
24ca9fa1-064d-49a0-8d54-d6f0171c8187_Facture_lunette,item[0].quantity missing
|
||||
24ca9fa1-064d-49a0-8d54-d6f0171c8187_Facture_lunette,item[1].quantity missing
|
||||
24ca9fa1-064d-49a0-8d54-d6f0171c8187_Facture_lunette,item[2].quantity missing
|
||||
24ca9fa1-064d-49a0-8d54-d6f0171c8187_Facture_lunette,item[3].quantity missing
|
||||
1724399050380osteo_Lina,item[0].quantity missing
|
||||
106b84f7-29a1-4c06-8cdf-4a297995e678_Facture_lunettes_Amelie,item[0].quantity missing
|
||||
106b84f7-29a1-4c06-8cdf-4a297995e678_Facture_lunettes_Amelie,item[1].quantity missing
|
||||
106b84f7-29a1-4c06-8cdf-4a297995e678_Facture_lunettes_Amelie,item[2].quantity missing
|
||||
106b84f7-29a1-4c06-8cdf-4a297995e678_Facture_lunettes_Amelie,item[3].quantity missing
|
||||
106b84f7-29a1-4c06-8cdf-4a297995e678_Facture_lunettes_Amelie,item[4].quantity missing
|
||||
106b84f7-29a1-4c06-8cdf-4a297995e678_Facture_lunettes_Amelie,item[5].quantity missing
|
||||
|
54
filter/check_008/item_total_billed_mismatches.csv
Normal file
54
filter/check_008/item_total_billed_mismatches.csv
Normal file
@@ -0,0 +1,54 @@
|
||||
source_image,total_billed,sum_item_amount,diff
|
||||
74441931-f8d0-4cb0-8eb5-df4147a72bbe_mgp,71.9,0.0,71.9
|
||||
Facture des lentilles de contact-27718628,201.52,223.92,-22.399999999999977
|
||||
7f084bf4-357d-452d-bb4c-76bff332ea0c_IMG_7791,78.43,0.0,78.43
|
||||
6eeacb70-27be-40dd-9511-522025b459a3_20241226_080214,65.5,65.05000000000001,0.44999999999998863
|
||||
Facture ostéopathe-1066603,45.0,65.0,-20.0
|
||||
fa95b732-7a88-4fea-a8e7-9975dbbc69a8_Facture_0122202407740700218,238.03,218.03000000000003,19.99999999999997
|
||||
cc0c0b33-1a94-469b-bd88-ca534e257d33_Osteo,60.0,80.0,-20.0
|
||||
32ffb8dc-fbd0-4edf-94b2-7b784cc72ff2_17479876010256548167621008372337,92.4,75.72,16.680000000000007
|
||||
e997eddb-05a4-49f9-a7fd-82b48c2694b3_IMG_OPHTALMOLOGIE_20250117_0001,119.48,0.0,119.48
|
||||
c7704de1-820a-44bf-aef9-f7b185f94a26_facture_ophtalmo,120.0,0.0,120.0
|
||||
c7704de1-820a-44bf-aef9-f7b185f94a26_facture_ophtalmo,20.8,0.0,20.8
|
||||
edb77e5c-ea68-4bad-a177-1256a3bafc9c_CF_1686176,64.48,59.88,4.600000000000001
|
||||
Facture ostéopathe-26112640,66.0,65.0,1.0
|
||||
1498c5f5-22cf-41d4-87f0-9b295ff481f4_IMG_5659,50.0,41.41,8.590000000000003
|
||||
Facture ostéopathe-25449739,70.0,60.0,10.0
|
||||
4fa21bd9-393e-41d4-9af2-bf42004f4e94_image,15.93,14.48,1.4499999999999993
|
||||
895777f3-4478-4e8d-a616-d498850d1523_factures-lunettes_Tess,41.0,79.0,-38.0
|
||||
6f544990-74c6-4c7e-8a25-08eb3f9138c8_RemboursementSoins01082024_1,24.98,41.41,-16.429999999999996
|
||||
9901adc0-96d4-4cfd-a028-8286fd1f7841_Fre_Ophtalmo,70.69,72.69,-2.0
|
||||
5c4353b0-55a4-4687-9744-d1a6da183abd_facture_lentilles_de_contact,78.98,65.82,13.16000000000001
|
||||
598221c3-d0b3-403a-a261-c68cbe56b453_facture_ophtalmo,44.0,88.0,-44.0
|
||||
803f61ed-759c-4773-90f2-d8737ef911ab_image,115.0,0.0,115.0
|
||||
8c8c6ff8-8e9c-49fc-b131-1dce75bcdfc2_Facture_FA1947_04_02_2025,70.0,58.33,11.670000000000002
|
||||
e6272522-cc2a-44b0-af14-9f54d1a76a59_Facture_lunettes_,691.62,1253.62,-561.9999999999999
|
||||
8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes,262.0,257.0,5.0
|
||||
47399c82-030f-4904-b8f4-2c3dc7c46723_image,77.61,0.0,77.61
|
||||
e0f0eab0-f98c-4b74-95f2-bfc61ad76d28_Ophtalmo_Isa_01-24,76.0,0.0,76.0
|
||||
DDE prestation Facture des lentilles de contact-26736761,78.0,76.0,2.0
|
||||
8ed22fd8-18ae-4c54-93ae-2f5cd4ec2af6_17375530503755104210735675998866,73.43,0.0,73.43
|
||||
7b830a57-2131-479c-bf31-1673e7308903_invoice_2025_1_31,75.95,59.96,15.990000000000002
|
||||
0a26adda-beef-4977-966c-12efc73f1d15_20250129_190625,74.61,0.0,74.61
|
||||
bedf599f-99db-4613-aa22-17d52690c34c_doc00194520240521104544,95.29,0.0,95.29
|
||||
ee2bacc2-7a66-4adf-b4ff-95fc5c13a2da_20250213_112821,10.26,9.629999999999999,0.6300000000000008
|
||||
172442503141724-04-08_recu_kine_Suz,66.12,0.0,66.12
|
||||
8019accc-33b4-4f20-95eb-3ff1e1d4db0b_nnn,127.77,0.0,127.77
|
||||
Facture des lentilles de contact-27700403,41.2,41.8,-0.5999999999999943
|
||||
9c2763f2-fb0c-4fe4-be38-6f94fba64162_optical_facture_lunettes_apres_accident,551.58,620.5799999999999,-68.99999999999989
|
||||
1724397366294irm,252.72,0.0,252.72
|
||||
209e4abe-4973-4fd3-83cc-64f940fb7372_facture_ophtalmologue,72.63,0.0,72.63
|
||||
e07571f8-3ba4-46fc-a37e-53bd6dd965c7_IMG20250123190152,167.0,174.65,-7.650000000000006
|
||||
fecefa42-3451-496b-bcb5-d5ebd4cdd8da_invoice_2024_10_14,195.92,163.28,32.639999999999986
|
||||
44e87f9e-a4e2-4851-b383-e1401b36e853_image,118.14,0.0,118.14
|
||||
96be3553-7261-4698-bf20-88ca5c826889_ReleveMensuel_Mai,154.04,535.1099999999999,-381.06999999999994
|
||||
a13a6613-da2d-48b7-96ef-412ba5a88af0_Factures_Clemence_,71.34,39.0,32.34
|
||||
78ec53bb-a4c5-469f-a4d0-f42db4cf9b11_CF_1697735,64.48,59.88,4.600000000000001
|
||||
2dbb4025-f570-49f9-83d1-08aedb2b122d_lunettes_Lou-Ann,213.4,253.0,-39.599999999999994
|
||||
3fe044b5-fd6a-4f5c-accc-ce7b987575a8_IMG_6817,98.61,0.0,98.61
|
||||
82ac654c-54d4-401f-a006-96d9e46531fe_Facture_ostheo_JEAN,40.0,60.0,-20.0
|
||||
605bfa50-15ce-48a0-87ec-201c3aa0557a_lunette_auregane_daden_001,627.9,557.7,70.19999999999993
|
||||
43666807-3eb2-40b9-bdf6-94d4fe31b71e_factures,147.0,105.0,42.0
|
||||
9c1cc24a-5f7e-42ef-998d-c813a686a7b5_549D8459-524A-4A5C-9612-09EEA1ADDB58,77.61,0.0,77.61
|
||||
Facture des lentilles de contact-27693842,130.0,150.0,-20.0
|
||||
626c99a4-986b-401f-b91e-9b6c0f15cce6_Commande_n100691223,39.51,43.9,-4.390000000000001
|
||||
|
188
filter/check_008/label_analysis_report.md
Normal file
188
filter/check_008/label_analysis_report.md
Normal file
@@ -0,0 +1,188 @@
|
||||
# Label Analysis Report
|
||||
|
||||
Input: `008_label_data_sample_seed_1997.json`
|
||||
|
||||
|
||||
## Overview
|
||||
|
||||
- Total records: 1232
|
||||
- Total labels (flattened): 1263
|
||||
- is_bill distribution: {True: 1124, False: 139}
|
||||
- bill_paid distribution: {False: 68, True: 1015, None: 180}
|
||||
- Invoice dates span: 2012-04-17 .. 2025-06-12
|
||||
- Unique year-month pairs: 63
|
||||
|
||||
## Professions (top)
|
||||
|
||||
- Ostéopathe: 371
|
||||
- Ostéopathie: 295
|
||||
- Optique: 228
|
||||
- (missing): 119
|
||||
- Psychologue: 67
|
||||
- Chiropractie: 57
|
||||
- Unknown: 20
|
||||
- Kinésithérapie: 16
|
||||
- Sophrologie: 8
|
||||
- Podologue: 8
|
||||
- Hypnothérapie: 7
|
||||
- Pharmacie: 7
|
||||
- Sophrologue: 6
|
||||
- Ophtalmologie: 6
|
||||
- Réflexologie: 5
|
||||
- Radiologie: 5
|
||||
- Étiopathie: 4
|
||||
- Sage-femme: 3
|
||||
- Psychologie: 3
|
||||
- Soins hospitalier: 3
|
||||
- Naturopathie: 2
|
||||
- Addictologie: 2
|
||||
- Etiopathie: 2
|
||||
- Kinésiologie: 2
|
||||
- Diététicienne: 2
|
||||
- Étiopathe: 2
|
||||
- Etiopathe: 2
|
||||
- Psychomotricité: 1
|
||||
- Homéopathie: 1
|
||||
- Kinésiologue: 1
|
||||
- Optique hay Ophtalmologue???: 1
|
||||
- Médecine du sport: 1
|
||||
- Otique: 1
|
||||
- Biologie: 1
|
||||
- Chiropratie: 1
|
||||
- Chirurgie dentaire: 1
|
||||
- Diététique: 1
|
||||
- Audiopathe: 1
|
||||
|
||||
## Currency distribution
|
||||
|
||||
- EUR: 1135
|
||||
- F: 2
|
||||
|
||||
## Identifier and key field presence
|
||||
|
||||
- adeli_number: 898 present
|
||||
- rpps_number: 182 present
|
||||
- finess_number: 139 present
|
||||
- prescripteur_finess_number: 41 present
|
||||
- doctor_name: 1040 present
|
||||
- invoice_issuer: 1120 present
|
||||
- insured_name: 208 present
|
||||
- beneficiary_name: 1093 present
|
||||
- security_number: 492 present
|
||||
- currency: 1137 present
|
||||
|
||||
## Flags
|
||||
|
||||
- is_handwriting: {False: 327, None: 880, True: 56}
|
||||
- is_rotated: {False: 358, None: 879, True: 26}
|
||||
|
||||
## Numeric summaries
|
||||
|
||||
- total_billed: {'count': 1130, 'min': 9.9, 'p25': 55.0, 'median': 60.0, 'p75': 70.0, 'max': 8000.0, 'mean': 105.40392920353982, 'sum': 119106.44, 'missing': 133}
|
||||
- amount_paid: {'count': 1011, 'min': 0.0, 'p25': 50.0, 'median': 60.0, 'p75': 66.5, 'max': 8000.0, 'mean': 98.68897131552917, 'sum': 99774.55, 'missing': 252}
|
||||
- remaining_payment: {'count': 247, 'min': 0.0, 'p25': 0.0, 'median': 0.0, 'p75': 0.0, 'max': 204.06, 'mean': 2.793765182186235, 'sum': 690.06, 'missing': 1016}
|
||||
- client_part: {'count': 145, 'min': 0.0, 'p25': 26.44, 'median': 48.77, 'p75': 90.0, 'max': 1420.0, 'mean': 99.44468965517241, 'sum': 14419.480000000003, 'missing': 1118}
|
||||
- mandatory_coverage: {'count': 137, 'min': 0.0, 'p25': 0.09, 'median': 27.63, 'p75': 48.55, 'max': 1232.47, 'mean': 43.21014598540146, 'sum': 5919.790000000002, 'missing': 1126}
|
||||
- complementary_coverage: {'count': 93, 'min': 0.0, 'p25': 0.0, 'median': 0.0, 'p75': 90.0, 'max': 499.91, 'mean': 63.8452688172043, 'sum': 5937.61, 'missing': 1170}
|
||||
|
||||
## Items analysis
|
||||
|
||||
- Items per label: count=1263, min=0, max=10, mean=1.25
|
||||
- total_billed vs sum(items.amount) mismatches: 53
|
||||
|
||||
## Data quality issues (sample)
|
||||
|
||||
- Facture médecine douce-27776417: item[0].quantity missing
|
||||
- 551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024: item[0].quantity missing
|
||||
- 551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024: item[1].quantity missing
|
||||
- 551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024: item[2].quantity missing
|
||||
- 551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024: item[3].quantity missing
|
||||
- 551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024: item[4].quantity missing
|
||||
- 551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024: item[5].quantity missing
|
||||
- Facture médecine douce-27640153: item[0].quantity missing
|
||||
- Facture médecine douce-27640153: item[1].quantity missing
|
||||
- 1724397366229Facture_RIVALLANDChrystelle_20240413ICQO: bill_paid True but amount_paid missing
|
||||
- Facture ostéopathe-25417095: bill_paid True but remaining_payment > 0
|
||||
- Facture ostéopathe-GED N-R-2323015: item[0].quantity missing
|
||||
- 74441931-f8d0-4cb0-8eb5-df4147a72bbe_mgp: item[0].quantity missing
|
||||
- 74441931-f8d0-4cb0-8eb5-df4147a72bbe_mgp: currency missing for bill
|
||||
- 74441931-f8d0-4cb0-8eb5-df4147a72bbe_mgp: currency missing for bill
|
||||
- 1724658126141JACOB_Francois_2024-111: item[0].quantity missing
|
||||
- Facture des lentilles de contact-27559701: item[5].amount negative: -6.67
|
||||
- 91c41171-ac88-4ce4-8f15-48c4ad2c5aa2_Devis_lunette: item[0].quantity missing
|
||||
- 0252b5fb-bc84-4fca-bfa7-483ac611885a_Scan_0115: item[0].quantity missing
|
||||
- 0252b5fb-bc84-4fca-bfa7-483ac611885a_Scan_0115: item[1].quantity missing
|
||||
- 7f084bf4-357d-452d-bb4c-76bff332ea0c_IMG_7791: item[0].quantity missing
|
||||
- 7f084bf4-357d-452d-bb4c-76bff332ea0c_IMG_7791: item[1].quantity missing
|
||||
- 70c3e7f5-405b-49d8-a73c-dae83beafb59_Facture_ophtalmo: item[0].quantity missing
|
||||
- 70c3e7f5-405b-49d8-a73c-dae83beafb59_Facture_ophtalmo: item[1].quantity missing
|
||||
- 70c3e7f5-405b-49d8-a73c-dae83beafb59_Facture_ophtalmo: item[2].quantity missing
|
||||
- Facture médecine douce-27776613: item[0].quantity missing
|
||||
- 6eeacb70-27be-40dd-9511-522025b459a3_20241226_080214: item[0].quantity missing
|
||||
- 6eeacb70-27be-40dd-9511-522025b459a3_20241226_080214: item[1].quantity missing
|
||||
- 1724399050339FACTURE_132120424_MME_RONDIN_MAHEVA: item[0].quantity missing
|
||||
- ec117b21-9348-4a76-9a22-2bae87639392_facture_du_14.02_Tony: item[0].quantity missing
|
||||
- ec117b21-9348-4a76-9a22-2bae87639392_facture_du_14.02_Tony: item[1].quantity missing
|
||||
- Facture ostéopathe-25979892: item[0].quantity missing
|
||||
- bf09a877-0705-4c79-b908-6e8da44e44c7_17490641879424403676372514736136: item[0].quantity missing
|
||||
- bf09a877-0705-4c79-b908-6e8da44e44c7_17490641879424403676372514736136: item[1].quantity missing
|
||||
- d4f310c8-3bcf-4bb5-bf53-27ad74d66c1e_FACTURE_OPTICIEN_OPHTALMO: item[0].quantity missing
|
||||
- DDE prestation Facture des lentilles de contact-26740934: item[0].quantity missing
|
||||
- 45ccffcd-5fe8-418b-80c2-894086de9087_facture_ophtalmo_Fred_: item[0].quantity missing
|
||||
- 45ccffcd-5fe8-418b-80c2-894086de9087_facture_ophtalmo_Fred_: item[1].quantity missing
|
||||
- Facture ostéopathe-26626551: item[0].quantity missing
|
||||
- 27ddc814-9239-4944-951a-fdd8a48c5926_IMG_20240922_0001: item[0].quantity missing
|
||||
- 27ddc814-9239-4944-951a-fdd8a48c5926_IMG_20240922_0001: item[1].quantity missing
|
||||
- 27ddc814-9239-4944-951a-fdd8a48c5926_IMG_20240922_0001: item[2].quantity missing
|
||||
- 27ddc814-9239-4944-951a-fdd8a48c5926_IMG_20240922_0001: item[3].quantity missing
|
||||
- 8eceb56a-b571-4ab6-a64e-668db6bf4ad5_chiropracteur_fevrier_25: item[0].quantity missing
|
||||
- Facture des lentilles de contact-27576495: item[2].quantity missing
|
||||
- 32ffb8dc-fbd0-4edf-94b2-7b784cc72ff2_17479876010256548167621008372337: item[0].quantity missing
|
||||
- 32ffb8dc-fbd0-4edf-94b2-7b784cc72ff2_17479876010256548167621008372337: item[1].quantity missing
|
||||
- 32ffb8dc-fbd0-4edf-94b2-7b784cc72ff2_17479876010256548167621008372337: item[2].quantity missing
|
||||
- a5bd8b97-a2a8-49ae-900d-0673d2f96637_quittance_optique_: item[0].quantity missing
|
||||
- e997eddb-05a4-49f9-a7fd-82b48c2694b3_IMG_OPHTALMOLOGIE_20250117_0001: item[0].quantity missing
|
||||
|
||||
## Plots
|
||||
|
||||
- is_bill
|
||||
|
||||
|
||||
- bill_paid
|
||||
|
||||
|
||||
- is_handwriting
|
||||
|
||||
|
||||
- is_rotated
|
||||
|
||||
|
||||
- professions_top20
|
||||
|
||||
|
||||
- currency
|
||||
|
||||
|
||||
- invoice_year_month
|
||||
|
||||
|
||||
- items_per_label
|
||||
|
||||
|
||||
- hist_total_billed
|
||||
|
||||
|
||||
- hist_amount_paid
|
||||
|
||||
|
||||
- hist_remaining_payment
|
||||
|
||||
|
||||
- hist_client_part
|
||||
|
||||
|
||||
- hist_mandatory_coverage
|
||||
|
||||
|
||||
- hist_complementary_coverage
|
||||
|
||||
BIN
filter/check_008/plots/bill_paid.png
Normal file
BIN
filter/check_008/plots/bill_paid.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 20 KiB |
BIN
filter/check_008/plots/currency.png
Normal file
BIN
filter/check_008/plots/currency.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 18 KiB |
BIN
filter/check_008/plots/hist_amount_paid.png
Normal file
BIN
filter/check_008/plots/hist_amount_paid.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 25 KiB |
BIN
filter/check_008/plots/hist_client_part.png
Normal file
BIN
filter/check_008/plots/hist_client_part.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 24 KiB |
BIN
filter/check_008/plots/hist_complementary_coverage.png
Normal file
BIN
filter/check_008/plots/hist_complementary_coverage.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 24 KiB |
BIN
filter/check_008/plots/hist_mandatory_coverage.png
Normal file
BIN
filter/check_008/plots/hist_mandatory_coverage.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 24 KiB |
BIN
filter/check_008/plots/hist_remaining_payment.png
Normal file
BIN
filter/check_008/plots/hist_remaining_payment.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 23 KiB |
BIN
filter/check_008/plots/hist_total_billed.png
Normal file
BIN
filter/check_008/plots/hist_total_billed.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 25 KiB |
BIN
filter/check_008/plots/invoice_year_month.png
Normal file
BIN
filter/check_008/plots/invoice_year_month.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 39 KiB |
BIN
filter/check_008/plots/is_bill.png
Normal file
BIN
filter/check_008/plots/is_bill.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 18 KiB |
BIN
filter/check_008/plots/is_handwriting.png
Normal file
BIN
filter/check_008/plots/is_handwriting.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 18 KiB |
BIN
filter/check_008/plots/is_rotated.png
Normal file
BIN
filter/check_008/plots/is_rotated.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 17 KiB |
BIN
filter/check_008/plots/items_per_label.png
Normal file
BIN
filter/check_008/plots/items_per_label.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 22 KiB |
BIN
filter/check_008/plots/professions_top20.png
Normal file
BIN
filter/check_008/plots/professions_top20.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 89 KiB |
39
filter/check_008/professions_counts.csv
Normal file
39
filter/check_008/professions_counts.csv
Normal file
@@ -0,0 +1,39 @@
|
||||
profession,count
|
||||
Ostéopathe,371
|
||||
Ostéopathie,295
|
||||
Optique,228
|
||||
(missing),119
|
||||
Psychologue,67
|
||||
Chiropractie,57
|
||||
Unknown,20
|
||||
Kinésithérapie,16
|
||||
Sophrologie,8
|
||||
Podologue,8
|
||||
Hypnothérapie,7
|
||||
Pharmacie,7
|
||||
Sophrologue,6
|
||||
Ophtalmologie,6
|
||||
Réflexologie,5
|
||||
Radiologie,5
|
||||
Étiopathie,4
|
||||
Sage-femme,3
|
||||
Psychologie,3
|
||||
Soins hospitalier,3
|
||||
Naturopathie,2
|
||||
Addictologie,2
|
||||
Etiopathie,2
|
||||
Kinésiologie,2
|
||||
Diététicienne,2
|
||||
Étiopathe,2
|
||||
Etiopathe,2
|
||||
Psychomotricité,1
|
||||
Homéopathie,1
|
||||
Kinésiologue,1
|
||||
Optique hay Ophtalmologue???,1
|
||||
Médecine du sport,1
|
||||
Otique,1
|
||||
Biologie,1
|
||||
Chiropratie,1
|
||||
Chirurgie dentaire,1
|
||||
Diététique,1
|
||||
Audiopathe,1
|
||||
|
4
filter/check_009/bill_paid_counts.csv
Normal file
4
filter/check_009/bill_paid_counts.csv
Normal file
@@ -0,0 +1,4 @@
|
||||
bill_paid,count
|
||||
False,74
|
||||
True,1011
|
||||
,174
|
||||
|
3
filter/check_009/currency_counts.csv
Normal file
3
filter/check_009/currency_counts.csv
Normal file
@@ -0,0 +1,3 @@
|
||||
currency,count
|
||||
EUR,1137
|
||||
F,2
|
||||
|
11
filter/check_009/id_presence.csv
Normal file
11
filter/check_009/id_presence.csv
Normal file
@@ -0,0 +1,11 @@
|
||||
field,present_count
|
||||
adeli_number,902
|
||||
rpps_number,179
|
||||
finess_number,133
|
||||
prescripteur_finess_number,42
|
||||
doctor_name,1051
|
||||
invoice_issuer,1122
|
||||
insured_name,203
|
||||
beneficiary_name,1098
|
||||
security_number,494
|
||||
currency,1139
|
||||
|
3
filter/check_009/is_bill_counts.csv
Normal file
3
filter/check_009/is_bill_counts.csv
Normal file
@@ -0,0 +1,3 @@
|
||||
is_bill,count
|
||||
True,1126
|
||||
False,133
|
||||
|
277
filter/check_009/issues.csv
Normal file
277
filter/check_009/issues.csv
Normal file
@@ -0,0 +1,277 @@
|
||||
source,issue
|
||||
eedda33e-049b-4ab8-aee8-6f5d0c88b500_IMG_0358,item[0].quantity missing
|
||||
eedda33e-049b-4ab8-aee8-6f5d0c88b500_IMG_0358,item[1].quantity missing
|
||||
e72e49e0-7891-4dc8-b62f-40c7bf6252a4_Nuria_Ravier_Ophtalomologiste,item[0].quantity missing
|
||||
e72e49e0-7891-4dc8-b62f-40c7bf6252a4_Nuria_Ravier_Ophtalomologiste,item[0].quantity missing
|
||||
Facture ostéopathe-25417096,item[0].quantity missing
|
||||
eb5561dd-937b-4c0b-b6c6-56da8b528c94_facture_vision_claire_ophtalmo_1_001,item[0].quantity missing
|
||||
6f544990-74c6-4c7e-8a25-08eb3f9138c8_RemboursementSoins01082024_1,item[0].quantity missing
|
||||
6f544990-74c6-4c7e-8a25-08eb3f9138c8_RemboursementSoins01082024_1,item[1].quantity missing
|
||||
1150ac6a-abdc-409d-b7ca-296e7b35769c_Facture_BOLZERJean_Francois_20250131GMAM,item[0].quantity missing
|
||||
Facture ostéopathe-26738638,item[0].quantity missing
|
||||
34162b51-6c3f-4dcc-a18a-d6b9e45e4f22_Recu_ophtalmo_Cadet_Corine_2025-03-09,bill_paid True but remaining_payment > 0
|
||||
34162b51-6c3f-4dcc-a18a-d6b9e45e4f22_Recu_ophtalmo_Cadet_Corine_2025-03-09,item[0].quantity missing
|
||||
34162b51-6c3f-4dcc-a18a-d6b9e45e4f22_Recu_ophtalmo_Cadet_Corine_2025-03-09,item[1].quantity missing
|
||||
abff81b4-d895-47d5-b3d4-93da48f5c1e6_20241212_172044,item[0].quantity missing
|
||||
abff81b4-d895-47d5-b3d4-93da48f5c1e6_20241212_172044,item[1].quantity missing
|
||||
2c75bd51-e61c-452e-b19b-494fa70e9cc9_Facture_lentilles_janvier_2024,item[4].amount negative: -2.9
|
||||
2c75bd51-e61c-452e-b19b-494fa70e9cc9_Facture_lentilles_janvier_2024,item[5].amount negative: -3.0
|
||||
Facture ostéopathe-26605349,item[0].quantity missing
|
||||
Facture médecine douce-27702698,item[0].quantity missing
|
||||
6221379e-7344-4868-9ee0-04f2c4e26874_consultation_ophtalmo27janvier2025BEAUPERINyann,item[0].quantity missing
|
||||
f55f9121-499d-4c25-b79b-464413d7ca6c_Quittance_Ophtalmologue,item[0].quantity missing
|
||||
f55f9121-499d-4c25-b79b-464413d7ca6c_Quittance_Ophtalmologue,item[0].quantity missing
|
||||
62b45277-a437-48c6-af52-d1814f23434f_17460042180346042803920585280783,item[0].quantity missing
|
||||
62b45277-a437-48c6-af52-d1814f23434f_17460042180346042803920585280783,item[1].quantity missing
|
||||
Facture ostéopathe-25847017,bill_paid True but remaining_payment > 0
|
||||
02358c4c-820a-409f-9489-a75f22af3f32_image,item[0].quantity missing
|
||||
02358c4c-820a-409f-9489-a75f22af3f32_image,item[1].quantity missing
|
||||
7ef7306d-400e-4a56-b31c-02237212c470_Scan,item[0].quantity missing
|
||||
7ef7306d-400e-4a56-b31c-02237212c470_Scan,item[1].quantity missing
|
||||
f482ebef-e885-4627-a927-96ad54ab5aa0_Facture,item[0].quantity missing
|
||||
f482ebef-e885-4627-a927-96ad54ab5aa0_Facture,item[1].quantity missing
|
||||
f482ebef-e885-4627-a927-96ad54ab5aa0_Facture,item[3].quantity missing
|
||||
f482ebef-e885-4627-a927-96ad54ab5aa0_Facture,item[4].quantity missing
|
||||
f482ebef-e885-4627-a927-96ad54ab5aa0_Facture,item[6].quantity missing
|
||||
f482ebef-e885-4627-a927-96ad54ab5aa0_Facture,item[7].quantity missing
|
||||
f482ebef-e885-4627-a927-96ad54ab5aa0_Facture,item[8].quantity missing
|
||||
f482ebef-e885-4627-a927-96ad54ab5aa0_Facture,item[9].quantity missing
|
||||
f482ebef-e885-4627-a927-96ad54ab5aa0_Facture,item[11].quantity missing
|
||||
Facture ostéopathe-26610949,item[0].quantity missing
|
||||
8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes,item[0].quantity missing
|
||||
8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes,item[1].quantity missing
|
||||
8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes,item[2].quantity missing
|
||||
8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes,item[3].quantity missing
|
||||
8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes,item[4].quantity missing
|
||||
bf09a877-0705-4c79-b908-6e8da44e44c7_17490641879424403676372514736136,item[0].quantity missing
|
||||
bf09a877-0705-4c79-b908-6e8da44e44c7_17490641879424403676372514736136,item[1].quantity missing
|
||||
17b264a2-4881-49a4-8326-48ace17dd86f_factures_ophtalmo,item[0].quantity missing
|
||||
17b264a2-4881-49a4-8326-48ace17dd86f_factures_ophtalmo,item[1].quantity missing
|
||||
17b264a2-4881-49a4-8326-48ace17dd86f_factures_ophtalmo,item[0].quantity missing
|
||||
e566a3b0-cc99-4c4d-8715-9f3be841a066_noname,item[0].quantity missing
|
||||
e566a3b0-cc99-4c4d-8715-9f3be841a066_noname,item[1].quantity missing
|
||||
c6013936-5850-4027-9f61-32c30486e6d5_optique,item[0].quantity missing
|
||||
c6013936-5850-4027-9f61-32c30486e6d5_optique,item[1].quantity missing
|
||||
47399c82-030f-4904-b8f4-2c3dc7c46723_image,item[0].quantity missing
|
||||
47399c82-030f-4904-b8f4-2c3dc7c46723_image,item[1].quantity missing
|
||||
57188e7d-280a-4f94-9d7d-6af62ede30ed_lunettes,item[0].quantity missing
|
||||
209e4abe-4973-4fd3-83cc-64f940fb7372_facture_ophtalmologue,item[0].quantity missing
|
||||
209e4abe-4973-4fd3-83cc-64f940fb7372_facture_ophtalmologue,item[1].quantity missing
|
||||
85120394-0ea3-4a2c-b0d4-11b0fe31a4e7_20250213_145033,item[0].quantity missing
|
||||
85120394-0ea3-4a2c-b0d4-11b0fe31a4e7_20250213_145033,item[1].quantity missing
|
||||
Facture ostéopathe-26972680,item[0].quantity missing
|
||||
Facture médecine douce-27784971,item[0].quantity missing
|
||||
8de5aacb-e142-4321-aaaf-c4179f031d7a_20240810152224_001,item[0].quantity missing
|
||||
8de5aacb-e142-4321-aaaf-c4179f031d7a_20240810152224_001,item[1].quantity missing
|
||||
8de5aacb-e142-4321-aaaf-c4179f031d7a_20240810152224_001,item[2].quantity missing
|
||||
Facture ostéopathe-26479073,item[0].quantity missing
|
||||
b5cfbf34-f15b-4f2e-bbe9-e83c86549d98_FACTURE_OPHTALMO,item[0].quantity missing
|
||||
b5cfbf34-f15b-4f2e-bbe9-e83c86549d98_FACTURE_OPHTALMO,item[1].quantity missing
|
||||
43666807-3eb2-40b9-bdf6-94d4fe31b71e_factures,item[0].quantity missing
|
||||
43666807-3eb2-40b9-bdf6-94d4fe31b71e_factures,item[1].quantity missing
|
||||
43666807-3eb2-40b9-bdf6-94d4fe31b71e_factures,item[2].quantity missing
|
||||
70e53730-c41c-402b-a227-2262cdd9fa7b_osteo_Laurette,item[0].quantity missing
|
||||
1724226151795facture_du_30_mars_2024,item[0].quantity missing
|
||||
Facture médecine douce-27688568,bill_paid True but amount_paid missing
|
||||
Facture ostéopathe-26626551,item[0].quantity missing
|
||||
Facture des lentilles de contact-27559701,item[5].amount negative: -6.67
|
||||
Facture des lentilles de contact-27601451,item[0].quantity missing
|
||||
Facture des lentilles de contact-27601451,item[1].quantity missing
|
||||
Facture des lentilles de contact-27680116,item[0].quantity missing
|
||||
Facture des lentilles de contact-27680116,item[1].quantity missing
|
||||
Facture médecine douce-27640153,item[0].quantity missing
|
||||
Facture médecine douce-27640153,item[1].quantity missing
|
||||
Facture des lentilles de contact-27653858,item[4].amount negative: -2.9
|
||||
Facture des lentilles de contact-27653858,item[5].amount negative: -3.0
|
||||
Facture des lentilles de contact-27545715,item[1].quantity missing
|
||||
ed44bada-dfae-4ab1-b810-e47cec83e3d9_PXL_20250206_222803759.MP,item[0].quantity missing
|
||||
f6fc955d-e941-4a94-892b-ffebcfbb949a_image,item[0].quantity missing
|
||||
f6fc955d-e941-4a94-892b-ffebcfbb949a_image,item[1].quantity missing
|
||||
f6fc955d-e941-4a94-892b-ffebcfbb949a_image,item[2].quantity missing
|
||||
252c4ad4-8fa3-4ed1-9508-ee77f6dce6fe_img20250206_20040851,item[0].quantity missing
|
||||
c53ffdcc-0ee6-4bf2-8c43-923c5471310f_IMG_7285,item[0].quantity missing
|
||||
c53ffdcc-0ee6-4bf2-8c43-923c5471310f_IMG_7285,item[1].quantity missing
|
||||
d8203a60-049f-4f78-befd-556987e766d7_Facture_Mme_GLACON_Adeline,item[0].quantity missing
|
||||
d8203a60-049f-4f78-befd-556987e766d7_Facture_Mme_GLACON_Adeline,item[1].quantity missing
|
||||
821b6297-8c00-4f88-89a7-a9213c38c3e8_JEAN_PIERRE_001,item[0].quantity missing
|
||||
821b6297-8c00-4f88-89a7-a9213c38c3e8_JEAN_PIERRE_001,item[1].quantity missing
|
||||
b442261a-d504-4cda-98cd-e22a6875bfd0_M._ZIMMER_2401,item[0].quantity missing
|
||||
819f6faf-d9f3-4668-9000-e414d92c7f41_ROYER_LUDIVINE_FACTURE_OPHTALMO,item[0].quantity missing
|
||||
819f6faf-d9f3-4668-9000-e414d92c7f41_ROYER_LUDIVINE_FACTURE_OPHTALMO,item[1].quantity missing
|
||||
Facture ostéopathe-25152747,bill_paid True but amount_paid missing
|
||||
DDE prestation Facture des lentilles de contact-26705902,item[3].amount negative: -3.0
|
||||
Facture ostéopathe-26750142,item[0].quantity missing
|
||||
9934f5be-de66-495a-a478-f34e4ced1fa5_Facture_lunettes_Emnah,item[0].quantity missing
|
||||
9934f5be-de66-495a-a478-f34e4ced1fa5_Facture_lunettes_Emnah,item[1].quantity missing
|
||||
9934f5be-de66-495a-a478-f34e4ced1fa5_Facture_lunettes_Emnah,item[2].quantity missing
|
||||
9934f5be-de66-495a-a478-f34e4ced1fa5_Facture_lunettes_Emnah,item[3].quantity missing
|
||||
9934f5be-de66-495a-a478-f34e4ced1fa5_Facture_lunettes_Emnah,item[4].quantity missing
|
||||
9934f5be-de66-495a-a478-f34e4ced1fa5_Facture_lunettes_Emnah,item[5].quantity missing
|
||||
e997eddb-05a4-49f9-a7fd-82b48c2694b3_IMG_OPHTALMOLOGIE_20250117_0001,item[0].quantity missing
|
||||
fa25423e-b79c-48f9-89ae-32ffd95e3101_Facture_Ophtalmologie,item[0].quantity missing
|
||||
91c41171-ac88-4ce4-8f15-48c4ad2c5aa2_Devis_lunette,item[0].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26736761,item[0].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26736761,item[1].quantity missing
|
||||
Facture ostéopathe-26077624,item[0].quantity missing
|
||||
Facture médecine douce-27701986,item[0].quantity missing
|
||||
801fa72b-9dba-4852-9378-3b69ea925679_17354854480549213702943309634816,item[0].quantity missing
|
||||
Facture médecine douce-27784881,item[0].quantity missing
|
||||
d3ef8b3d-b78f-4455-b2d6-1c9ef9ec5e60_Quittance_Ophtalmologue,item[0].quantity missing
|
||||
d3ef8b3d-b78f-4455-b2d6-1c9ef9ec5e60_Quittance_Ophtalmologue,item[0].quantity missing
|
||||
Facture des lentilles de contact-27654482,item[3].amount negative: -5.9
|
||||
Facture des lentilles de contact-27654482,item[4].amount negative: -17.38
|
||||
Facture médecine douce-27784857,item[0].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26729570,item[0].quantity missing
|
||||
1724156677847Facture_osteophate_13_08_2024_POIGNANT_Thierry,item[0].quantity missing
|
||||
5cf26866-c1ae-4dfd-8793-54ef5148224c_FACTURE_OSTEO_SILLON_DU_22-11-2024,item[0].quantity missing
|
||||
Facture ostéopathe-26300731,item[0].quantity missing
|
||||
Facture ostéopathe-26300731,item[1].quantity missing
|
||||
Facture ostéopathe-26300731,item[2].quantity missing
|
||||
dc4e7302-925d-438f-82d5-e78510d858c5_7447113,item[3].amount negative: -5.9
|
||||
dc4e7302-925d-438f-82d5-e78510d858c5_7447113,item[4].amount negative: -5.0
|
||||
Facture ostéopathe-25089174,item[0].quantity missing
|
||||
d9e1cb2a-b44b-4cc8-b07a-030ddf3acd28_Ordonnance_lentilles_Mary,bill_paid True but amount_paid missing
|
||||
1724658376437factosteo,item[0].quantity missing
|
||||
Facture des lentilles de contact-27708923,item[0].quantity missing
|
||||
b46e8411-29d3-44b6-ae65-55f11bf360bd_FACTURE_OCULISTE_JANE,item[0].quantity missing
|
||||
b46e8411-29d3-44b6-ae65-55f11bf360bd_FACTURE_OCULISTE_JANE,item[1].quantity missing
|
||||
b46e8411-29d3-44b6-ae65-55f11bf360bd_FACTURE_OCULISTE_JANE,item[2].quantity missing
|
||||
27c16ad0-3309-4eb1-9432-ab8a546fee7c_osteo,currency missing for bill
|
||||
c2009b55-58d4-411f-a25a-3dc1e32e4a89_facture_lentille,item[3].amount negative: -3.0
|
||||
25f19f18-b19d-4e1e-b105-349dd1584c81_75109795868__1FEA46AA-A04E-44AF-9748-541DE3F7B438,item[0].quantity missing
|
||||
25f19f18-b19d-4e1e-b105-349dd1584c81_75109795868__1FEA46AA-A04E-44AF-9748-541DE3F7B438,item[1].quantity missing
|
||||
1724399050339FACTURE_132120424_MME_RONDIN_MAHEVA,item[0].quantity missing
|
||||
64fa4c19-efa4-48f1-acf8-8c2e8f573b8b_Facture_osteopathe_,item[0].quantity missing
|
||||
c7704de1-820a-44bf-aef9-f7b185f94a26_facture_ophtalmo,item[0].quantity missing
|
||||
c7704de1-820a-44bf-aef9-f7b185f94a26_facture_ophtalmo,item[1].quantity missing
|
||||
c7704de1-820a-44bf-aef9-f7b185f94a26_facture_ophtalmo,item[0].quantity missing
|
||||
bae2e636-32c5-4b2c-b2d2-a7b28b30760f_KM_C250i24082011140,item[0].quantity missing
|
||||
bae2e636-32c5-4b2c-b2d2-a7b28b30760f_KM_C250i24082011140,item[1].quantity missing
|
||||
e0f0eab0-f98c-4b74-95f2-bfc61ad76d28_Ophtalmo_Isa_01-24,item[0].quantity missing
|
||||
e0f0eab0-f98c-4b74-95f2-bfc61ad76d28_Ophtalmo_Isa_01-24,item[1].quantity missing
|
||||
Facture des lentilles de contact-27647325,item[0].quantity missing
|
||||
1724399050380osteo_Lina,item[0].quantity missing
|
||||
70c3e7f5-405b-49d8-a73c-dae83beafb59_Facture_ophtalmo,item[0].quantity missing
|
||||
70c3e7f5-405b-49d8-a73c-dae83beafb59_Facture_ophtalmo,item[1].quantity missing
|
||||
70c3e7f5-405b-49d8-a73c-dae83beafb59_Facture_ophtalmo,item[2].quantity missing
|
||||
66de7232-78a6-46a0-8392-47ec4630ad31_image,item[0].quantity missing
|
||||
66de7232-78a6-46a0-8392-47ec4630ad31_image,item[1].quantity missing
|
||||
66de7232-78a6-46a0-8392-47ec4630ad31_image,item[2].quantity missing
|
||||
0be67d5b-5af6-49ba-8851-d3976f9920a0_Facture_optalmo_Emma_PINSARD,item[0].quantity missing
|
||||
0be67d5b-5af6-49ba-8851-d3976f9920a0_Facture_optalmo_Emma_PINSARD,item[1].quantity missing
|
||||
b79c0ed4-4126-4ff5-86a8-0348cd1c308c_IMG_0048,item[0].quantity missing
|
||||
b79c0ed4-4126-4ff5-86a8-0348cd1c308c_IMG_0048,item[1].quantity missing
|
||||
b79c0ed4-4126-4ff5-86a8-0348cd1c308c_IMG_0048,item[2].quantity missing
|
||||
Facture ostéopathe-26252579,item[0].quantity missing
|
||||
Facture ostéopathe-26252579,item[1].quantity missing
|
||||
Facture ostéopathe-26252579,item[2].quantity missing
|
||||
Facture ostéopathe-26252579,item[3].quantity missing
|
||||
a5bd8b97-a2a8-49ae-900d-0673d2f96637_quittance_optique_,item[0].quantity missing
|
||||
8b6539db-21b4-41dc-a27f-db2cd351fe5f_Doc_34,item[0].quantity missing
|
||||
172465822585720240416133632216,item[0].quantity missing
|
||||
Facture médecine douce-27781814,item[0].quantity missing
|
||||
Facture ostéopathe-26349654,item[0].quantity missing
|
||||
4950b870-6662-4fcd-a7e9-1fc3abd0fdc9_IMG_20250321_182918,item[0].quantity missing
|
||||
4950b870-6662-4fcd-a7e9-1fc3abd0fdc9_IMG_20250321_182918,currency missing for bill
|
||||
cbc7ddff-2ed1-441b-85cb-5c1079588a52_Mes_des_factures,item[0].quantity missing
|
||||
cbc7ddff-2ed1-441b-85cb-5c1079588a52_Mes_des_factures,item[1].quantity missing
|
||||
91efe623-7d6b-4a96-8b03-87199c56566a_image,item[0].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26705131,item[2].amount negative: -5.0
|
||||
Facture des lentilles de contact-27620389,item[0].quantity missing
|
||||
Facture des lentilles de contact-27620389,item[1].quantity missing
|
||||
2da98e03-19d0-430e-90da-a09706de143b_Facture_Lentilles_Stephanie_Marchenay_,item[0].quantity missing
|
||||
Facture-quittance toutes spécialités-27648502,item[0].quantity missing
|
||||
Facture médecine douce-27776613,item[0].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26685394,item[0].quantity missing
|
||||
ec6baa8c-a189-4850-b310-c90bab372cc2_Facture_Krys,bill_paid True but remaining_payment > 0
|
||||
DDE prestation Facture des lentilles de contact-26772555,item[0].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26772555,item[1].quantity missing
|
||||
Facture des lentilles de contact-27700117,item[0].quantity missing
|
||||
1724399050308image,item[0].quantity missing
|
||||
179673c3-7b80-47cb-9467-3425e010905a_ordonnance08.10.2024,item[0].quantity missing
|
||||
179673c3-7b80-47cb-9467-3425e010905a_ordonnance08.10.2024,item[1].quantity missing
|
||||
cdea6c95-be16-4d2b-ba78-95ef182d8e63_IMG_20250523_084341,item[0].quantity missing
|
||||
Facture ostéopathe-25073562,item[0].quantity missing
|
||||
499639d7-4faa-4d3e-b412-7f75da0b4e01_quercy,item[0].quantity missing
|
||||
499639d7-4faa-4d3e-b412-7f75da0b4e01_quercy,item[1].quantity missing
|
||||
Facture ostéopathe-26189649,item[0].quantity missing
|
||||
Facture psychologie - 27581557,item[0].quantity missing
|
||||
Facture psychologie - 27581557,item[0].quantity missing
|
||||
fdaa0d42-8e79-4506-b222-373c88891ff5_notehonoraire__20250212_143947,item[0].quantity missing
|
||||
92340ad4-27cf-476b-a842-051d0b6a2c9f_IMG_20250619_231842,item[0].quantity missing
|
||||
Facture ostéopathe-1142513,item[0].quantity missing
|
||||
Facture ostéopathe-26447822,bill_paid True but amount_paid missing
|
||||
1724156469365facture_osteo08.24,item[0].quantity missing
|
||||
1eea4521-e98f-4f41-911b-07ffaf3b52a0_Facture_MGP,item[0].quantity missing
|
||||
1eea4521-e98f-4f41-911b-07ffaf3b52a0_Facture_MGP,item[1].quantity missing
|
||||
Facture ostéopathe-26595219,item[0].quantity missing
|
||||
0aa44c3d-2dc8-47fc-9307-d1485df454c1_20250207152016_001,item[0].quantity missing
|
||||
Facture ostéopathe-25416514,item[0].quantity missing
|
||||
Sans titre,item[0].quantity missing
|
||||
707771f6-bb3b-4e5e-8d7d-acd584a23009_IMG_20240726_142929,item[0].quantity missing
|
||||
707771f6-bb3b-4e5e-8d7d-acd584a23009_IMG_20240726_142929,item[1].quantity missing
|
||||
d35e4ea0-6678-4dd1-b0d5-fe905d262430_FACTURE_GAFFE_Thierry,item[0].quantity missing
|
||||
Facture ostéopathe-25921103,item[0].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26764274,item[0].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26764274,item[1].quantity missing
|
||||
1724658376419facture_ostheo_avril_24,item[0].quantity missing
|
||||
Facture des lentilles de contact-27654407,bill_paid True but amount_paid missing
|
||||
Facture ostéopathe-26610950,item[0].quantity missing
|
||||
Facture des lentilles de contact-27668729,item[4].amount negative: -4.9
|
||||
Facture des lentilles de contact-27668729,item[5].amount negative: -12.8
|
||||
a05800f6-c1f1-4d50-9598-98e0f90b83f0_17476639221036811513370155179329,item[0].quantity missing
|
||||
32ffb8dc-fbd0-4edf-94b2-7b784cc72ff2_17479876010256548167621008372337,item[0].quantity missing
|
||||
32ffb8dc-fbd0-4edf-94b2-7b784cc72ff2_17479876010256548167621008372337,item[1].quantity missing
|
||||
32ffb8dc-fbd0-4edf-94b2-7b784cc72ff2_17479876010256548167621008372337,item[2].quantity missing
|
||||
Facture ostéopathe-1119174,item[0].quantity missing
|
||||
Facture ostéopathe-26298895,item[0].quantity missing
|
||||
82ac654c-54d4-401f-a006-96d9e46531fe_Facture_ostheo_JEAN,item[0].quantity missing
|
||||
Facture ostéopathe-26585743,item[0].quantity missing
|
||||
106b84f7-29a1-4c06-8cdf-4a297995e678_Facture_lunettes_Amelie,item[0].quantity missing
|
||||
106b84f7-29a1-4c06-8cdf-4a297995e678_Facture_lunettes_Amelie,item[1].quantity missing
|
||||
106b84f7-29a1-4c06-8cdf-4a297995e678_Facture_lunettes_Amelie,item[2].quantity missing
|
||||
106b84f7-29a1-4c06-8cdf-4a297995e678_Facture_lunettes_Amelie,item[3].quantity missing
|
||||
106b84f7-29a1-4c06-8cdf-4a297995e678_Facture_lunettes_Amelie,item[4].quantity missing
|
||||
106b84f7-29a1-4c06-8cdf-4a297995e678_Facture_lunettes_Amelie,item[5].quantity missing
|
||||
284cb22c-17ad-409b-8d5f-79e43a6dd51b_Facture_,item[0].quantity missing
|
||||
fe86f6a8-8360-458e-834c-805ac99350cf_Monsieur_OUEDRAOGO_DAOUDA,item[0].quantity missing
|
||||
fe86f6a8-8360-458e-834c-805ac99350cf_Monsieur_OUEDRAOGO_DAOUDA,item[1].quantity missing
|
||||
Facture des lentilles de contact-27576127,item[2].amount negative: -53.0
|
||||
1724425079263JACOB_Francois_2024-111,item[0].quantity missing
|
||||
46ddeec5-97bf-4175-b214-b7bd74c4fa24_Renouvin_Ferlicot_Andre,item[0].quantity missing
|
||||
46ddeec5-97bf-4175-b214-b7bd74c4fa24_Renouvin_Ferlicot_Andre,item[1].quantity missing
|
||||
0a26adda-beef-4977-966c-12efc73f1d15_20250129_190625,item[0].quantity missing
|
||||
0a26adda-beef-4977-966c-12efc73f1d15_20250129_190625,item[1].quantity missing
|
||||
Facture ostéopathe-26699279,item[0].quantity missing
|
||||
Facture ostéopathe-1216961,bill_paid True but amount_paid missing
|
||||
Facture ostéopathe-1216961,item[0].quantity missing
|
||||
Facture ostéopathe-26935993,item[0].quantity missing
|
||||
1724425079207douilly_6,item[0].quantity missing
|
||||
Facture ostéopathe-26298310,item[0].quantity missing
|
||||
8019accc-33b4-4f20-95eb-3ff1e1d4db0b_nnn,item[0].quantity missing
|
||||
8019accc-33b4-4f20-95eb-3ff1e1d4db0b_nnn,item[1].quantity missing
|
||||
379ed230-4e27-4f61-9039-226972f26cc9_Factures_Dani_medecin_,item[0].quantity missing
|
||||
379ed230-4e27-4f61-9039-226972f26cc9_Factures_Dani_medecin_,item[1].quantity missing
|
||||
b410345a-31de-4397-a896-bc11d97e524c_FACTURE_OPHTALMOLOGISTE,item[0].quantity missing
|
||||
0268b186-6926-46af-b75f-ba01c02cb647_IMG_20240514_152542,item[0].quantity missing
|
||||
0bfb5041-2751-421d-b5d5-60d696c361c1_17391960081576654684571490136238,item[0].quantity missing
|
||||
598221c3-d0b3-403a-a261-c68cbe56b453_facture_ophtalmo,item[0].quantity missing
|
||||
598221c3-d0b3-403a-a261-c68cbe56b453_facture_ophtalmo,item[1].quantity missing
|
||||
1724226151831IMG_8005,item[0].quantity missing
|
||||
c8fa8d11-c2df-4f39-9909-28790acc5957_Factures_opthalmo,item[0].quantity missing
|
||||
50eeaebf-d552-4a12-92a4-66d185bb992b_facture_optique,item[0].quantity missing
|
||||
50eeaebf-d552-4a12-92a4-66d185bb992b_facture_optique,item[1].quantity missing
|
||||
50eeaebf-d552-4a12-92a4-66d185bb992b_facture_optique,item[2].quantity missing
|
||||
Facture ostéopathe-26744045,item[0].quantity missing
|
||||
1724425031419Facture_Osteo_20240417,item[0].quantity missing
|
||||
Facture ostéopathe-25187936,item[0].quantity missing
|
||||
ec117b21-9348-4a76-9a22-2bae87639392_facture_du_14.02_Tony,item[0].quantity missing
|
||||
ec117b21-9348-4a76-9a22-2bae87639392_facture_du_14.02_Tony,item[1].quantity missing
|
||||
Facture ostéopathe-26052732,item[0].quantity missing
|
||||
de6a66cd-d729-479c-a119-8b9e442702db_Screenshot_20250227_112521_Gallery,item[0].quantity missing
|
||||
de6a66cd-d729-479c-a119-8b9e442702db_Screenshot_20250227_112521_Gallery,item[1].quantity missing
|
||||
Facture médecine douce-27709304,item[0].quantity missing
|
||||
DDE prestation Facture des lentilles de contact-26715605,item[0].quantity missing
|
||||
Facture médecine douce-27702699,item[0].quantity missing
|
||||
70546a38-b6d7-4151-b08a-82b01c8e4a98_480755849_2183001178819517_1003204107786000285_n,item[0].quantity missing
|
||||
1724658298461facture_podologue_alicia,item[0].quantity missing
|
||||
|
61
filter/check_009/item_total_billed_mismatches.csv
Normal file
61
filter/check_009/item_total_billed_mismatches.csv
Normal file
@@ -0,0 +1,61 @@
|
||||
source_image,total_billed,sum_item_amount,diff
|
||||
6f544990-74c6-4c7e-8a25-08eb3f9138c8_RemboursementSoins01082024_1,24.98,41.41,-16.429999999999996
|
||||
ea64ca35-29f5-49ba-894b-13aa3421f1d6_doc00681020250513143142,138.96,115.78,23.180000000000007
|
||||
b7df45bd-a142-4cb1-bb00-69122c5b405a_IMG20250520184626,594.8,798.0,-203.20000000000005
|
||||
2dbb4025-f570-49f9-83d1-08aedb2b122d_lunettes_Lou-Ann,213.4,253.0,-39.599999999999994
|
||||
Facture des lentilles de contact-27693842,130.0,150.0,-20.0
|
||||
6221379e-7344-4868-9ee0-04f2c4e26874_consultation_ophtalmo27janvier2025BEAUPERINyann,20.8,20.86,-0.05999999999999872
|
||||
0a639214-b39e-4971-8d51-d83b7d2d2c2e_facture_lentilles_17_01_2025,540.8,550.8,-10.0
|
||||
8f0e72ca-64e5-4797-87bc-9378b842433a_CF_1676107,64.48,59.88,4.600000000000001
|
||||
Facture ostéopathe-26610949,60.0,100.0,-40.0
|
||||
8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes,262.0,257.0,5.0
|
||||
e566a3b0-cc99-4c4d-8715-9f3be841a066_noname,44.0,43.28,0.7199999999999989
|
||||
Facture ostéopathe-25449739,70.0,60.0,10.0
|
||||
47399c82-030f-4904-b8f4-2c3dc7c46723_image,77.61,0.0,77.61
|
||||
209e4abe-4973-4fd3-83cc-64f940fb7372_facture_ophtalmologue,72.63,0.0,72.63
|
||||
85120394-0ea3-4a2c-b0d4-11b0fe31a4e7_20250213_145033,73.33,84.33,-11.0
|
||||
43666807-3eb2-40b9-bdf6-94d4fe31b71e_factures,147.0,105.0,42.0
|
||||
a3e845ff-e89c-4eb0-bf8d-164979dfee2b_Recu_Osteo_LIGNAC_Sebastien,65.0,60.0,5.0
|
||||
060cd8e5-ac6f-47e5-b8f5-fe9d3920c522_CHIRO_FEVRIER_2025,40.0,90.0,-50.0
|
||||
1724397121050Adobe_Scan_17_avr._2024,45.96,43.00999999999999,2.95000000000001
|
||||
8f2b09a0-d20d-4a94-a70a-fda31024af49_Facture_22_04_2025,340.0,290.0,50.0
|
||||
06e1588f-71a7-43fc-bf99-d066db847189_Opticien_,375.6,613.6,-238.0
|
||||
72cb658b-3239-4479-918d-aac53bc4a552_Facture_ophtalmo_14-01-2025,130.0,0.0,130.0
|
||||
e997eddb-05a4-49f9-a7fd-82b48c2694b3_IMG_OPHTALMOLOGIE_20250117_0001,119.48,0.0,119.48
|
||||
Facture ostéopathe-1066603,45.0,65.0,-20.0
|
||||
DDE prestation Facture des lentilles de contact-26736761,78.0,76.0,2.0
|
||||
Facture ostéopathe-26555547,220.65,0.0,220.65
|
||||
96be3553-7261-4698-bf20-88ca5c826889_ReleveMensuel_Mai,154.04,535.1099999999999,-381.06999999999994
|
||||
ba1b499f-199f-4106-bb2c-aac62a67c235_doc00448420250313084147,644.16,643.96,0.1999999999999318
|
||||
1724397121048facture_kine_15-04-24,88.4,17.68,70.72
|
||||
Facture ostéopathe-26401996,205.56,0.0,205.56
|
||||
27c16ad0-3309-4eb1-9432-ab8a546fee7c_osteo,60.0,0.0,60.0
|
||||
c7704de1-820a-44bf-aef9-f7b185f94a26_facture_ophtalmo,120.0,0.0,120.0
|
||||
c7704de1-820a-44bf-aef9-f7b185f94a26_facture_ophtalmo,20.8,0.0,20.8
|
||||
bae2e636-32c5-4b2c-b2d2-a7b28b30760f_KM_C250i24082011140,110.48,0.0,110.48
|
||||
e0f0eab0-f98c-4b74-95f2-bfc61ad76d28_Ophtalmo_Isa_01-24,76.0,0.0,76.0
|
||||
670b97e0-61da-4341-843d-a02b4ae76ef5_image,352.0,468.67,-116.67000000000002
|
||||
e6c39a83-a53c-48f5-bffe-6bdb57337b33_image,560.06,560.0,0.05999999999994543
|
||||
172442503141724-04-08_recu_kine_Suz,66.12,0.0,66.12
|
||||
499639d7-4faa-4d3e-b412-7f75da0b4e01_quercy,97.63,97.43,0.19999999999998863
|
||||
5dec7b4c-0160-443c-a498-6e94875222aa_FEVRIER_HYPNO,90.0,72.0,18.0
|
||||
ff6211dc-b681-4709-9451-d36fb961476f_MICHELIZZA_QuittanceFSE_1952,280.0,245.0,35.0
|
||||
8ed22fd8-18ae-4c54-93ae-2f5cd4ec2af6_17375530503755104210735675998866,73.43,0.0,73.43
|
||||
78ec53bb-a4c5-469f-a4d0-f42db4cf9b11_CF_1697735,64.48,59.88,4.600000000000001
|
||||
4fa21bd9-393e-41d4-9af2-bf42004f4e94_image,15.93,14.48,1.4499999999999993
|
||||
DDE prestation Facture des lentilles de contact-26764274,78.0,76.0,2.0
|
||||
ffc129de-c82b-4c0b-8ab8-2941fd28cda9_Facture_opticien,525.6,828.3,-302.69999999999993
|
||||
edb77e5c-ea68-4bad-a177-1256a3bafc9c_CF_1686176,64.48,59.88,4.600000000000001
|
||||
32ffb8dc-fbd0-4edf-94b2-7b784cc72ff2_17479876010256548167621008372337,92.4,75.72,16.680000000000007
|
||||
82ac654c-54d4-401f-a006-96d9e46531fe_Facture_ostheo_JEAN,40.0,60.0,-20.0
|
||||
Facture des lentilles de contact-27603799,73.7,67.8,5.900000000000006
|
||||
fe86f6a8-8360-458e-834c-805ac99350cf_Monsieur_OUEDRAOGO_DAOUDA,87.0,0.0,87.0
|
||||
1498c5f5-22cf-41d4-87f0-9b295ff481f4_IMG_5659,50.0,41.41,8.590000000000003
|
||||
0a26adda-beef-4977-966c-12efc73f1d15_20250129_190625,74.61,0.0,74.61
|
||||
Facture des lentilles de contact-27687795,63.98,58.0,5.979999999999997
|
||||
8019accc-33b4-4f20-95eb-3ff1e1d4db0b_nnn,127.77,0.0,127.77
|
||||
379ed230-4e27-4f61-9039-226972f26cc9_Factures_Dani_medecin_,26.5,53.0,-26.5
|
||||
598221c3-d0b3-403a-a261-c68cbe56b453_facture_ophtalmo,44.0,88.0,-44.0
|
||||
1724226151831IMG_8005,33.0,38.0,-5.0
|
||||
50eeaebf-d552-4a12-92a4-66d185bb992b_facture_optique,85.08,70.61,14.469999999999999
|
||||
b5ecf45a-00cd-45a8-900d-6a4fc90a79a4_ZOPIE_CORPETTI_CAROLE,113.44,113.34,0.09999999999999432
|
||||
|
193
filter/check_009/label_analysis_report.md
Normal file
193
filter/check_009/label_analysis_report.md
Normal file
@@ -0,0 +1,193 @@
|
||||
# Label Analysis Report
|
||||
|
||||
Input: `009_label_data_sample_seed_42.json`
|
||||
|
||||
|
||||
## Overview
|
||||
|
||||
- Total records: 1232
|
||||
- Total labels (flattened): 1259
|
||||
- is_bill distribution: {True: 1126, False: 133}
|
||||
- bill_paid distribution: {False: 74, True: 1011, None: 174}
|
||||
- Invoice dates span: 2012-04-17 .. 2025-12-20
|
||||
- Unique year-month pairs: 64
|
||||
|
||||
## Professions (top)
|
||||
|
||||
- Ostéopathe: 377
|
||||
- Ostéopathie: 308
|
||||
- Optique: 221
|
||||
- (missing): 108
|
||||
- Chiropractie: 54
|
||||
- Psychologue: 52
|
||||
- Unknown: 21
|
||||
- Kinésithérapie: 15
|
||||
- Ophtalmologie: 13
|
||||
- Sophrologie: 12
|
||||
- Podologue: 9
|
||||
- Hypnothérapie: 7
|
||||
- Pharmacie: 5
|
||||
- Naturopathie: 5
|
||||
- Etiopathie: 4
|
||||
- Radiologie: 4
|
||||
- Étiopathie: 4
|
||||
- Soins hospitalier: 4
|
||||
- Sophrologue: 3
|
||||
- Etiopathe: 3
|
||||
- Psychologie: 2
|
||||
- Psychomotricité: 2
|
||||
- Otique: 2
|
||||
- Médecine Générale: 2
|
||||
- Homéopathie: 2
|
||||
- Kinésiologie: 2
|
||||
- Pompes funèbres: 2
|
||||
- Chirurgie dentaire: 1
|
||||
- Biologie: 1
|
||||
- Sevrage tabagique: 1
|
||||
- Acupuncture: 1
|
||||
- Étiopathe: 1
|
||||
- unknown: 1
|
||||
- Orthopédie: 1
|
||||
- Psychiatrie: 1
|
||||
- Diététicienne: 1
|
||||
- Chiropratie: 1
|
||||
- Medecine Générale: 1
|
||||
- Sage-femme: 1
|
||||
- Addictologie: 1
|
||||
- Ophtalmologue: 1
|
||||
- Diététique: 1
|
||||
- Réflexologie: 1
|
||||
|
||||
## Currency distribution
|
||||
|
||||
- EUR: 1137
|
||||
- F: 2
|
||||
|
||||
## Identifier and key field presence
|
||||
|
||||
- adeli_number: 902 present
|
||||
- rpps_number: 179 present
|
||||
- finess_number: 133 present
|
||||
- prescripteur_finess_number: 42 present
|
||||
- doctor_name: 1051 present
|
||||
- invoice_issuer: 1122 present
|
||||
- insured_name: 203 present
|
||||
- beneficiary_name: 1098 present
|
||||
- security_number: 494 present
|
||||
- currency: 1139 present
|
||||
|
||||
## Flags
|
||||
|
||||
- is_handwriting: {True: 62, None: 858, False: 339}
|
||||
- is_rotated: {False: 372, None: 856, True: 31}
|
||||
|
||||
## Numeric summaries
|
||||
|
||||
- total_billed: {'count': 1134, 'min': 1.0, 'p25': 55.0, 'median': 60.0, 'p75': 70.0, 'max': 18600.0, 'mean': 112.98805114638448, 'sum': 128128.45000000001, 'missing': 125}
|
||||
- amount_paid: {'count': 1012, 'min': 0.0, 'p25': 50.0, 'median': 60.0, 'p75': 65.0, 'max': 18600.0, 'mean': 106.93289525691699, 'sum': 108216.08999999997, 'missing': 247}
|
||||
- remaining_payment: {'count': 252, 'min': 0.0, 'p25': 0.0, 'median': 0.0, 'p75': 0.0, 'max': 2744.64, 'mean': 14.226587301587301, 'sum': 3585.1, 'missing': 1007}
|
||||
- client_part: {'count': 153, 'min': 0.0, 'p25': 27.72, 'median': 46.41, 'p75': 81.5, 'max': 1100.0, 'mean': 95.07934640522876, 'sum': 14547.140000000007, 'missing': 1106}
|
||||
- mandatory_coverage: {'count': 138, 'min': 0.0, 'p25': 0.09, 'median': 27.380000000000003, 'p75': 47.730000000000004, 'max': 1175.72, 'mean': 39.25376811594203, 'sum': 5417.020000000004, 'missing': 1121}
|
||||
- complementary_coverage: {'count': 89, 'min': 0.0, 'p25': 0.0, 'median': 0.0, 'p75': 60.0, 'max': 370.03, 'mean': 54.395168539325844, 'sum': 4841.169999999999, 'missing': 1170}
|
||||
|
||||
## Items analysis
|
||||
|
||||
- Items per label: count=1259, min=0, max=14, mean=1.22
|
||||
- total_billed vs sum(items.amount) mismatches: 60
|
||||
|
||||
## Data quality issues (sample)
|
||||
|
||||
- eedda33e-049b-4ab8-aee8-6f5d0c88b500_IMG_0358: item[0].quantity missing
|
||||
- eedda33e-049b-4ab8-aee8-6f5d0c88b500_IMG_0358: item[1].quantity missing
|
||||
- e72e49e0-7891-4dc8-b62f-40c7bf6252a4_Nuria_Ravier_Ophtalomologiste: item[0].quantity missing
|
||||
- e72e49e0-7891-4dc8-b62f-40c7bf6252a4_Nuria_Ravier_Ophtalomologiste: item[0].quantity missing
|
||||
- Facture ostéopathe-25417096: item[0].quantity missing
|
||||
- eb5561dd-937b-4c0b-b6c6-56da8b528c94_facture_vision_claire_ophtalmo_1_001: item[0].quantity missing
|
||||
- 6f544990-74c6-4c7e-8a25-08eb3f9138c8_RemboursementSoins01082024_1: item[0].quantity missing
|
||||
- 6f544990-74c6-4c7e-8a25-08eb3f9138c8_RemboursementSoins01082024_1: item[1].quantity missing
|
||||
- 1150ac6a-abdc-409d-b7ca-296e7b35769c_Facture_BOLZERJean_Francois_20250131GMAM: item[0].quantity missing
|
||||
- Facture ostéopathe-26738638: item[0].quantity missing
|
||||
- 34162b51-6c3f-4dcc-a18a-d6b9e45e4f22_Recu_ophtalmo_Cadet_Corine_2025-03-09: bill_paid True but remaining_payment > 0
|
||||
- 34162b51-6c3f-4dcc-a18a-d6b9e45e4f22_Recu_ophtalmo_Cadet_Corine_2025-03-09: item[0].quantity missing
|
||||
- 34162b51-6c3f-4dcc-a18a-d6b9e45e4f22_Recu_ophtalmo_Cadet_Corine_2025-03-09: item[1].quantity missing
|
||||
- abff81b4-d895-47d5-b3d4-93da48f5c1e6_20241212_172044: item[0].quantity missing
|
||||
- abff81b4-d895-47d5-b3d4-93da48f5c1e6_20241212_172044: item[1].quantity missing
|
||||
- 2c75bd51-e61c-452e-b19b-494fa70e9cc9_Facture_lentilles_janvier_2024: item[4].amount negative: -2.9
|
||||
- 2c75bd51-e61c-452e-b19b-494fa70e9cc9_Facture_lentilles_janvier_2024: item[5].amount negative: -3.0
|
||||
- Facture ostéopathe-26605349: item[0].quantity missing
|
||||
- Facture médecine douce-27702698: item[0].quantity missing
|
||||
- 6221379e-7344-4868-9ee0-04f2c4e26874_consultation_ophtalmo27janvier2025BEAUPERINyann: item[0].quantity missing
|
||||
- f55f9121-499d-4c25-b79b-464413d7ca6c_Quittance_Ophtalmologue: item[0].quantity missing
|
||||
- f55f9121-499d-4c25-b79b-464413d7ca6c_Quittance_Ophtalmologue: item[0].quantity missing
|
||||
- 62b45277-a437-48c6-af52-d1814f23434f_17460042180346042803920585280783: item[0].quantity missing
|
||||
- 62b45277-a437-48c6-af52-d1814f23434f_17460042180346042803920585280783: item[1].quantity missing
|
||||
- Facture ostéopathe-25847017: bill_paid True but remaining_payment > 0
|
||||
- 02358c4c-820a-409f-9489-a75f22af3f32_image: item[0].quantity missing
|
||||
- 02358c4c-820a-409f-9489-a75f22af3f32_image: item[1].quantity missing
|
||||
- 7ef7306d-400e-4a56-b31c-02237212c470_Scan: item[0].quantity missing
|
||||
- 7ef7306d-400e-4a56-b31c-02237212c470_Scan: item[1].quantity missing
|
||||
- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[0].quantity missing
|
||||
- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[1].quantity missing
|
||||
- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[3].quantity missing
|
||||
- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[4].quantity missing
|
||||
- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[6].quantity missing
|
||||
- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[7].quantity missing
|
||||
- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[8].quantity missing
|
||||
- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[9].quantity missing
|
||||
- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[11].quantity missing
|
||||
- Facture ostéopathe-26610949: item[0].quantity missing
|
||||
- 8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes: item[0].quantity missing
|
||||
- 8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes: item[1].quantity missing
|
||||
- 8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes: item[2].quantity missing
|
||||
- 8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes: item[3].quantity missing
|
||||
- 8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes: item[4].quantity missing
|
||||
- bf09a877-0705-4c79-b908-6e8da44e44c7_17490641879424403676372514736136: item[0].quantity missing
|
||||
- bf09a877-0705-4c79-b908-6e8da44e44c7_17490641879424403676372514736136: item[1].quantity missing
|
||||
- 17b264a2-4881-49a4-8326-48ace17dd86f_factures_ophtalmo: item[0].quantity missing
|
||||
- 17b264a2-4881-49a4-8326-48ace17dd86f_factures_ophtalmo: item[1].quantity missing
|
||||
- 17b264a2-4881-49a4-8326-48ace17dd86f_factures_ophtalmo: item[0].quantity missing
|
||||
- e566a3b0-cc99-4c4d-8715-9f3be841a066_noname: item[0].quantity missing
|
||||
|
||||
## Plots
|
||||
|
||||
- is_bill
|
||||
|
||||
|
||||
- bill_paid
|
||||
|
||||
|
||||
- is_handwriting
|
||||
|
||||
|
||||
- is_rotated
|
||||
|
||||
|
||||
- professions_top20
|
||||
|
||||
|
||||
- currency
|
||||
|
||||
|
||||
- invoice_year_month
|
||||
|
||||
|
||||
- items_per_label
|
||||
|
||||
|
||||
- hist_total_billed
|
||||
|
||||
|
||||
- hist_amount_paid
|
||||
|
||||
|
||||
- hist_remaining_payment
|
||||
|
||||
|
||||
- hist_client_part
|
||||
|
||||
|
||||
- hist_mandatory_coverage
|
||||
|
||||
|
||||
- hist_complementary_coverage
|
||||
|
||||
BIN
filter/check_009/plots/bill_paid.png
Normal file
BIN
filter/check_009/plots/bill_paid.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 20 KiB |
BIN
filter/check_009/plots/currency.png
Normal file
BIN
filter/check_009/plots/currency.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 18 KiB |
BIN
filter/check_009/plots/hist_amount_paid.png
Normal file
BIN
filter/check_009/plots/hist_amount_paid.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 24 KiB |
BIN
filter/check_009/plots/hist_client_part.png
Normal file
BIN
filter/check_009/plots/hist_client_part.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 22 KiB |
BIN
filter/check_009/plots/hist_complementary_coverage.png
Normal file
BIN
filter/check_009/plots/hist_complementary_coverage.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 24 KiB |
BIN
filter/check_009/plots/hist_mandatory_coverage.png
Normal file
BIN
filter/check_009/plots/hist_mandatory_coverage.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 24 KiB |
BIN
filter/check_009/plots/hist_remaining_payment.png
Normal file
BIN
filter/check_009/plots/hist_remaining_payment.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 23 KiB |
BIN
filter/check_009/plots/hist_total_billed.png
Normal file
BIN
filter/check_009/plots/hist_total_billed.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 24 KiB |
BIN
filter/check_009/plots/invoice_year_month.png
Normal file
BIN
filter/check_009/plots/invoice_year_month.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 38 KiB |
BIN
filter/check_009/plots/is_bill.png
Normal file
BIN
filter/check_009/plots/is_bill.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 18 KiB |
BIN
filter/check_009/plots/is_handwriting.png
Normal file
BIN
filter/check_009/plots/is_handwriting.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 18 KiB |
BIN
filter/check_009/plots/is_rotated.png
Normal file
BIN
filter/check_009/plots/is_rotated.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 21 KiB |
BIN
filter/check_009/plots/items_per_label.png
Normal file
BIN
filter/check_009/plots/items_per_label.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 23 KiB |
BIN
filter/check_009/plots/professions_top20.png
Normal file
BIN
filter/check_009/plots/professions_top20.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 84 KiB |
44
filter/check_009/professions_counts.csv
Normal file
44
filter/check_009/professions_counts.csv
Normal file
@@ -0,0 +1,44 @@
|
||||
profession,count
|
||||
Ostéopathe,377
|
||||
Ostéopathie,308
|
||||
Optique,221
|
||||
(missing),108
|
||||
Chiropractie,54
|
||||
Psychologue,52
|
||||
Unknown,21
|
||||
Kinésithérapie,15
|
||||
Ophtalmologie,13
|
||||
Sophrologie,12
|
||||
Podologue,9
|
||||
Hypnothérapie,7
|
||||
Pharmacie,5
|
||||
Naturopathie,5
|
||||
Etiopathie,4
|
||||
Radiologie,4
|
||||
Étiopathie,4
|
||||
Soins hospitalier,4
|
||||
Sophrologue,3
|
||||
Etiopathe,3
|
||||
Psychologie,2
|
||||
Psychomotricité,2
|
||||
Otique,2
|
||||
Médecine Générale,2
|
||||
Homéopathie,2
|
||||
Kinésiologie,2
|
||||
Pompes funèbres,2
|
||||
Chirurgie dentaire,1
|
||||
Biologie,1
|
||||
Sevrage tabagique,1
|
||||
Acupuncture,1
|
||||
Étiopathe,1
|
||||
unknown,1
|
||||
Orthopédie,1
|
||||
Psychiatrie,1
|
||||
Diététicienne,1
|
||||
Chiropratie,1
|
||||
Medecine Générale,1
|
||||
Sage-femme,1
|
||||
Addictologie,1
|
||||
Ophtalmologue,1
|
||||
Diététique,1
|
||||
Réflexologie,1
|
||||
|
163
filter/create_label_data.py
Normal file
163
filter/create_label_data.py
Normal file
@@ -0,0 +1,163 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Script to filter label_data.json based on filepaths in dbscan_filtered_results.json
|
||||
|
||||
This script will keep only the samples from label_data.json where the 'image' field
|
||||
matches with the filepath names found in the 'results' section of dbscan_filtered_results.json
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import List, Dict, Set
|
||||
|
||||
|
||||
def extract_image_names_from_filepath(filepath: str) -> str:
|
||||
"""
|
||||
Extract the base image name from a filepath for comparison.
|
||||
|
||||
Args:
|
||||
filepath: Full filepath from dbscan results
|
||||
|
||||
Returns:
|
||||
Base filename without extension and path
|
||||
"""
|
||||
return Path(filepath).stem
|
||||
|
||||
|
||||
def load_dbscan_filtered_results(filepath: str) -> Set[str]:
|
||||
"""
|
||||
Load dbscan filtered results and extract image names.
|
||||
|
||||
Args:
|
||||
filepath: Path to dbscan_filtered_results.json
|
||||
|
||||
Returns:
|
||||
Set of image names extracted from filepaths
|
||||
"""
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
data = json.load(f)
|
||||
|
||||
image_names = set()
|
||||
|
||||
# Extract image names from all results
|
||||
for result in data.get('results', []):
|
||||
filepath_str = result.get('filepath', '')
|
||||
if filepath_str:
|
||||
# Extract filename without extension
|
||||
image_name = extract_image_names_from_filepath(filepath_str)
|
||||
image_names.add(image_name)
|
||||
|
||||
# Also try to match different variations
|
||||
# Some images might have different naming patterns
|
||||
filename = Path(filepath_str).name
|
||||
# Remove extension and add to set
|
||||
filename_without_ext = Path(filename).stem
|
||||
image_names.add(filename_without_ext)
|
||||
|
||||
return image_names
|
||||
|
||||
|
||||
def load_label_data(filepath: str) -> List[Dict]:
|
||||
"""
|
||||
Load label data from JSON file.
|
||||
|
||||
Args:
|
||||
filepath: Path to label_data.json
|
||||
|
||||
Returns:
|
||||
List of label data samples
|
||||
"""
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
|
||||
|
||||
def filter_label_data(label_data: List[Dict], valid_image_names: Set[str]) -> List[Dict]:
|
||||
"""
|
||||
Filter label data to keep only samples with matching image names.
|
||||
|
||||
Args:
|
||||
label_data: List of label data samples
|
||||
valid_image_names: Set of valid image names from dbscan results
|
||||
|
||||
Returns:
|
||||
Filtered list of label data samples
|
||||
"""
|
||||
filtered_data = []
|
||||
|
||||
for sample in label_data:
|
||||
image_name = sample.get('image', '')
|
||||
|
||||
# Check if the image name matches any of the valid names
|
||||
# We'll check various matching strategies
|
||||
if (image_name in valid_image_names or
|
||||
# Check if any valid name is contained in the image name
|
||||
any(valid_name in image_name for valid_name in valid_image_names) or
|
||||
# Check if image name is contained in any valid name
|
||||
any(image_name in valid_name for valid_name in valid_image_names)):
|
||||
filtered_data.append(sample)
|
||||
|
||||
return filtered_data
|
||||
|
||||
|
||||
def save_filtered_data(filtered_data: List[Dict], output_filepath: str):
|
||||
"""
|
||||
Save filtered data to JSON file.
|
||||
|
||||
Args:
|
||||
filtered_data: Filtered label data
|
||||
output_filepath: Path to save the filtered data
|
||||
"""
|
||||
with open(output_filepath, 'w', encoding='utf-8') as f:
|
||||
json.dump(filtered_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
|
||||
def main():
|
||||
"""Main function to execute the filtering process."""
|
||||
|
||||
# Input file paths
|
||||
# dbscan_results_path = '014_dbscan_filtered_results_temp.json'
|
||||
# dbscan_results_path = '/home/nguyendc/sonnh/embedding-clustering/filter/027_dbscan_v3_filtered_results_temp.json'
|
||||
dbscan_results_path = '/home/nguyendc/sonnh/embedding-clustering/filter/029_dbscan_v3_filtered_results_temp_30.json'
|
||||
|
||||
print(dbscan_results_path)
|
||||
label_data_path = '/home/nguyendc/sonnh/embedding-clustering/filter/label_data.json'
|
||||
|
||||
# Output file path
|
||||
# output_path = '014_filtered_label_data_dbscan_v2_30_percent_amount_data.json'
|
||||
|
||||
output_path = '030_dbscan_v3_retrain_014.json'
|
||||
|
||||
# Check if input files exist
|
||||
if not os.path.exists(dbscan_results_path):
|
||||
print(f"Error: {dbscan_results_path} not found!")
|
||||
return
|
||||
|
||||
if not os.path.exists(label_data_path):
|
||||
print(f"Error: {label_data_path} not found!")
|
||||
return
|
||||
|
||||
print("Loading dbscan filtered results...")
|
||||
valid_image_names = load_dbscan_filtered_results(dbscan_results_path)
|
||||
print(f"Found {len(valid_image_names)} unique image names in dbscan results")
|
||||
|
||||
print("Loading label data...")
|
||||
label_data = load_label_data(label_data_path)
|
||||
print(f"Loaded {len(label_data)} samples from label data")
|
||||
|
||||
print("Filtering label data...")
|
||||
filtered_data = filter_label_data(label_data, valid_image_names)
|
||||
print(f"Filtered to {len(filtered_data)} samples")
|
||||
|
||||
print(f"Saving filtered data to {output_path}...")
|
||||
save_filtered_data(filtered_data, output_path)
|
||||
|
||||
print("Filtering completed successfully!")
|
||||
print(f"Original samples: {len(label_data)}")
|
||||
print(f"Filtered samples: {len(filtered_data)}")
|
||||
print(f"Reduction: {len(label_data) - len(filtered_data)} samples removed")
|
||||
print(f"Retention rate: {len(filtered_data)/len(label_data)*100:.2f}%")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
326
filter/dbscan.py
Normal file
326
filter/dbscan.py
Normal file
@@ -0,0 +1,326 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
DBSCAN Clustering Filter
|
||||
|
||||
Filters clustering results based on specific criteria:
|
||||
- For each cluster: select 50% of points
|
||||
- 25% from center region (closest to centroid)
|
||||
- 25% from border region (furthest from centroid)
|
||||
- All noise points are selected
|
||||
- Uses cosine distance metric
|
||||
"""
|
||||
|
||||
import json
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import normalize
|
||||
from sklearn.metrics.pairwise import cosine_distances
|
||||
import argparse
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
class DBSCANFilter:
|
||||
def __init__(self, embeddings_path, clustering_results_path):
|
||||
"""
|
||||
Initialize DBSCAN filter
|
||||
|
||||
Args:
|
||||
embeddings_path: Path to embeddings JSON file
|
||||
clustering_results_path: Path to DBSCAN clustering results JSON
|
||||
"""
|
||||
self.embeddings_path = embeddings_path
|
||||
self.clustering_results_path = clustering_results_path
|
||||
self.embeddings = None
|
||||
self.embeddings_normalized = None
|
||||
self.clustering_results = None
|
||||
self.filepath_to_embedding = {}
|
||||
|
||||
def load_data(self):
|
||||
"""Load embeddings and clustering results"""
|
||||
print("Loading embeddings...")
|
||||
with open(self.embeddings_path, 'r') as f:
|
||||
embeddings_data = json.load(f)
|
||||
|
||||
# Create mapping from filepath to embedding
|
||||
embeddings_list = []
|
||||
filepaths = []
|
||||
for item in embeddings_data:
|
||||
self.filepath_to_embedding[item['filepath']] = item['embedding']
|
||||
embeddings_list.append(item['embedding'])
|
||||
filepaths.append(item['filepath'])
|
||||
|
||||
self.embeddings = np.array(embeddings_list, dtype=np.float32)
|
||||
self.embeddings_normalized = normalize(self.embeddings, norm='l2')
|
||||
print(f"Loaded {len(embeddings_list)} embeddings")
|
||||
|
||||
print("Loading clustering results...")
|
||||
with open(self.clustering_results_path, 'r') as f:
|
||||
self.clustering_results = json.load(f)
|
||||
|
||||
print(f"Loaded clustering results: {self.clustering_results['n_clusters']} clusters, "
|
||||
f"{self.clustering_results['n_samples']} samples")
|
||||
|
||||
def group_by_clusters(self):
|
||||
"""Group data points by cluster labels"""
|
||||
clusters = {}
|
||||
noise_points = []
|
||||
|
||||
for result in self.clustering_results['results']:
|
||||
cluster_id = result['cluster']
|
||||
filepath = result['filepath']
|
||||
if 'is_noise' in result:
|
||||
is_noise = result['is_noise']
|
||||
else:
|
||||
is_noise = False
|
||||
|
||||
if is_noise or cluster_id == -1:
|
||||
noise_points.append({
|
||||
'filepath': filepath,
|
||||
'embedding': self.filepath_to_embedding[filepath]
|
||||
})
|
||||
else:
|
||||
if cluster_id not in clusters:
|
||||
clusters[cluster_id] = []
|
||||
clusters[cluster_id].append({
|
||||
'filepath': filepath,
|
||||
'embedding': self.filepath_to_embedding[filepath]
|
||||
})
|
||||
|
||||
return clusters, noise_points
|
||||
|
||||
def calculate_cluster_centroid(self, cluster_points):
|
||||
"""Calculate centroid of a cluster using normalized embeddings"""
|
||||
embeddings = np.array([point['embedding'] for point in cluster_points])
|
||||
embeddings_normalized = normalize(embeddings, norm='l2')
|
||||
|
||||
# For cosine distance, centroid is the normalized mean
|
||||
centroid = np.mean(embeddings_normalized, axis=0)
|
||||
centroid_normalized = normalize(centroid.reshape(1, -1), norm='l2')[0]
|
||||
|
||||
return centroid_normalized
|
||||
|
||||
def calculate_cosine_distances_to_centroid(self, cluster_points, centroid):
|
||||
"""Calculate cosine distances from each point to cluster centroid"""
|
||||
embeddings = np.array([point['embedding'] for point in cluster_points])
|
||||
embeddings_normalized = normalize(embeddings, norm='l2')
|
||||
|
||||
# Calculate cosine distances to centroid
|
||||
distances = cosine_distances(embeddings_normalized, centroid.reshape(1, -1)).flatten()
|
||||
|
||||
return distances
|
||||
|
||||
# v1 0.5 data, 0.5 center 0.5 border
|
||||
|
||||
# v2 0.5 data, 0.25 center 0.75 border
|
||||
# def filter_cluster(self, cluster_points, selection_ratio=0.5):
|
||||
|
||||
# v3 0.75 data, 0.25 center 0.75 border
|
||||
def filter_cluster(self, cluster_points, selection_ratio=0.5):
|
||||
|
||||
"""
|
||||
Filter points from a cluster
|
||||
|
||||
Args:
|
||||
cluster_points: List of points in the cluster
|
||||
selection_ratio: Ratio of points to select (default: 0.5 = 50%)
|
||||
|
||||
Returns:
|
||||
List of selected points
|
||||
"""
|
||||
if len(cluster_points) == 0:
|
||||
return []
|
||||
|
||||
|
||||
# Calculate how many points to select
|
||||
total_points = len(cluster_points)
|
||||
num_to_select = max(15, int(total_points * selection_ratio))
|
||||
|
||||
# If we need to select all or almost all points, just return all
|
||||
if num_to_select >= total_points:
|
||||
return cluster_points
|
||||
|
||||
# Calculate centroid
|
||||
centroid = self.calculate_cluster_centroid(cluster_points)
|
||||
|
||||
# Calculate distances to centroid
|
||||
distances = self.calculate_cosine_distances_to_centroid(cluster_points, centroid)
|
||||
|
||||
# Create list of (point, distance) pairs
|
||||
point_distance_pairs = list(zip(cluster_points, distances))
|
||||
|
||||
# Sort by distance (closest to furthest from centroid)
|
||||
point_distance_pairs.sort(key=lambda x: x[1])
|
||||
|
||||
# Calculate how many points to select from center and border
|
||||
# 25% from center (closest to centroid)
|
||||
center_count = max(1, int(num_to_select * 0.25)) # 50% of selected points from center
|
||||
# 25% from border (furthest from centroid)
|
||||
border_count = num_to_select - center_count # remaining from border
|
||||
|
||||
selected_points = []
|
||||
|
||||
# Select center points (closest to centroid)
|
||||
center_points = [pair[0] for pair in point_distance_pairs[:center_count]]
|
||||
selected_points.extend(center_points)
|
||||
|
||||
# Select border points (furthest from centroid)
|
||||
if border_count > 0:
|
||||
border_points = [pair[0] for pair in point_distance_pairs[-border_count:]]
|
||||
selected_points.extend(border_points)
|
||||
|
||||
print(f"Cluster with {total_points} points -> selected {len(selected_points)} points "
|
||||
f"({center_count} center + {border_count} border)")
|
||||
|
||||
return selected_points
|
||||
|
||||
def filter_all_clusters(self):
|
||||
"""Filter all clusters according to the specified criteria"""
|
||||
print("\n" + "="*60)
|
||||
print("FILTERING DBSCAN CLUSTERING RESULTS")
|
||||
print("="*60)
|
||||
|
||||
clusters, noise_points = self.group_by_clusters()
|
||||
|
||||
print(f"Found {len(clusters)} clusters and {len(noise_points)} noise points")
|
||||
|
||||
filtered_results = []
|
||||
|
||||
# Process each cluster
|
||||
for cluster_id, cluster_points in clusters.items():
|
||||
print(f"\nProcessing Cluster {cluster_id}:")
|
||||
filtered_points = self.filter_cluster(cluster_points)
|
||||
|
||||
# Add cluster information
|
||||
for point in filtered_points:
|
||||
filtered_results.append({
|
||||
'filepath': point['filepath'],
|
||||
'cluster': cluster_id,
|
||||
'is_noise': False,
|
||||
'selection_type': 'cluster_filtered'
|
||||
})
|
||||
|
||||
# Add all noise points
|
||||
print(f"\nAdding all {len(noise_points)} noise points...")
|
||||
for point in noise_points:
|
||||
filtered_results.append({
|
||||
'filepath': point['filepath'],
|
||||
'cluster': -1,
|
||||
'is_noise': True,
|
||||
'selection_type': 'noise'
|
||||
})
|
||||
|
||||
return filtered_results
|
||||
|
||||
def save_filtered_results(self, filtered_results, output_path=None):
|
||||
"""Save filtered results to JSON file"""
|
||||
if output_path is None:
|
||||
# Generate output filename based on input
|
||||
base_name = Path(self.clustering_results_path).stem
|
||||
output_path = f"{base_name}_filtered.json"
|
||||
|
||||
# Create summary statistics
|
||||
cluster_stats = {}
|
||||
noise_count = 0
|
||||
|
||||
for result in filtered_results:
|
||||
if result['is_noise']:
|
||||
noise_count += 1
|
||||
else:
|
||||
cluster_id = result['cluster']
|
||||
if cluster_id not in cluster_stats:
|
||||
cluster_stats[cluster_id] = 0
|
||||
cluster_stats[cluster_id] += 1
|
||||
|
||||
# Prepare output data
|
||||
output_data = {
|
||||
"method": "DBSCAN_FILTERED",
|
||||
"original_n_clusters": self.clustering_results['n_clusters'],
|
||||
"original_n_samples": self.clustering_results['n_samples'],
|
||||
"filtered_n_samples": len(filtered_results),
|
||||
"filtering_criteria": {
|
||||
"cluster_selection_ratio": 0.5,
|
||||
"center_points_ratio": 0.5, # 50% of selected points from center
|
||||
"border_points_ratio": 0.5, # 50% of selected points from border
|
||||
"noise_points": "all_selected"
|
||||
},
|
||||
"cluster_statistics": cluster_stats,
|
||||
"noise_points": noise_count,
|
||||
"results": filtered_results
|
||||
}
|
||||
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(output_data, f, indent=4, ensure_ascii=False)
|
||||
|
||||
print("\n" + "="*60)
|
||||
print("FILTERING SUMMARY")
|
||||
print("="*60)
|
||||
print(f"Original samples: {self.clustering_results['n_samples']}")
|
||||
print(f"Filtered samples: {len(filtered_results)}")
|
||||
print(f"Reduction ratio: {len(filtered_results)/self.clustering_results['n_samples']:.2%}")
|
||||
print("\nCluster breakdown:")
|
||||
for cluster_id, count in sorted(cluster_stats.items()):
|
||||
print(f" Cluster {cluster_id}: {count} points")
|
||||
print(f" Noise points: {noise_count} points")
|
||||
print(f"\nFiltered results saved to: {output_path}")
|
||||
|
||||
return output_path
|
||||
|
||||
def create_filepath_list(self, filtered_results, output_txt_path=None):
|
||||
"""Create a simple text file with filtered filepaths"""
|
||||
if output_txt_path is None:
|
||||
base_name = Path(self.clustering_results_path).stem
|
||||
output_txt_path = f"{base_name}_filtered_filepaths.txt"
|
||||
|
||||
filepaths = [result['filepath'] for result in filtered_results]
|
||||
|
||||
with open(output_txt_path, 'w', encoding='utf-8') as f:
|
||||
for filepath in filepaths:
|
||||
f.write(f"{filepath}\n")
|
||||
|
||||
print(f"Filepath list saved to: {output_txt_path}")
|
||||
return output_txt_path
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Filter DBSCAN clustering results")
|
||||
parser.add_argument("--embeddings_path", required=True,
|
||||
help="Path to embeddings JSON file")
|
||||
parser.add_argument("--clustering_results_path", required=True,
|
||||
help="Path to DBSCAN clustering results JSON file")
|
||||
parser.add_argument("--output_path",
|
||||
help="Output path for filtered results (optional)")
|
||||
parser.add_argument("--create_filepath_list", action="store_true",
|
||||
help="Also create a simple text file with filtered filepaths")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Validate input files exist
|
||||
if not os.path.exists(args.embeddings_path):
|
||||
print(f"Error: Embeddings file not found: {args.embeddings_path}")
|
||||
return
|
||||
|
||||
if not os.path.exists(args.clustering_results_path):
|
||||
print(f"Error: Clustering results file not found: {args.clustering_results_path}")
|
||||
return
|
||||
|
||||
# Initialize filter
|
||||
filter_obj = DBSCANFilter(args.embeddings_path, args.clustering_results_path)
|
||||
|
||||
# Load data
|
||||
filter_obj.load_data()
|
||||
|
||||
# Filter clusters
|
||||
filtered_results = filter_obj.filter_all_clusters()
|
||||
|
||||
# Save results
|
||||
filter_obj.save_filtered_results(filtered_results, args.output_path)
|
||||
|
||||
# Create filepath list if requested
|
||||
if args.create_filepath_list:
|
||||
filter_obj.create_filepath_list(filtered_results)
|
||||
|
||||
print("\nFiltering completed successfully!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
429
filter/dbscan_only_one_cluster.py
Normal file
429
filter/dbscan_only_one_cluster.py
Normal file
@@ -0,0 +1,429 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
DBSCAN Clustering Filter
|
||||
|
||||
Filters clustering results based on specific criteria:
|
||||
- For each cluster: select 50% of points
|
||||
- 25% from center region (closest to centroid)
|
||||
- 25% from border region (furthest from centroid)
|
||||
- All noise points are selected
|
||||
- Uses cosine distance metric
|
||||
"""
|
||||
|
||||
import json
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import normalize
|
||||
from sklearn.metrics.pairwise import cosine_distances
|
||||
import argparse
|
||||
import os
|
||||
from pathlib import Path
|
||||
import random
|
||||
|
||||
|
||||
class DBSCANFilter:
|
||||
def __init__(self, embeddings_path, clustering_results_path):
|
||||
"""
|
||||
Initialize DBSCAN filter
|
||||
|
||||
Args:
|
||||
embeddings_path: Path to embeddings JSON file
|
||||
clustering_results_path: Path to DBSCAN clustering results JSON
|
||||
"""
|
||||
self.embeddings_path = embeddings_path
|
||||
self.clustering_results_path = clustering_results_path
|
||||
self.embeddings = None
|
||||
self.embeddings_normalized = None
|
||||
self.clustering_results = None
|
||||
self.filepath_to_embedding = {}
|
||||
|
||||
def load_data(self):
|
||||
"""Load embeddings and clustering results"""
|
||||
print("Loading embeddings...")
|
||||
with open(self.embeddings_path, 'r') as f:
|
||||
embeddings_data = json.load(f)
|
||||
|
||||
# Create mapping from filepath to embedding
|
||||
embeddings_list = []
|
||||
filepaths = []
|
||||
for item in embeddings_data:
|
||||
self.filepath_to_embedding[item['filepath']] = item['embedding']
|
||||
embeddings_list.append(item['embedding'])
|
||||
filepaths.append(item['filepath'])
|
||||
|
||||
self.embeddings = np.array(embeddings_list, dtype=np.float32)
|
||||
self.embeddings_normalized = normalize(self.embeddings, norm='l2')
|
||||
print(f"Loaded {len(embeddings_list)} embeddings")
|
||||
|
||||
print("Loading clustering results...")
|
||||
with open(self.clustering_results_path, 'r') as f:
|
||||
self.clustering_results = json.load(f)
|
||||
|
||||
print(f"Loaded clustering results: {self.clustering_results['n_clusters']} clusters, "
|
||||
f"{self.clustering_results['n_samples']} samples")
|
||||
|
||||
def group_by_clusters(self):
|
||||
"""Group data points by cluster labels"""
|
||||
clusters = {}
|
||||
noise_points = []
|
||||
|
||||
for result in self.clustering_results['results']:
|
||||
cluster_id = result['cluster']
|
||||
filepath = result['filepath']
|
||||
if 'is_noise' in result:
|
||||
is_noise = result['is_noise']
|
||||
else:
|
||||
is_noise = False
|
||||
|
||||
if is_noise or cluster_id == -1:
|
||||
noise_points.append({
|
||||
'filepath': filepath,
|
||||
'embedding': self.filepath_to_embedding[filepath]
|
||||
})
|
||||
else:
|
||||
if cluster_id not in clusters:
|
||||
clusters[cluster_id] = []
|
||||
clusters[cluster_id].append({
|
||||
'filepath': filepath,
|
||||
'embedding': self.filepath_to_embedding[filepath]
|
||||
})
|
||||
|
||||
return clusters, noise_points
|
||||
|
||||
def calculate_cluster_centroid(self, cluster_points):
|
||||
"""Calculate centroid of a cluster using normalized embeddings"""
|
||||
embeddings = np.array([point['embedding'] for point in cluster_points])
|
||||
embeddings_normalized = normalize(embeddings, norm='l2')
|
||||
|
||||
# For cosine distance, centroid is the normalized mean
|
||||
centroid = np.mean(embeddings_normalized, axis=0)
|
||||
centroid_normalized = normalize(centroid.reshape(1, -1), norm='l2')[0]
|
||||
|
||||
return centroid_normalized
|
||||
|
||||
def calculate_cosine_distances_to_centroid(self, cluster_points, centroid):
|
||||
"""Calculate cosine distances from each point to cluster centroid"""
|
||||
embeddings = np.array([point['embedding'] for point in cluster_points])
|
||||
embeddings_normalized = normalize(embeddings, norm='l2')
|
||||
|
||||
# Calculate cosine distances to centroid
|
||||
distances = cosine_distances(embeddings_normalized, centroid.reshape(1, -1)).flatten()
|
||||
|
||||
return distances
|
||||
|
||||
# v3
|
||||
def filter_cluster(self, cluster_points, selection_ratio=0.3):
|
||||
|
||||
"""
|
||||
Filter points from a cluster
|
||||
|
||||
Args:
|
||||
cluster_points: List of points in the cluster
|
||||
selection_ratio: Ratio of points to select (default: 0.5 = 50%)
|
||||
|
||||
Returns:
|
||||
List of selected points
|
||||
"""
|
||||
if len(cluster_points) == 0:
|
||||
return []
|
||||
|
||||
|
||||
# Calculate how many points to select
|
||||
total_points = len(cluster_points)
|
||||
num_to_select = max(15, int(total_points * selection_ratio))
|
||||
|
||||
# If we need to select all or almost all points, just return all
|
||||
if num_to_select >= total_points:
|
||||
return cluster_points
|
||||
|
||||
# Calculate centroid
|
||||
centroid = self.calculate_cluster_centroid(cluster_points)
|
||||
|
||||
# Calculate distances to centroid
|
||||
distances = self.calculate_cosine_distances_to_centroid(cluster_points, centroid)
|
||||
|
||||
# Create list of (point, distance) pairs
|
||||
point_distance_pairs = list(zip(cluster_points, distances))
|
||||
|
||||
# Sort by distance (closest to furthest from centroid)
|
||||
point_distance_pairs.sort(key=lambda x: x[1])
|
||||
|
||||
dis = 0.1
|
||||
# count_center = sum(1 for pair in point_distance_pairs if pair[1] < dis)
|
||||
all_center_points = [pair[0] for pair in point_distance_pairs if pair[1] < dis]
|
||||
print(f"Number of center points (distance < {dis}): {len(all_center_points)}")
|
||||
|
||||
# count_border = sum(1 for pair in point_distance_pairs if pair[1] >= dis)
|
||||
all_border_points = [pair[0] for pair in point_distance_pairs if pair[1] >= dis]
|
||||
print(f"Number of border points (distance >= {dis}): {len(all_border_points)}")
|
||||
|
||||
# Calculate how many points to select from center and border
|
||||
n_center = len(all_center_points)
|
||||
center_count = max(1, int(n_center * 0.15))
|
||||
|
||||
n_border = len(all_border_points)
|
||||
if n_border < 70:
|
||||
border_count = n_border
|
||||
else:
|
||||
border_count = max(0, int(n_border * 0.3)) # remaining from border
|
||||
|
||||
selected_points = []
|
||||
|
||||
random.seed(42)
|
||||
# Select center points (closest to centroid)
|
||||
# center_points = [pair[0] for pair in point_distance_pairs[:center_count]]
|
||||
center_points = random.sample(all_center_points, center_count)
|
||||
selected_points.extend(center_points)
|
||||
|
||||
# # Select border points (furthest from centroid)
|
||||
if border_count > 0:
|
||||
# border_points = [pair[0] for pair in point_distance_pairs[-border_count:]]
|
||||
border_points = random.sample(all_border_points, border_count)
|
||||
selected_points.extend(border_points)
|
||||
|
||||
print(f"Cluster with {total_points} points -> selected {len(selected_points)} points "
|
||||
f"({center_count} center + {border_count} border)")
|
||||
|
||||
return selected_points
|
||||
|
||||
def filter_all_clusters(self):
|
||||
"""Filter all clusters according to the specified criteria"""
|
||||
print("\n" + "="*60)
|
||||
print("FILTERING DBSCAN CLUSTERING RESULTS")
|
||||
print("="*60)
|
||||
|
||||
clusters, noise_points = self.group_by_clusters()
|
||||
|
||||
print(f"Found {len(clusters)} clusters and {len(noise_points)} noise points")
|
||||
|
||||
filtered_results = []
|
||||
|
||||
# Process each cluster
|
||||
for cluster_id, cluster_points in clusters.items():
|
||||
print(f"\nProcessing Cluster {cluster_id}:")
|
||||
filtered_points = self.filter_cluster(cluster_points)
|
||||
|
||||
# Add cluster information
|
||||
for point in filtered_points:
|
||||
filtered_results.append({
|
||||
'filepath': point['filepath'],
|
||||
'cluster': cluster_id,
|
||||
'is_noise': False,
|
||||
'selection_type': 'cluster_filtered'
|
||||
})
|
||||
|
||||
# Add all noise points
|
||||
print(f"\nAdding all {len(noise_points)} noise points...")
|
||||
for point in noise_points:
|
||||
filtered_results.append({
|
||||
'filepath': point['filepath'],
|
||||
'cluster': -1,
|
||||
'is_noise': True,
|
||||
'selection_type': 'noise'
|
||||
})
|
||||
|
||||
return filtered_results
|
||||
|
||||
def save_full_clusters_separately(self, output_dir=None):
|
||||
"""Save ALL points of each cluster (and noise) into separate JSON files.
|
||||
|
||||
The output format for each file matches the existing aggregated format
|
||||
produced by save_filtered_results (keys / structure unchanged). Only the
|
||||
content (subset of results) differs. Noise is treated as a cluster with id -1.
|
||||
|
||||
Args:
|
||||
output_dir: Optional directory to place per-cluster files. If None,
|
||||
files are written next to the clustering_results file.
|
||||
Returns:
|
||||
List of written file paths.
|
||||
"""
|
||||
clusters, noise_points = self.group_by_clusters()
|
||||
|
||||
base_name = Path(self.clustering_results_path).stem
|
||||
if output_dir is None:
|
||||
output_dir = Path('.')
|
||||
else:
|
||||
output_dir = Path(output_dir)
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
written_files = []
|
||||
|
||||
# Helper to build and write one cluster file
|
||||
def _write_cluster_file(cluster_id, points, is_noise=False):
|
||||
# Build results entries preserving field names
|
||||
results_entries = []
|
||||
for p in points:
|
||||
results_entries.append({
|
||||
'filepath': p['filepath'],
|
||||
'cluster': -1 if is_noise else cluster_id,
|
||||
'is_noise': is_noise,
|
||||
'selection_type': 'noise' if is_noise else 'cluster_full'
|
||||
})
|
||||
|
||||
# Stats
|
||||
if is_noise:
|
||||
cluster_stats = {}
|
||||
noise_count = len(points)
|
||||
else:
|
||||
cluster_stats = {cluster_id: len(points)}
|
||||
noise_count = 0
|
||||
|
||||
output_data = {
|
||||
"method": "DBSCAN_FILTERED", # keep unchanged label
|
||||
"original_n_clusters": self.clustering_results['n_clusters'],
|
||||
"original_n_samples": self.clustering_results['n_samples'],
|
||||
"filtered_n_samples": len(results_entries),
|
||||
"filtering_criteria": {
|
||||
# Keep same keys / structure (values kept as original literal values)
|
||||
"cluster_selection_ratio": 0.5,
|
||||
"center_points_ratio": 0.5,
|
||||
"border_points_ratio": 0.5,
|
||||
"noise_points": "all_selected"
|
||||
},
|
||||
"cluster_statistics": cluster_stats,
|
||||
"noise_points": noise_count,
|
||||
"results": results_entries
|
||||
}
|
||||
|
||||
# Filename
|
||||
if is_noise:
|
||||
filename = f"{base_name}_cluster_noise.json"
|
||||
else:
|
||||
filename = f"{base_name}_cluster_{cluster_id}.json"
|
||||
out_path = output_dir / filename
|
||||
with open(out_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(output_data, f, indent=4, ensure_ascii=False)
|
||||
written_files.append(str(out_path))
|
||||
print(f"Saved cluster {cluster_id if not is_noise else 'noise'} -> {out_path} ({len(points)} samples)")
|
||||
|
||||
# Write each real cluster
|
||||
for cid, pts in clusters.items():
|
||||
_write_cluster_file(cid, pts, is_noise=False)
|
||||
|
||||
# Noise as its own cluster
|
||||
if noise_points:
|
||||
_write_cluster_file(-1, noise_points, is_noise=True)
|
||||
|
||||
return written_files
|
||||
|
||||
def save_filtered_results(self, filtered_results, output_path=None):
|
||||
"""Save filtered results to JSON file"""
|
||||
if output_path is None:
|
||||
# Generate output filename based on input
|
||||
base_name = Path(self.clustering_results_path).stem
|
||||
output_path = f"{base_name}_filtered.json"
|
||||
|
||||
# Create summary statistics
|
||||
cluster_stats = {}
|
||||
noise_count = 0
|
||||
|
||||
for result in filtered_results:
|
||||
if result['is_noise']:
|
||||
noise_count += 1
|
||||
else:
|
||||
cluster_id = result['cluster']
|
||||
if cluster_id not in cluster_stats:
|
||||
cluster_stats[cluster_id] = 0
|
||||
cluster_stats[cluster_id] += 1
|
||||
|
||||
# Prepare output data
|
||||
output_data = {
|
||||
"method": "DBSCAN_FILTERED",
|
||||
"original_n_clusters": self.clustering_results['n_clusters'],
|
||||
"original_n_samples": self.clustering_results['n_samples'],
|
||||
"filtered_n_samples": len(filtered_results),
|
||||
"filtering_criteria": {
|
||||
"cluster_selection_ratio": 0.5,
|
||||
"center_points_ratio": 0.5, # 50% of selected points from center
|
||||
"border_points_ratio": 0.5, # 50% of selected points from border
|
||||
"noise_points": "all_selected"
|
||||
},
|
||||
"cluster_statistics": cluster_stats,
|
||||
"noise_points": noise_count,
|
||||
"results": filtered_results
|
||||
}
|
||||
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(output_data, f, indent=4, ensure_ascii=False)
|
||||
|
||||
print("\n" + "="*60)
|
||||
print("FILTERING SUMMARY")
|
||||
print("="*60)
|
||||
print(f"Original samples: {self.clustering_results['n_samples']}")
|
||||
print(f"Filtered samples: {len(filtered_results)}")
|
||||
print(f"Reduction ratio: {len(filtered_results)/self.clustering_results['n_samples']:.2%}")
|
||||
print("\nCluster breakdown:")
|
||||
for cluster_id, count in sorted(cluster_stats.items()):
|
||||
print(f" Cluster {cluster_id}: {count} points")
|
||||
print(f" Noise points: {noise_count} points")
|
||||
print(f"\nFiltered results saved to: {output_path}")
|
||||
|
||||
return output_path
|
||||
|
||||
def create_filepath_list(self, filtered_results, output_txt_path=None):
|
||||
"""Create a simple text file with filtered filepaths"""
|
||||
if output_txt_path is None:
|
||||
base_name = Path(self.clustering_results_path).stem
|
||||
output_txt_path = f"{base_name}_filtered_filepaths.txt"
|
||||
|
||||
filepaths = [result['filepath'] for result in filtered_results]
|
||||
|
||||
with open(output_txt_path, 'w', encoding='utf-8') as f:
|
||||
for filepath in filepaths:
|
||||
f.write(f"{filepath}\n")
|
||||
|
||||
print(f"Filepath list saved to: {output_txt_path}")
|
||||
return output_txt_path
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Filter DBSCAN clustering results")
|
||||
parser.add_argument("--embeddings_path", required=True,
|
||||
help="Path to embeddings JSON file")
|
||||
parser.add_argument("--clustering_results_path", required=True,
|
||||
help="Path to DBSCAN clustering results JSON file")
|
||||
parser.add_argument("--output_path",
|
||||
help="Output path for filtered results (optional)")
|
||||
parser.add_argument("--create_filepath_list", action="store_true",
|
||||
help="Also create a simple text file with filtered filepaths")
|
||||
parser.add_argument("--save_full_clusters", action="store_true",
|
||||
help="Save ALL points of each cluster (and noise) into separate JSON files without altering format")
|
||||
parser.add_argument("--clusters_output_dir",
|
||||
help="Directory to store per-cluster JSON files (default: current directory)")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Validate input files exist
|
||||
if not os.path.exists(args.embeddings_path):
|
||||
print(f"Error: Embeddings file not found: {args.embeddings_path}")
|
||||
return
|
||||
|
||||
if not os.path.exists(args.clustering_results_path):
|
||||
print(f"Error: Clustering results file not found: {args.clustering_results_path}")
|
||||
return
|
||||
|
||||
# Initialize filter
|
||||
filter_obj = DBSCANFilter(args.embeddings_path, args.clustering_results_path)
|
||||
|
||||
# Load data
|
||||
filter_obj.load_data()
|
||||
|
||||
# Filter clusters
|
||||
filtered_results = filter_obj.filter_all_clusters()
|
||||
|
||||
# Save results
|
||||
filter_obj.save_filtered_results(filtered_results, args.output_path)
|
||||
|
||||
# Save full clusters separately if requested
|
||||
if args.save_full_clusters:
|
||||
print("\nSaving full clusters (no filtering) separately...")
|
||||
filter_obj.save_full_clusters_separately(args.clusters_output_dir)
|
||||
|
||||
# Create filepath list if requested
|
||||
if args.create_filepath_list:
|
||||
filter_obj.create_filepath_list(filtered_results)
|
||||
|
||||
print("\nFiltering completed successfully!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
981
filter/dbscan_results_filtered_filepaths.txt
Normal file
981
filter/dbscan_results_filtered_filepaths.txt
Normal file
@@ -0,0 +1,981 @@
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/eedda33e-049b-4ab8-aee8-6f5d0c88b500_IMG_0358_1_scale_0.6.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/dd1f6f53-e26f-44a1-90af-3f0167eea8e6_IMG_1045_1_scale_0.6.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/d05b8d2e-0d0b-4354-9f3a-5cfaacf274da_image_1_scale_0.6.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/f5e670b2-9825-444b-a6c3-4f5e2a8d2882_20250206_110853_1_scale_0.6.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/6e609e6b-dd09-4dd8-9192-416dd99e3fc6_IMG_20250207_135202_1_scale_0.6.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/7e3f586b-8053-40b0-9902-485f6af20e7c_image_1_scale_0.6.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/df1aed59-e90c-44b3-b554-9486d68418da_IMG_4636_1_scale_0.8.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/6b927189-bc6c-49e5-aadc-9c58f56a6d68_IMG_2695_1_scale_0.6.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/3f09fc00-ffd5-498a-adb8-b6805519ad3a_20250212_133526_1_scale_0.6.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/dbe359c6-62bd-4697-874d-992495192860_20250212_164630_1_scale_0.6.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/1724425031238IMG_20240417_161859.jpg_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/1271a6e8-cf74-4e87-927d-abdefb4609f6_1739029106267._1_scale_0.6.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Extrait acte de naissance - 27049977-1_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/5ff72751-5d92-463c-a272-97fda77c34f8_2025-01-31-13187-Luc_Lisa_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/881d86b7-5bdb-4a16-bcbc-57202e0879b1_osteo_fevrier_2025_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/8c0d32a4-a14c-4294-9130-d34cc8fcdffa_Piece_identite__livret_famille-2_1_scale_0.8.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/caaa1340-dc45-4133-9ff4-8bac8e2f6bbd_Facture_Lunettes_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/17244250314221713370771620192636844333496435_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Extrait acte de naissance - 27060888-1_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/141f7ccc-37ab-4da5-9788-80a27e819b8b_Feuilles_de_soins_Psychologue_x2_2_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/4b0d7324-9a46-4f2c-872b-4b9c06cb0566_595570692474790_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/72347d5c-1c18-4a42-8b6c-f0a365dbaf97_FACTURE_N_14_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/46ddeec5-97bf-4175-b214-b7bd74c4fa24_Renouvin_Ferlicot_Andre_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/172415668128117241471676118541084374852557430_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture médecine douce-27781771_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/8682e432-4b4f-4152-a1d6-0e5f52eee148_Osteo_1_scale_0.6.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture des lentilles de contact-27620389_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/a87c1caa-f6cb-4fbe-a460-8f95e76217c8_IMG_20250207_100001_1_scale_0.9.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/32ffb8dc-fbd0-4edf-94b2-7b784cc72ff2_17479876010256548167621008372337_1_scale_0.6.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/b32c815a-62d9-40d2-b979-eb10e581f7a9_image_1_scale_0.6.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/DDE prestation Facture des lentilles de contact-26684583_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/33fea246-3d1b-4751-b216-3c2f5a07d26d_facture_lunettes_pat_16.5.2024_001_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/5600db4f-5884-4f54-87ce-56c8b3f5f0a0_Aurelie_GALEOTTI_Ezio__1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/preview-locataire-certificat-de-scolarite-1_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/1724156469378osteopathe_renau_anais_le_31-07-2024_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-25352522_2_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/eb5561dd-937b-4c0b-b6c6-56da8b528c94_facture_vision_claire_ophtalmo_1_001_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/1724397366229Facture_RIVALLANDChrystelle_20240413ICQO_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/8588f081-a48b-4676-90e1-cabcd66a74ad_IMG_20250207_110801_1_scale_0.9.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture médecine douce-27703140_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/831984b8-6c87-4f5d-b0a0-2d0ab5cc9061_20250207_140107_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/f02ef934-5964-4230-bfab-42bb97f20398_Scan_20250206_112050_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture-quittance toutes spécialités-27238995_1_scale_1.5.jpg
|
||||
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/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/DDE prestation Facture des lentilles de contact-26705886_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture psychologie-GED S-Z22-1003798_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-26539712_2_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-25152747_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/fee6466d-f0bb-4974-82a5-556586667c21_20250210_192626_1_scale_0.6.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-25330401_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/1724156469365facture_osteo08.24_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/k-bis-103142_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/13194c98-bb10-4227-a438-b7a1b9f2b6cb_facture_osteo_caro_08022025_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-1207573_1_scale_1.5.jpg
|
||||
496
filter/dbscan_v2.py
Normal file
496
filter/dbscan_v2.py
Normal file
@@ -0,0 +1,496 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
DBSCAN Clustering Filter
|
||||
|
||||
Filters clustering results based on specific criteria:
|
||||
- For each cluster: select 50% of points
|
||||
- 25% from center region (closest to centroid)
|
||||
- 25% from border region (furthest from centroid)
|
||||
- All noise points are selected
|
||||
- Uses cosine distance metric
|
||||
"""
|
||||
|
||||
import json
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import normalize
|
||||
from sklearn.metrics.pairwise import cosine_distances
|
||||
import argparse
|
||||
import os
|
||||
from pathlib import Path
|
||||
import random
|
||||
|
||||
|
||||
class DBSCANFilter:
|
||||
def __init__(self, embeddings_path, clustering_results_path):
|
||||
"""
|
||||
Initialize DBSCAN filter
|
||||
|
||||
Args:
|
||||
embeddings_path: Path to embeddings JSON file
|
||||
clustering_results_path: Path to DBSCAN clustering results JSON
|
||||
"""
|
||||
self.embeddings_path = embeddings_path
|
||||
self.clustering_results_path = clustering_results_path
|
||||
self.embeddings = None
|
||||
self.embeddings_normalized = None
|
||||
self.clustering_results = None
|
||||
self.filepath_to_embedding = {}
|
||||
|
||||
def load_data(self):
|
||||
"""Load embeddings and clustering results"""
|
||||
print("Loading embeddings...")
|
||||
with open(self.embeddings_path, 'r') as f:
|
||||
embeddings_data = json.load(f)
|
||||
|
||||
# Create mapping from filepath to embedding
|
||||
embeddings_list = []
|
||||
filepaths = []
|
||||
for item in embeddings_data:
|
||||
self.filepath_to_embedding[item['filepath']] = item['embedding']
|
||||
embeddings_list.append(item['embedding'])
|
||||
filepaths.append(item['filepath'])
|
||||
|
||||
self.embeddings = np.array(embeddings_list, dtype=np.float32)
|
||||
self.embeddings_normalized = normalize(self.embeddings, norm='l2')
|
||||
print(f"Loaded {len(embeddings_list)} embeddings")
|
||||
|
||||
print("Loading clustering results...")
|
||||
with open(self.clustering_results_path, 'r') as f:
|
||||
self.clustering_results = json.load(f)
|
||||
|
||||
print(f"Loaded clustering results: {self.clustering_results['n_clusters']} clusters, "
|
||||
f"{self.clustering_results['n_samples']} samples")
|
||||
|
||||
def group_by_clusters(self):
|
||||
"""Group data points by cluster labels"""
|
||||
clusters = {}
|
||||
noise_points = []
|
||||
|
||||
for result in self.clustering_results['results']:
|
||||
cluster_id = result['cluster']
|
||||
filepath = result['filepath']
|
||||
if 'is_noise' in result:
|
||||
is_noise = result['is_noise']
|
||||
else:
|
||||
is_noise = False
|
||||
|
||||
if is_noise or cluster_id == -1:
|
||||
noise_points.append({
|
||||
'filepath': filepath,
|
||||
'embedding': self.filepath_to_embedding[filepath]
|
||||
})
|
||||
else:
|
||||
if cluster_id not in clusters:
|
||||
clusters[cluster_id] = []
|
||||
clusters[cluster_id].append({
|
||||
'filepath': filepath,
|
||||
'embedding': self.filepath_to_embedding[filepath]
|
||||
})
|
||||
|
||||
return clusters, noise_points
|
||||
|
||||
def calculate_cluster_centroid(self, cluster_points):
|
||||
"""Calculate centroid of a cluster using normalized embeddings"""
|
||||
embeddings = np.array([point['embedding'] for point in cluster_points])
|
||||
embeddings_normalized = normalize(embeddings, norm='l2')
|
||||
|
||||
# For cosine distance, centroid is the normalized mean
|
||||
centroid = np.mean(embeddings_normalized, axis=0)
|
||||
centroid_normalized = normalize(centroid.reshape(1, -1), norm='l2')[0]
|
||||
|
||||
return centroid_normalized
|
||||
|
||||
def calculate_cosine_distances_to_centroid(self, cluster_points, centroid):
|
||||
"""Calculate cosine distances from each point to cluster centroid"""
|
||||
embeddings = np.array([point['embedding'] for point in cluster_points])
|
||||
embeddings_normalized = normalize(embeddings, norm='l2')
|
||||
|
||||
# Calculate cosine distances to centroid
|
||||
distances = cosine_distances(embeddings_normalized, centroid.reshape(1, -1)).flatten()
|
||||
|
||||
return distances
|
||||
|
||||
# v1 0.5 data, 0.5 center 0.5 border
|
||||
|
||||
# v2 0.5 data, 0.25 center 0.75 border
|
||||
# def filter_cluster(self, cluster_points, selection_ratio=0.5):
|
||||
|
||||
# v3 0.75 data, 0.25 center 0.75 border
|
||||
|
||||
|
||||
#dbscan 014
|
||||
# def filter_cluster(self, cluster_points, selection_ratio=0.3):
|
||||
|
||||
# """
|
||||
# Filter points from a cluster
|
||||
|
||||
# Args:
|
||||
# cluster_points: List of points in the cluster
|
||||
# selection_ratio: Ratio of points to select (default: 0.5 = 50%)
|
||||
|
||||
# Returns:
|
||||
# List of selected points
|
||||
# """
|
||||
# if len(cluster_points) == 0:
|
||||
# return []
|
||||
|
||||
|
||||
# # Calculate how many points to select
|
||||
# total_points = len(cluster_points)
|
||||
# num_to_select = max(15, int(total_points * selection_ratio))
|
||||
|
||||
# # If we need to select all or almost all points, just return all
|
||||
# if num_to_select >= total_points:
|
||||
# return cluster_points
|
||||
|
||||
# # Calculate centroid
|
||||
# centroid = self.calculate_cluster_centroid(cluster_points)
|
||||
|
||||
# # Calculate distances to centroid
|
||||
# distances = self.calculate_cosine_distances_to_centroid(cluster_points, centroid)
|
||||
|
||||
# # Create list of (point, distance) pairs
|
||||
# point_distance_pairs = list(zip(cluster_points, distances))
|
||||
|
||||
# # Sort by distance (closest to furthest from centroid)
|
||||
# point_distance_pairs.sort(key=lambda x: x[1])
|
||||
|
||||
# dis = 0.1
|
||||
# # count_center = sum(1 for pair in point_distance_pairs if pair[1] < dis)
|
||||
# all_center_points = [pair[0] for pair in point_distance_pairs if pair[1] < dis]
|
||||
# print(f"Number of center points (distance < {dis}): {len(all_center_points)}")
|
||||
|
||||
# # count_border = sum(1 for pair in point_distance_pairs if pair[1] >= dis)
|
||||
# all_border_points = [pair[0] for pair in point_distance_pairs if pair[1] >= dis]
|
||||
# print(f"Number of border points (distance >= {dis}): {len(all_border_points)}")
|
||||
|
||||
# # Calculate how many points to select from center and border
|
||||
# n_center = len(all_center_points)
|
||||
# center_count = max(1, int(n_center * 0.15))
|
||||
|
||||
# n_border = len(all_border_points)
|
||||
# if n_border < 70:
|
||||
# border_count = n_border
|
||||
# else:
|
||||
# border_count = max(0, int(n_border * 0.3)) # remaining from border
|
||||
|
||||
# selected_points = []
|
||||
|
||||
# random.seed(42)
|
||||
# # Select center points (closest to centroid)
|
||||
# # center_points = [pair[0] for pair in point_distance_pairs[:center_count]]
|
||||
# center_points = random.sample(all_center_points, center_count)
|
||||
# selected_points.extend(center_points)
|
||||
|
||||
# # # Select border points (furthest from centroid)
|
||||
# if border_count > 0:
|
||||
# # border_points = [pair[0] for pair in point_distance_pairs[-border_count:]]
|
||||
# border_points = random.sample(all_border_points, border_count)
|
||||
# selected_points.extend(border_points)
|
||||
|
||||
# print(f"Cluster with {total_points} points -> selected {len(selected_points)} points "
|
||||
# f"({center_count} center + {border_count} border)")
|
||||
|
||||
# return selected_points
|
||||
|
||||
# dbscan 015
|
||||
def filter_cluster(self, cluster_points, selection_ratio=0.3):
|
||||
|
||||
"""
|
||||
Filter points from a cluster
|
||||
|
||||
Args:
|
||||
cluster_points: List of points in the cluster
|
||||
selection_ratio: Ratio of points to select (default: 0.5 = 50%)
|
||||
|
||||
Returns:
|
||||
List of selected points
|
||||
"""
|
||||
if len(cluster_points) == 0:
|
||||
return []
|
||||
|
||||
|
||||
# Calculate how many points to select
|
||||
total_points = len(cluster_points)
|
||||
num_to_select = max(15, int(total_points * selection_ratio))
|
||||
|
||||
# If we need to select all or almost all points, just return all
|
||||
if num_to_select >= total_points:
|
||||
return cluster_points
|
||||
|
||||
# Calculate centroid
|
||||
centroid = self.calculate_cluster_centroid(cluster_points)
|
||||
|
||||
# Calculate distances to centroid
|
||||
distances = self.calculate_cosine_distances_to_centroid(cluster_points, centroid)
|
||||
|
||||
# Create list of (point, distance) pairs
|
||||
point_distance_pairs = list(zip(cluster_points, distances))
|
||||
|
||||
# Sort by distance (closest to furthest from centroid)
|
||||
point_distance_pairs.sort(key=lambda x: x[1])
|
||||
|
||||
dis = 0.1
|
||||
# count_center = sum(1 for pair in point_distance_pairs if pair[1] < dis)
|
||||
all_center_points = [pair[0] for pair in point_distance_pairs if pair[1] < dis]
|
||||
print(f"Number of center points (distance < {dis}): {len(all_center_points)}")
|
||||
|
||||
# count_border = sum(1 for pair in point_distance_pairs if pair[1] >= dis)
|
||||
all_border_points = [pair[0] for pair in point_distance_pairs if pair[1] >= dis]
|
||||
print(f"Number of border points (distance >= {dis}): {len(all_border_points)}")
|
||||
|
||||
# Calculate how many points to select from center and border
|
||||
n_center = len(all_center_points)
|
||||
center_count = max(1, int(n_center * 0.15))
|
||||
|
||||
n_border = len(all_border_points)
|
||||
if n_border < 70:
|
||||
border_count = n_border
|
||||
else:
|
||||
border_count = max(0, int(n_border * 0.3)) # remaining from border
|
||||
|
||||
selected_points = []
|
||||
|
||||
random.seed(42)
|
||||
# Select center points (closest to centroid)
|
||||
# center_points = [pair[0] for pair in point_distance_pairs[:center_count]]
|
||||
center_points = random.sample(all_center_points, center_count)
|
||||
selected_points.extend(center_points)
|
||||
|
||||
# # Select border points (furthest from centroid)
|
||||
if border_count > 0:
|
||||
# border_points = [pair[0] for pair in point_distance_pairs[-border_count:]]
|
||||
border_points = random.sample(all_border_points, border_count)
|
||||
selected_points.extend(border_points)
|
||||
|
||||
print(f"Cluster with {total_points} points -> selected {len(selected_points)} points "
|
||||
f"({center_count} center + {border_count} border)")
|
||||
|
||||
return selected_points
|
||||
|
||||
#gmm
|
||||
# def filter_cluster(self, cluster_points, selection_ratio=0.3):
|
||||
|
||||
# """
|
||||
# Filter points from a cluster
|
||||
|
||||
# Args:
|
||||
# cluster_points: List of points in the cluster
|
||||
# selection_ratio: Ratio of points to select (default: 0.5 = 50%)
|
||||
|
||||
# Returns:
|
||||
# List of selected points
|
||||
# """
|
||||
# if len(cluster_points) == 0:
|
||||
# return []
|
||||
|
||||
|
||||
# # Calculate how many points to select
|
||||
# total_points = len(cluster_points)
|
||||
# num_to_select = max(15, int(total_points * selection_ratio))
|
||||
|
||||
# # If we need to select all or almost all points, just return all
|
||||
# if num_to_select >= total_points:
|
||||
# return cluster_points
|
||||
|
||||
# # Calculate centroid
|
||||
# centroid = self.calculate_cluster_centroid(cluster_points)
|
||||
|
||||
# # Calculate distances to centroid
|
||||
# distances = self.calculate_cosine_distances_to_centroid(cluster_points, centroid)
|
||||
|
||||
# # Create list of (point, distance) pairs
|
||||
# point_distance_pairs = list(zip(cluster_points, distances))
|
||||
|
||||
# # Sort by distance (closest to furthest from centroid)
|
||||
# point_distance_pairs.sort(key=lambda x: x[1])
|
||||
|
||||
# dis = 0.2
|
||||
# # count_center = sum(1 for pair in point_distance_pairs if pair[1] < dis)
|
||||
# all_center_points = [pair[0] for pair in point_distance_pairs if pair[1] < dis]
|
||||
# print(f"Number of center points (distance < {dis}): {len(all_center_points)}")
|
||||
|
||||
# # count_border = sum(1 for pair in point_distance_pairs if pair[1] >= dis)
|
||||
# all_border_points = [pair[0] for pair in point_distance_pairs if pair[1] >= dis]
|
||||
# print(f"Number of border points (distance >= {dis}): {len(all_border_points)}")
|
||||
|
||||
# # Calculate how many points to select from center and border
|
||||
# n_center = len(all_center_points)
|
||||
# center_count = max(1, int(n_center * 0.15))
|
||||
|
||||
# n_border = len(all_border_points)
|
||||
# if n_border < 70:
|
||||
# border_count = n_border
|
||||
# else:
|
||||
# border_count = max(0, int(n_border * 0.3)) # remaining from border
|
||||
|
||||
# selected_points = []
|
||||
|
||||
# random.seed(42)
|
||||
# # Select center points (closest to centroid)
|
||||
# # center_points = [pair[0] for pair in point_distance_pairs[:center_count]]
|
||||
# center_points = random.sample(all_center_points, center_count)
|
||||
# selected_points.extend(center_points)
|
||||
|
||||
# # # Select border points (furthest from centroid)
|
||||
# if border_count > 0:
|
||||
# # border_points = [pair[0] for pair in point_distance_pairs[-border_count:]]
|
||||
# border_points = random.sample(all_border_points, border_count)
|
||||
# selected_points.extend(border_points)
|
||||
|
||||
# print(f"Cluster with {total_points} points -> selected {len(selected_points)} points "
|
||||
# f"({center_count} center + {border_count} border)")
|
||||
|
||||
# return selected_points
|
||||
|
||||
def filter_all_clusters(self):
|
||||
"""Filter all clusters according to the specified criteria"""
|
||||
print("\n" + "="*60)
|
||||
print("FILTERING DBSCAN CLUSTERING RESULTS")
|
||||
print("="*60)
|
||||
|
||||
clusters, noise_points = self.group_by_clusters()
|
||||
|
||||
print(f"Found {len(clusters)} clusters and {len(noise_points)} noise points")
|
||||
|
||||
filtered_results = []
|
||||
|
||||
# Process each cluster
|
||||
for cluster_id, cluster_points in clusters.items():
|
||||
print(f"\nProcessing Cluster {cluster_id}:")
|
||||
filtered_points = self.filter_cluster(cluster_points)
|
||||
|
||||
# Add cluster information
|
||||
for point in filtered_points:
|
||||
filtered_results.append({
|
||||
'filepath': point['filepath'],
|
||||
'cluster': cluster_id,
|
||||
'is_noise': False,
|
||||
'selection_type': 'cluster_filtered'
|
||||
})
|
||||
|
||||
# Add all noise points
|
||||
print(f"\nAdding all {len(noise_points)} noise points...")
|
||||
for point in noise_points:
|
||||
filtered_results.append({
|
||||
'filepath': point['filepath'],
|
||||
'cluster': -1,
|
||||
'is_noise': True,
|
||||
'selection_type': 'noise'
|
||||
})
|
||||
|
||||
return filtered_results
|
||||
|
||||
def save_filtered_results(self, filtered_results, output_path=None):
|
||||
"""Save filtered results to JSON file"""
|
||||
if output_path is None:
|
||||
# Generate output filename based on input
|
||||
base_name = Path(self.clustering_results_path).stem
|
||||
output_path = f"{base_name}_filtered.json"
|
||||
|
||||
# Create summary statistics
|
||||
cluster_stats = {}
|
||||
noise_count = 0
|
||||
|
||||
for result in filtered_results:
|
||||
if result['is_noise']:
|
||||
noise_count += 1
|
||||
else:
|
||||
cluster_id = result['cluster']
|
||||
if cluster_id not in cluster_stats:
|
||||
cluster_stats[cluster_id] = 0
|
||||
cluster_stats[cluster_id] += 1
|
||||
|
||||
# Prepare output data
|
||||
output_data = {
|
||||
"method": "DBSCAN_FILTERED",
|
||||
"original_n_clusters": self.clustering_results['n_clusters'],
|
||||
"original_n_samples": self.clustering_results['n_samples'],
|
||||
"filtered_n_samples": len(filtered_results),
|
||||
"filtering_criteria": {
|
||||
"cluster_selection_ratio": 0.5,
|
||||
"center_points_ratio": 0.5, # 50% of selected points from center
|
||||
"border_points_ratio": 0.5, # 50% of selected points from border
|
||||
"noise_points": "all_selected"
|
||||
},
|
||||
"cluster_statistics": cluster_stats,
|
||||
"noise_points": noise_count,
|
||||
"results": filtered_results
|
||||
}
|
||||
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(output_data, f, indent=4, ensure_ascii=False)
|
||||
|
||||
print("\n" + "="*60)
|
||||
print("FILTERING SUMMARY")
|
||||
print("="*60)
|
||||
print(f"Original samples: {self.clustering_results['n_samples']}")
|
||||
print(f"Filtered samples: {len(filtered_results)}")
|
||||
print(f"Reduction ratio: {len(filtered_results)/self.clustering_results['n_samples']:.2%}")
|
||||
print("\nCluster breakdown:")
|
||||
for cluster_id, count in sorted(cluster_stats.items()):
|
||||
print(f" Cluster {cluster_id}: {count} points")
|
||||
print(f" Noise points: {noise_count} points")
|
||||
print(f"\nFiltered results saved to: {output_path}")
|
||||
|
||||
return output_path
|
||||
|
||||
def create_filepath_list(self, filtered_results, output_txt_path=None):
|
||||
"""Create a simple text file with filtered filepaths"""
|
||||
if output_txt_path is None:
|
||||
base_name = Path(self.clustering_results_path).stem
|
||||
output_txt_path = f"{base_name}_filtered_filepaths.txt"
|
||||
|
||||
filepaths = [result['filepath'] for result in filtered_results]
|
||||
|
||||
with open(output_txt_path, 'w', encoding='utf-8') as f:
|
||||
for filepath in filepaths:
|
||||
f.write(f"{filepath}\n")
|
||||
|
||||
print(f"Filepath list saved to: {output_txt_path}")
|
||||
return output_txt_path
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Filter DBSCAN clustering results")
|
||||
parser.add_argument("--embeddings_path", required=True,
|
||||
help="Path to embeddings JSON file")
|
||||
parser.add_argument("--clustering_results_path", required=True,
|
||||
help="Path to DBSCAN clustering results JSON file")
|
||||
parser.add_argument("--output_path",
|
||||
help="Output path for filtered results (optional)")
|
||||
parser.add_argument("--create_filepath_list", action="store_true",
|
||||
help="Also create a simple text file with filtered filepaths")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Validate input files exist
|
||||
if not os.path.exists(args.embeddings_path):
|
||||
print(f"Error: Embeddings file not found: {args.embeddings_path}")
|
||||
return
|
||||
|
||||
if not os.path.exists(args.clustering_results_path):
|
||||
print(f"Error: Clustering results file not found: {args.clustering_results_path}")
|
||||
return
|
||||
|
||||
# Initialize filter
|
||||
filter_obj = DBSCANFilter(args.embeddings_path, args.clustering_results_path)
|
||||
|
||||
# Load data
|
||||
filter_obj.load_data()
|
||||
|
||||
# Filter clusters
|
||||
filtered_results = filter_obj.filter_all_clusters()
|
||||
|
||||
# Save results
|
||||
filter_obj.save_filtered_results(filtered_results, args.output_path)
|
||||
|
||||
# Create filepath list if requested
|
||||
if args.create_filepath_list:
|
||||
filter_obj.create_filepath_list(filtered_results)
|
||||
|
||||
print("\nFiltering completed successfully!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
352
filter/dbscan_v3.py
Normal file
352
filter/dbscan_v3.py
Normal file
@@ -0,0 +1,352 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
DBSCAN Clustering Filter
|
||||
|
||||
Filters clustering results based on specific criteria, parameterized via CLI:
|
||||
- For each cluster: select a configurable ratio of points (selection_ratio)
|
||||
- A configurable portion from center region (center_ratio)
|
||||
- A configurable portion from border region (border_ratio)
|
||||
- All noise points are selected
|
||||
- Uses cosine distance metric
|
||||
|
||||
CLI parameters added:
|
||||
--selection_ratio (float, default 0.5)
|
||||
--center_ratio (float, default 0.5)
|
||||
--border_ratio (float, default 0.5)
|
||||
|
||||
Example:
|
||||
python dbscan_v3.py \
|
||||
--embeddings_path embeddings.json \
|
||||
--clustering_results_path dbscan_results.json \
|
||||
--selection_ratio 0.4 --center_ratio 0.6 --border_ratio 0.4
|
||||
"""
|
||||
|
||||
import json
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import normalize
|
||||
from sklearn.metrics.pairwise import cosine_distances
|
||||
import argparse
|
||||
import os
|
||||
from pathlib import Path
|
||||
import random
|
||||
|
||||
|
||||
class DBSCANFilter:
|
||||
def __init__(self, embeddings_path, clustering_results_path,
|
||||
selection_ratio=0.5, center_ratio=0.5, border_ratio=0.5):
|
||||
"""Initialize DBSCAN filter
|
||||
|
||||
Args:
|
||||
embeddings_path: Path to embeddings JSON file
|
||||
clustering_results_path: Path to DBSCAN clustering results JSON
|
||||
selection_ratio: Ratio of total cluster points to consider selecting
|
||||
center_ratio: Ratio applied within center region (relative scaling)
|
||||
border_ratio: Ratio applied within border region (relative scaling)
|
||||
"""
|
||||
self.embeddings_path = embeddings_path
|
||||
self.clustering_results_path = clustering_results_path
|
||||
self.embeddings = None
|
||||
self.embeddings_normalized = None
|
||||
self.clustering_results = None
|
||||
self.filepath_to_embedding = {}
|
||||
self.selection_ratio = selection_ratio
|
||||
self.center_ratio = center_ratio
|
||||
self.border_ratio = border_ratio
|
||||
|
||||
def load_data(self):
|
||||
"""Load embeddings and clustering results"""
|
||||
print("Loading embeddings...")
|
||||
with open(self.embeddings_path, 'r') as f:
|
||||
embeddings_data = json.load(f)
|
||||
|
||||
# Create mapping from filepath to embedding
|
||||
embeddings_list = []
|
||||
filepaths = []
|
||||
for item in embeddings_data:
|
||||
self.filepath_to_embedding[item['filepath']] = item['embedding']
|
||||
embeddings_list.append(item['embedding'])
|
||||
filepaths.append(item['filepath'])
|
||||
|
||||
self.embeddings = np.array(embeddings_list, dtype=np.float32)
|
||||
self.embeddings_normalized = normalize(self.embeddings, norm='l2')
|
||||
print(f"Loaded {len(embeddings_list)} embeddings")
|
||||
|
||||
print("Loading clustering results...")
|
||||
with open(self.clustering_results_path, 'r') as f:
|
||||
self.clustering_results = json.load(f)
|
||||
|
||||
print(f"Loaded clustering results: {self.clustering_results['n_clusters']} clusters, "
|
||||
f"{self.clustering_results['n_samples']} samples")
|
||||
|
||||
def group_by_clusters(self):
|
||||
"""Group data points by cluster labels"""
|
||||
clusters = {}
|
||||
noise_points = []
|
||||
|
||||
for result in self.clustering_results['results']:
|
||||
cluster_id = result['cluster']
|
||||
filepath = result['filepath']
|
||||
if 'is_noise' in result:
|
||||
is_noise = result['is_noise']
|
||||
else:
|
||||
is_noise = False
|
||||
|
||||
if is_noise or cluster_id == -1:
|
||||
noise_points.append({
|
||||
'filepath': filepath,
|
||||
'embedding': self.filepath_to_embedding[filepath]
|
||||
})
|
||||
else:
|
||||
if cluster_id not in clusters:
|
||||
clusters[cluster_id] = []
|
||||
clusters[cluster_id].append({
|
||||
'filepath': filepath,
|
||||
'embedding': self.filepath_to_embedding[filepath]
|
||||
})
|
||||
|
||||
return clusters, noise_points
|
||||
|
||||
def calculate_cluster_centroid(self, cluster_points):
|
||||
"""Calculate centroid of a cluster using normalized embeddings"""
|
||||
embeddings = np.array([point['embedding'] for point in cluster_points])
|
||||
embeddings_normalized = normalize(embeddings, norm='l2')
|
||||
|
||||
# For cosine distance, centroid is the normalized mean
|
||||
centroid = np.mean(embeddings_normalized, axis=0)
|
||||
centroid_normalized = normalize(centroid.reshape(1, -1), norm='l2')[0]
|
||||
|
||||
return centroid_normalized
|
||||
|
||||
def calculate_cosine_distances_to_centroid(self, cluster_points, centroid):
|
||||
"""Calculate cosine distances from each point to cluster centroid"""
|
||||
embeddings = np.array([point['embedding'] for point in cluster_points])
|
||||
embeddings_normalized = normalize(embeddings, norm='l2')
|
||||
|
||||
# Calculate cosine distances to centroid
|
||||
distances = cosine_distances(embeddings_normalized, centroid.reshape(1, -1)).flatten()
|
||||
|
||||
return distances
|
||||
|
||||
def filter_cluster(self, cluster_points):
|
||||
"""Lọc điểm trong một cluster dựa trên các tham số đã cấu hình."""
|
||||
if not cluster_points:
|
||||
return []
|
||||
|
||||
selection_ratio = self.selection_ratio
|
||||
center_ratio = self.center_ratio
|
||||
border_ratio = self.border_ratio
|
||||
|
||||
total_points = len(cluster_points)
|
||||
num_to_select = max(15, int(total_points * selection_ratio))
|
||||
if num_to_select >= total_points and selection_ratio != 1:
|
||||
return cluster_points
|
||||
|
||||
centroid = self.calculate_cluster_centroid(cluster_points)
|
||||
distances = self.calculate_cosine_distances_to_centroid(cluster_points, centroid)
|
||||
point_distance_pairs = list(zip(cluster_points, distances))
|
||||
point_distance_pairs.sort(key=lambda x: x[1])
|
||||
|
||||
dis = 0.1 # ngưỡng khoảng cách để phân loại center / border
|
||||
all_center_points = [p for p, d in point_distance_pairs if d < dis]
|
||||
all_border_points = [p for p, d in point_distance_pairs if d >= dis]
|
||||
|
||||
print(f"Number of center points (distance < {dis}): {len(all_center_points)}")
|
||||
print(f"Number of border points (distance >= {dis}): {len(all_border_points)}")
|
||||
|
||||
n_center = len(all_center_points)
|
||||
n_border = len(all_border_points)
|
||||
|
||||
if n_center > 0:
|
||||
center_count = max(1, int(n_center * center_ratio * selection_ratio))
|
||||
center_count = min(center_count, n_center)
|
||||
else:
|
||||
center_count = 0
|
||||
|
||||
if n_border < 70:
|
||||
border_count = n_border
|
||||
else:
|
||||
border_count = max(0, int(n_border * border_ratio * selection_ratio))
|
||||
border_count = min(border_count, n_border)
|
||||
|
||||
random.seed(42)
|
||||
selected_points = []
|
||||
if center_count > 0:
|
||||
selected_points.extend(random.sample(all_center_points, center_count))
|
||||
if border_count > 0:
|
||||
selected_points.extend(random.sample(all_border_points, border_count))
|
||||
|
||||
print(
|
||||
f"Cluster with {total_points} points -> selected {len(selected_points)} points "
|
||||
f"({center_count} center + {border_count} border)"
|
||||
)
|
||||
return selected_points
|
||||
|
||||
def filter_all_clusters(self):
|
||||
"""Filter all clusters according to the specified criteria"""
|
||||
print("\n" + "="*60)
|
||||
print("FILTERING DBSCAN CLUSTERING RESULTS")
|
||||
print("="*60)
|
||||
|
||||
clusters, noise_points = self.group_by_clusters()
|
||||
|
||||
print(f"Found {len(clusters)} clusters and {len(noise_points)} noise points")
|
||||
|
||||
filtered_results = []
|
||||
|
||||
# Process each cluster
|
||||
for cluster_id, cluster_points in clusters.items():
|
||||
print(f"\nProcessing Cluster {cluster_id}:")
|
||||
filtered_points = self.filter_cluster(cluster_points)
|
||||
|
||||
# Add cluster information
|
||||
for point in filtered_points:
|
||||
filtered_results.append({
|
||||
'filepath': point['filepath'],
|
||||
'cluster': cluster_id,
|
||||
'is_noise': False,
|
||||
'selection_type': 'cluster_filtered'
|
||||
})
|
||||
|
||||
# Add all noise points
|
||||
print(f"\nAdding all {len(noise_points)} noise points...")
|
||||
n_noise = len(noise_points)
|
||||
noise_count = max(0, int(n_noise * self.selection_ratio))
|
||||
random.seed(42)
|
||||
selected_noise_points = random.sample(noise_points, noise_count)
|
||||
|
||||
for point in selected_noise_points:
|
||||
filtered_results.append({
|
||||
'filepath': point['filepath'],
|
||||
'cluster': -1,
|
||||
'is_noise': True,
|
||||
'selection_type': 'noise'
|
||||
})
|
||||
|
||||
return filtered_results
|
||||
|
||||
def save_filtered_results(self, filtered_results, output_path=None):
|
||||
"""Save filtered results to JSON file"""
|
||||
if output_path is None:
|
||||
# Generate output filename based on input
|
||||
base_name = Path(self.clustering_results_path).stem
|
||||
output_path = f"{base_name}_filtered.json"
|
||||
|
||||
# Create summary statistics
|
||||
cluster_stats = {}
|
||||
noise_count = 0
|
||||
|
||||
for result in filtered_results:
|
||||
if result['is_noise']:
|
||||
noise_count += 1
|
||||
else:
|
||||
cluster_id = result['cluster']
|
||||
if cluster_id not in cluster_stats:
|
||||
cluster_stats[cluster_id] = 0
|
||||
cluster_stats[cluster_id] += 1
|
||||
|
||||
# Prepare output data
|
||||
output_data = {
|
||||
"method": "DBSCAN_FILTERED",
|
||||
"original_n_clusters": self.clustering_results['n_clusters'],
|
||||
"original_n_samples": self.clustering_results['n_samples'],
|
||||
"filtered_n_samples": len(filtered_results),
|
||||
"filtering_criteria": {
|
||||
"cluster_selection_ratio": self.selection_ratio,
|
||||
"center_points_ratio": self.center_ratio,
|
||||
"border_points_ratio": self.border_ratio,
|
||||
"noise_points": "all_selected"
|
||||
},
|
||||
"cluster_statistics": cluster_stats,
|
||||
"noise_points": noise_count,
|
||||
"results": filtered_results
|
||||
}
|
||||
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(output_data, f, indent=4, ensure_ascii=False)
|
||||
|
||||
print("\n" + "="*60)
|
||||
print("FILTERING SUMMARY")
|
||||
print("="*60)
|
||||
print(f"Original samples: {self.clustering_results['n_samples']}")
|
||||
print(f"Filtered samples: {len(filtered_results)}")
|
||||
print(f"Reduction ratio: {len(filtered_results)/self.clustering_results['n_samples']:.2%}")
|
||||
print("\nCluster breakdown:")
|
||||
for cluster_id, count in sorted(cluster_stats.items()):
|
||||
print(f" Cluster {cluster_id}: {count} points")
|
||||
print(f" Noise points: {noise_count} points")
|
||||
print(f"\nFiltered results saved to: {output_path}")
|
||||
|
||||
return output_path
|
||||
|
||||
def create_filepath_list(self, filtered_results, output_txt_path=None):
|
||||
"""Create a simple text file with filtered filepaths"""
|
||||
if output_txt_path is None:
|
||||
base_name = Path(self.clustering_results_path).stem
|
||||
output_txt_path = f"{base_name}_filtered_filepaths.txt"
|
||||
|
||||
filepaths = [result['filepath'] for result in filtered_results]
|
||||
|
||||
with open(output_txt_path, 'w', encoding='utf-8') as f:
|
||||
for filepath in filepaths:
|
||||
f.write(f"{filepath}\n")
|
||||
|
||||
print(f"Filepath list saved to: {output_txt_path}")
|
||||
return output_txt_path
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Filter DBSCAN clustering results")
|
||||
parser.add_argument("--embeddings_path", required=True,
|
||||
help="Path to embeddings JSON file")
|
||||
parser.add_argument("--clustering_results_path", required=True,
|
||||
help="Path to DBSCAN clustering results JSON file")
|
||||
parser.add_argument("--output_path",
|
||||
help="Output path for filtered results (optional)")
|
||||
parser.add_argument("--create_filepath_list", action="store_true",
|
||||
help="Also create a simple text file with filtered filepaths")
|
||||
parser.add_argument("--selection_ratio", type=float, default=0.5,
|
||||
help="Overall ratio of points to sample per cluster (default: 0.5). Minimum 15 points enforced.")
|
||||
parser.add_argument("--center_ratio", type=float, default=0.5,
|
||||
help="Relative ratio applied to center region when sampling (default: 0.5)")
|
||||
parser.add_argument("--border_ratio", type=float, default=0.5,
|
||||
help="Relative ratio applied to border region when sampling (default: 0.5)")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Validate input files exist
|
||||
if not os.path.exists(args.embeddings_path):
|
||||
print(f"Error: Embeddings file not found: {args.embeddings_path}")
|
||||
return
|
||||
|
||||
if not os.path.exists(args.clustering_results_path):
|
||||
print(f"Error: Clustering results file not found: {args.clustering_results_path}")
|
||||
return
|
||||
|
||||
# Initialize filter
|
||||
# Initialize filter with user-provided ratios
|
||||
filter_obj = DBSCANFilter(
|
||||
args.embeddings_path,
|
||||
args.clustering_results_path,
|
||||
selection_ratio=args.selection_ratio,
|
||||
center_ratio=args.center_ratio,
|
||||
border_ratio=args.border_ratio
|
||||
)
|
||||
|
||||
# Load data
|
||||
filter_obj.load_data()
|
||||
|
||||
# Filter clusters
|
||||
filtered_results = filter_obj.filter_all_clusters()
|
||||
|
||||
# Save results
|
||||
filter_obj.save_filtered_results(filtered_results, args.output_path)
|
||||
|
||||
# Create filepath list if requested
|
||||
if args.create_filepath_list:
|
||||
filter_obj.create_filepath_list(filtered_results)
|
||||
|
||||
print("\nFiltering completed successfully!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
376
filter/fillter_all.py
Normal file
376
filter/fillter_all.py
Normal file
@@ -0,0 +1,376 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Universal Clustering Filter
|
||||
|
||||
Filters clustering results for multiple algorithms:
|
||||
- DBSCAN: handles noise points, uses density-based selection
|
||||
- GMM: uses probability-based selection, no noise points
|
||||
- K-Means: standard centroid-based selection
|
||||
|
||||
Uses cosine distance metric for all calculations.
|
||||
"""
|
||||
|
||||
import json
|
||||
import numpy as np
|
||||
from sklearn.preprocessing import normalize
|
||||
from sklearn.metrics.pairwise import cosine_distances
|
||||
import argparse
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
class UniversalClusterFilter:
|
||||
def __init__(self, embeddings_path, clustering_results_path):
|
||||
"""
|
||||
Initialize universal cluster filter
|
||||
|
||||
Args:
|
||||
embeddings_path: Path to embeddings JSON file
|
||||
clustering_results_path: Path to clustering results JSON
|
||||
"""
|
||||
self.embeddings_path = embeddings_path
|
||||
self.clustering_results_path = clustering_results_path
|
||||
self.embeddings = None
|
||||
self.embeddings_normalized = None
|
||||
self.clustering_results = None
|
||||
self.filepath_to_embedding = {}
|
||||
self.algorithm = None
|
||||
|
||||
def load_data(self):
|
||||
"""Load embeddings and clustering results"""
|
||||
print("Loading embeddings...")
|
||||
with open(self.embeddings_path, 'r') as f:
|
||||
embeddings_data = json.load(f)
|
||||
|
||||
# Create mapping from filepath to embedding
|
||||
embeddings_list = []
|
||||
filepaths = []
|
||||
for item in embeddings_data:
|
||||
self.filepath_to_embedding[item['filepath']] = item['embedding']
|
||||
embeddings_list.append(item['embedding'])
|
||||
filepaths.append(item['filepath'])
|
||||
|
||||
self.embeddings = np.array(embeddings_list, dtype=np.float32)
|
||||
self.embeddings_normalized = normalize(self.embeddings, norm='l2')
|
||||
print(f"Loaded {len(embeddings_list)} embeddings")
|
||||
|
||||
print("Loading clustering results...")
|
||||
with open(self.clustering_results_path, 'r') as f:
|
||||
self.clustering_results = json.load(f)
|
||||
|
||||
# Detect algorithm type
|
||||
self.algorithm = self.clustering_results.get('method', 'UNKNOWN')
|
||||
print(f"Detected algorithm: {self.algorithm}")
|
||||
print(f"Loaded clustering results: {self.clustering_results['n_clusters']} clusters, "
|
||||
f"{self.clustering_results['n_samples']} samples")
|
||||
|
||||
def group_by_clusters(self):
|
||||
"""Group data points by cluster labels (algorithm-agnostic)"""
|
||||
clusters = {}
|
||||
noise_points = []
|
||||
|
||||
for result in self.clustering_results['results']:
|
||||
cluster_id = result['cluster']
|
||||
filepath = result['filepath']
|
||||
|
||||
# Check for noise points (DBSCAN specific)
|
||||
is_noise = result.get('is_noise', False)
|
||||
|
||||
if is_noise or cluster_id == -1:
|
||||
noise_points.append({
|
||||
'filepath': filepath,
|
||||
'embedding': self.filepath_to_embedding[filepath],
|
||||
'metadata': result
|
||||
})
|
||||
else:
|
||||
if cluster_id not in clusters:
|
||||
clusters[cluster_id] = []
|
||||
clusters[cluster_id].append({
|
||||
'filepath': filepath,
|
||||
'embedding': self.filepath_to_embedding[filepath],
|
||||
'metadata': result
|
||||
})
|
||||
|
||||
return clusters, noise_points
|
||||
|
||||
def calculate_cluster_centroid(self, cluster_points):
|
||||
"""Calculate centroid of a cluster using normalized embeddings"""
|
||||
embeddings = np.array([point['embedding'] for point in cluster_points])
|
||||
embeddings_normalized = normalize(embeddings, norm='l2')
|
||||
|
||||
# For cosine distance, centroid is the normalized mean
|
||||
centroid = np.mean(embeddings_normalized, axis=0)
|
||||
centroid_normalized = normalize(centroid.reshape(1, -1), norm='l2')[0]
|
||||
|
||||
return centroid_normalized
|
||||
|
||||
def calculate_cosine_distances_to_centroid(self, cluster_points, centroid):
|
||||
"""Calculate cosine distances from each point to cluster centroid"""
|
||||
embeddings = np.array([point['embedding'] for point in cluster_points])
|
||||
embeddings_normalized = normalize(embeddings, norm='l2')
|
||||
|
||||
# Calculate cosine distances to centroid
|
||||
distances = cosine_distances(embeddings_normalized, centroid.reshape(1, -1)).flatten()
|
||||
|
||||
return distances
|
||||
|
||||
def filter_cluster_standard(self, cluster_points, selection_ratio=0.5):
|
||||
"""
|
||||
Standard filtering: 25% center + 75% border of selected points
|
||||
"""
|
||||
if len(cluster_points) == 0:
|
||||
return []
|
||||
|
||||
# Calculate how many points to select
|
||||
total_points = len(cluster_points)
|
||||
num_to_select = max(1, int(total_points * selection_ratio))
|
||||
|
||||
# If we need to select all or almost all points, just return all
|
||||
if num_to_select >= total_points:
|
||||
return cluster_points
|
||||
|
||||
# Calculate centroid
|
||||
centroid = self.calculate_cluster_centroid(cluster_points)
|
||||
|
||||
# Calculate distances to centroid
|
||||
distances = self.calculate_cosine_distances_to_centroid(cluster_points, centroid)
|
||||
|
||||
# Create list of (point, distance) pairs
|
||||
point_distance_pairs = list(zip(cluster_points, distances))
|
||||
|
||||
# Sort by distance (closest to furthest from centroid)
|
||||
point_distance_pairs.sort(key=lambda x: x[1])
|
||||
|
||||
# Calculate how many points to select from center and border
|
||||
center_count = max(1, int(num_to_select * 0.25)) # 25% from center
|
||||
border_count = num_to_select - center_count # 75% from border
|
||||
|
||||
selected_points = []
|
||||
|
||||
# Select center points (closest to centroid)
|
||||
center_points = [pair[0] for pair in point_distance_pairs[:center_count]]
|
||||
for point in center_points:
|
||||
point['selection_type'] = 'center'
|
||||
selected_points.extend(center_points)
|
||||
|
||||
# Select border points (furthest from centroid)
|
||||
if border_count > 0:
|
||||
border_points = [pair[0] for pair in point_distance_pairs[-border_count:]]
|
||||
for point in border_points:
|
||||
point['selection_type'] = 'border'
|
||||
selected_points.extend(border_points)
|
||||
|
||||
print(f"Cluster with {total_points} points -> selected {len(selected_points)} points "
|
||||
f"({center_count} center + {border_count} border)")
|
||||
|
||||
return selected_points
|
||||
|
||||
def filter_cluster_gmm(self, cluster_points, selection_ratio=0.5):
|
||||
"""
|
||||
GMM-specific filtering: consider probability scores if available
|
||||
"""
|
||||
if len(cluster_points) == 0:
|
||||
return []
|
||||
|
||||
# Check if we have probability scores
|
||||
has_probabilities = any('probability' in point['metadata'] for point in cluster_points)
|
||||
|
||||
if has_probabilities:
|
||||
# Use probability-based selection
|
||||
total_points = len(cluster_points)
|
||||
num_to_select = max(1, int(total_points * selection_ratio))
|
||||
|
||||
if num_to_select >= total_points:
|
||||
return cluster_points
|
||||
|
||||
# Sort by probability (highest confidence first)
|
||||
sorted_points = sorted(cluster_points,
|
||||
key=lambda x: x['metadata'].get('probability', 0),
|
||||
reverse=True)
|
||||
|
||||
# Take top probability points
|
||||
selected_points = sorted_points[:num_to_select]
|
||||
for point in selected_points:
|
||||
point['selection_type'] = 'high_probability'
|
||||
|
||||
print(f"GMM Cluster with {total_points} points -> selected {len(selected_points)} points "
|
||||
f"(top probability)")
|
||||
|
||||
return selected_points
|
||||
else:
|
||||
# Fall back to standard filtering
|
||||
return self.filter_cluster_standard(cluster_points, selection_ratio)
|
||||
|
||||
def filter_all_clusters(self, selection_ratio=0.5):
|
||||
"""Filter all clusters according to algorithm-specific criteria"""
|
||||
print("\n" + "="*60)
|
||||
print(f"FILTERING {self.algorithm} CLUSTERING RESULTS")
|
||||
print("="*60)
|
||||
|
||||
clusters, noise_points = self.group_by_clusters()
|
||||
|
||||
print(f"Found {len(clusters)} clusters and {len(noise_points)} noise points")
|
||||
|
||||
filtered_results = []
|
||||
|
||||
# Process each cluster
|
||||
for cluster_id, cluster_points in clusters.items():
|
||||
print(f"\nProcessing Cluster {cluster_id}:")
|
||||
|
||||
# Choose filtering method based on algorithm
|
||||
if self.algorithm.upper() == 'GMM' or 'GAUSSIAN' in self.algorithm.upper():
|
||||
filtered_points = self.filter_cluster_gmm(cluster_points, selection_ratio)
|
||||
else:
|
||||
filtered_points = self.filter_cluster_standard(cluster_points, selection_ratio)
|
||||
|
||||
# Add cluster information
|
||||
for point in filtered_points:
|
||||
filtered_results.append({
|
||||
'filepath': point['filepath'],
|
||||
'cluster': cluster_id,
|
||||
'is_noise': False,
|
||||
'selection_type': point.get('selection_type', 'cluster_filtered'),
|
||||
'original_metadata': point['metadata']
|
||||
})
|
||||
|
||||
# Add all noise points (DBSCAN only)
|
||||
if noise_points:
|
||||
print(f"\nAdding all {len(noise_points)} noise points...")
|
||||
for point in noise_points:
|
||||
filtered_results.append({
|
||||
'filepath': point['filepath'],
|
||||
'cluster': -1,
|
||||
'is_noise': True,
|
||||
'selection_type': 'noise',
|
||||
'original_metadata': point['metadata']
|
||||
})
|
||||
|
||||
return filtered_results
|
||||
|
||||
def save_filtered_results(self, filtered_results, output_path=None):
|
||||
"""Save filtered results to JSON file"""
|
||||
if output_path is None:
|
||||
base_name = Path(self.clustering_results_path).stem
|
||||
output_path = f"{base_name}_filtered.json"
|
||||
|
||||
# Create summary statistics
|
||||
cluster_stats = {}
|
||||
noise_count = 0
|
||||
selection_type_stats = {}
|
||||
|
||||
for result in filtered_results:
|
||||
# Cluster stats
|
||||
if result['is_noise']:
|
||||
noise_count += 1
|
||||
else:
|
||||
cluster_id = result['cluster']
|
||||
if cluster_id not in cluster_stats:
|
||||
cluster_stats[cluster_id] = 0
|
||||
cluster_stats[cluster_id] += 1
|
||||
|
||||
# Selection type stats
|
||||
sel_type = result['selection_type']
|
||||
selection_type_stats[sel_type] = selection_type_stats.get(sel_type, 0) + 1
|
||||
|
||||
# Prepare output data
|
||||
output_data = {
|
||||
"method": f"{self.algorithm}_FILTERED",
|
||||
"original_algorithm": self.algorithm,
|
||||
"original_n_clusters": self.clustering_results['n_clusters'],
|
||||
"original_n_samples": self.clustering_results['n_samples'],
|
||||
"filtered_n_samples": len(filtered_results),
|
||||
"filtering_criteria": {
|
||||
"cluster_selection_ratio": 0.5,
|
||||
"center_points_ratio": 0.25,
|
||||
"border_points_ratio": 0.75,
|
||||
"noise_points": "all_selected" if noise_count > 0 else "none_present"
|
||||
},
|
||||
"cluster_statistics": cluster_stats,
|
||||
"selection_type_statistics": selection_type_stats,
|
||||
"noise_points": noise_count,
|
||||
"results": filtered_results
|
||||
}
|
||||
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(output_data, f, indent=4, ensure_ascii=False)
|
||||
|
||||
print("\n" + "="*60)
|
||||
print("FILTERING SUMMARY")
|
||||
print("="*60)
|
||||
print(f"Algorithm: {self.algorithm}")
|
||||
print(f"Original samples: {self.clustering_results['n_samples']}")
|
||||
print(f"Filtered samples: {len(filtered_results)}")
|
||||
print(f"Reduction ratio: {len(filtered_results)/self.clustering_results['n_samples']:.2%}")
|
||||
print("\nCluster breakdown:")
|
||||
for cluster_id, count in sorted(cluster_stats.items()):
|
||||
print(f" Cluster {cluster_id}: {count} points")
|
||||
if noise_count > 0:
|
||||
print(f" Noise points: {noise_count} points")
|
||||
print("\nSelection type breakdown:")
|
||||
for sel_type, count in selection_type_stats.items():
|
||||
print(f" {sel_type}: {count} points")
|
||||
print(f"\nFiltered results saved to: {output_path}")
|
||||
|
||||
return output_path
|
||||
|
||||
def create_filepath_list(self, filtered_results, output_txt_path=None):
|
||||
"""Create a simple text file with filtered filepaths"""
|
||||
if output_txt_path is None:
|
||||
base_name = Path(self.clustering_results_path).stem
|
||||
output_txt_path = f"{base_name}_filtered_filepaths.txt"
|
||||
|
||||
filepaths = [result['filepath'] for result in filtered_results]
|
||||
|
||||
with open(output_txt_path, 'w', encoding='utf-8') as f:
|
||||
for filepath in filepaths:
|
||||
f.write(f"{filepath}\n")
|
||||
|
||||
print(f"Filepath list saved to: {output_txt_path}")
|
||||
return output_txt_path
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Universal filter for clustering results")
|
||||
parser.add_argument("--embeddings_path", required=True,
|
||||
help="Path to embeddings JSON file")
|
||||
parser.add_argument("--clustering_results_path", required=True,
|
||||
help="Path to clustering results JSON file")
|
||||
parser.add_argument("--output_path",
|
||||
help="Output path for filtered results (optional)")
|
||||
parser.add_argument("--selection_ratio", type=float, default=0.5,
|
||||
help="Ratio of points to select from each cluster (default: 0.5)")
|
||||
parser.add_argument("--create_filepath_list", action="store_true",
|
||||
help="Also create a simple text file with filtered filepaths")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Validate input files exist
|
||||
if not os.path.exists(args.embeddings_path):
|
||||
print(f"Error: Embeddings file not found: {args.embeddings_path}")
|
||||
return
|
||||
|
||||
if not os.path.exists(args.clustering_results_path):
|
||||
print(f"Error: Clustering results file not found: {args.clustering_results_path}")
|
||||
return
|
||||
|
||||
# Initialize filter
|
||||
filter_obj = UniversalClusterFilter(args.embeddings_path, args.clustering_results_path)
|
||||
|
||||
# Load data
|
||||
filter_obj.load_data()
|
||||
|
||||
# Filter clusters
|
||||
filtered_results = filter_obj.filter_all_clusters(args.selection_ratio)
|
||||
|
||||
# Save results
|
||||
filter_obj.save_filtered_results(filtered_results, args.output_path)
|
||||
|
||||
# Create filepath list if requested
|
||||
if args.create_filepath_list:
|
||||
filter_obj.create_filepath_list(filtered_results)
|
||||
|
||||
print("\nFiltering completed successfully!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
105
filter/generate_dbscan_results_filter.py
Normal file
105
filter/generate_dbscan_results_filter.py
Normal file
@@ -0,0 +1,105 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Generate filtered DBSCAN results based on images listed in 009_label_data_sample_seed_42.json.
|
||||
|
||||
Rules (v1): keep only entries whose base filename (before _<idx>_scale_* suffix or extension)
|
||||
can be matched with any "image" field in the label JSON. Matching strategy:
|
||||
- Extract filename without extension from each DBSCAN result filepath.
|
||||
- Remove trailing pattern: _<digits>_scale_<anything>
|
||||
- For each image name from labels, keep the DBSCAN entry if:
|
||||
* cleaned_filename startswith(image_name) OR
|
||||
* image_name startswith(cleaned_filename) OR
|
||||
* image_name in cleaned_filename OR cleaned_filename in image_name
|
||||
- This permissive logic aims to cover variations; can be tightened later.
|
||||
|
||||
Outputs file: dbscan_results_filter.json in the same folder with structure:
|
||||
{
|
||||
"method": "DBSCAN_FILTERED",
|
||||
"n_samples": <count_of_original>,
|
||||
"n_filtered": <count_after_filter>,
|
||||
"results": [ ...filtered entries... ]
|
||||
}
|
||||
"""
|
||||
from __future__ import annotations
|
||||
import json
|
||||
import re
|
||||
from pathlib import Path
|
||||
|
||||
BASE_DIR = Path(__file__).resolve().parent
|
||||
# LABEL_FILE = BASE_DIR / "009_label_data_sample_seed_42.json"
|
||||
LABEL_FILE = BASE_DIR / "015_label_data_sample_seed_1997.json"
|
||||
LABEL_FILE = BASE_DIR / "008_label_data_sample_seed_1997.json"
|
||||
|
||||
|
||||
|
||||
# Prefer the clustering output in cluster/ directory relative to project root
|
||||
# DBSCAN_FILE_CANDIDATES = [
|
||||
# BASE_DIR.parent / "cluster" / "dbscan_results.json",
|
||||
# BASE_DIR.parent / "result" / "dbscan_results.json",
|
||||
# ]
|
||||
|
||||
DBSCAN_FILE_CANDIDATES = [
|
||||
BASE_DIR.parent / "cluster" / "dbscan_results.json",
|
||||
BASE_DIR.parent / "result" / "dbscan_results.json",
|
||||
]
|
||||
OUTPUT_FILE = BASE_DIR / "dbscan_results_filter_008.json"
|
||||
|
||||
def load_first_existing(paths):
|
||||
for p in paths:
|
||||
if p.exists():
|
||||
return p
|
||||
raise FileNotFoundError("No dbscan_results.json file found in expected locations: " + ", ".join(map(str, paths)))
|
||||
|
||||
def normalize_image_name(name: str) -> str:
|
||||
return name.strip()
|
||||
|
||||
def cleaned_filename(path: str) -> str:
|
||||
stem = Path(path).stem # remove extension
|
||||
stem = re.sub(r"_[0-9]+_scale_.*$", "", stem)
|
||||
return stem
|
||||
|
||||
def build_image_name_set(label_path: Path) -> set[str]:
|
||||
data = json.loads(label_path.read_text())
|
||||
names: set[str] = set()
|
||||
for entry in data:
|
||||
if not isinstance(entry, dict):
|
||||
continue
|
||||
img = entry.get("image")
|
||||
if isinstance(img, str) and img.strip():
|
||||
names.add(normalize_image_name(img))
|
||||
return names
|
||||
|
||||
def match_entry(core_name: str, image_names: set[str]) -> bool:
|
||||
for img in image_names:
|
||||
if core_name.startswith(img) or img.startswith(core_name):
|
||||
return True
|
||||
if img in core_name or core_name in img:
|
||||
return True
|
||||
return False
|
||||
|
||||
def main():
|
||||
dbscan_file = load_first_existing(DBSCAN_FILE_CANDIDATES)
|
||||
image_names = build_image_name_set(LABEL_FILE)
|
||||
raw = json.loads(dbscan_file.read_text())
|
||||
results = raw.get("results", [])
|
||||
filtered = []
|
||||
for rec in results:
|
||||
filepath = rec.get("filepath")
|
||||
if not filepath:
|
||||
continue
|
||||
core = cleaned_filename(filepath)
|
||||
if match_entry(core, image_names):
|
||||
filtered.append(rec)
|
||||
out = {
|
||||
"method": "DBSCAN_FILTERED",
|
||||
"n_samples": len(results),
|
||||
"n_filtered": len(filtered),
|
||||
"match_ratio": round(len(filtered) / len(results), 4) if results else 0.0,
|
||||
"source_file": str(dbscan_file),
|
||||
"label_source": str(LABEL_FILE),
|
||||
"results": filtered,
|
||||
}
|
||||
OUTPUT_FILE.write_text(json.dumps(out, indent=2, ensure_ascii=False))
|
||||
print(f"Wrote {len(filtered)} filtered results to {OUTPUT_FILE}")
|
||||
|
||||
if __name__ == "__main__": # pragma: no cover
|
||||
main()
|
||||
1454
filter/gmm_final_results_aic_20250805_150636_filtered_filepaths.txt
Normal file
1454
filter/gmm_final_results_aic_20250805_150636_filtered_filepaths.txt
Normal file
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,460 @@
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture-quittance toutes spécialités-26471195_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-26539711_1_scale_1.5.jpg
|
||||
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|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/eb5561dd-937b-4c0b-b6c6-56da8b528c94_facture_vision_claire_ophtalmo_1_001_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/7bad16be-dff9-4b92-8041-4a9c38638e29_facturation_osteo-1_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-25330401_2_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/2af3fe93-a285-4ced-898b-90b4106bef8c_osteo_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture des lentilles de contact-27646193_2_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture médecine douce-27691255_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture des lentilles de contact-27576127_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/a9f88b8f-a751-478e-9338-2d31fb1062e7_image_1_scale_0.6.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture médecine douce-27717154_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture médecine douce-27784971_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Quittance de Loyer 11.24 Mr NGUYEN-1_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/DDE prestation Facture des lentilles de contact-26691031_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture des lentilles de contact-27653858_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-25279751_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/0cae671e-6b7f-41aa-a0ab-d74b8e15e424_DIMART_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-1196314_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture des lentilles de contact-27554784_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/1724658376437factosteo_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-25919222_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/DDE prestation Facture des lentilles de contact-26729570_2_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/1724226151715Facture_Julie_Boissel_2_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/1d3b5d22-87f2-4c89-90e5-8b0350bca84e_facture__Surre_Chloe_03_fevrier_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/04fde30d-7891-439d-85e7-285a0d8ffee1_IMG_1550_1_scale_0.6.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-25273058_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/2b3ed78c-1a3c-40b3-b997-0e74f6ba497f_FENOLLAR_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/1724156469373facture_psy_DOMI_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-26107527_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/d317e79c-0485-42f8-b24d-413d94e7a779_Livret_de_famille-1_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture médecine douce-27710953_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-1106092_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/834a074c-811f-4d27-9c19-134a4b9ab2ea_FACTURE-25-02-208-GEORGES_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/c7bbe834-632d-4fb6-8e24-c9c5caf488bc_IMG_4966_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/DDE prestation Facture des lentilles de contact-26723200_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/f8c6fe47-9608-4f9e-938b-117bfa4dcd30_ostheopatie_2025-02-12_Grenot-JLuc_Mme-Tabary_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/f0395d11-18fb-4b9f-bee1-5ca75d656d14_image_1_scale_0.6.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture des lentilles de contact-27664783_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-26592477_2_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/abd25cab-e2d5-456c-ac6c-d209b698daf2_Le_Monnier_Fauche_Nolan_facture_2025-01-31_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-1105977_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture-quittance toutes spécialités-27474258_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/8d5e5dfe-71a8-4a05-a376-8727cd264ffd_osteopathe_do_2_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-25187936_2_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/1724658126141JACOB_Francois_2024-111_2_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-26368529_2_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-25263730_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/Facture ostéopathe-25330401_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/ab26e47b-d267-439c-9b12-8699c17d5d47_facture_Aurelie_Claude_naturopathe_2_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/47a3ed6d-aade-4da2-8006-1ec798c5e915_Stiegler_Gary_1_scale_1.5.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/f30627ed-f84d-4e7e-aad5-fd4e06475247_livret-de-famille-1-2_1_scale_1.0.jpg
|
||||
/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/1724156469377Facture_DAURISFlorian_20240815CGEI_1_scale_1.5.jpg
|
||||
2
filter/log_gmm_extensive_update.txt
Normal file
2
filter/log_gmm_extensive_update.txt
Normal file
@@ -0,0 +1,2 @@
|
||||
nohup: ignoring input
|
||||
python: can't open file '/home/nguyendc/sonnh/embedding-clustering/filter/gmm_extensive.py': [Errno 2] No such file or directory
|
||||
111
filter/random_sample_data.py
Normal file
111
filter/random_sample_data.py
Normal file
@@ -0,0 +1,111 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Script để random pick 50% samples từ label_data.json với 3 seeds khác nhau
|
||||
và lưu thành 3 files riêng biệt.
|
||||
"""
|
||||
|
||||
import json
|
||||
import random
|
||||
import os
|
||||
from typing import List, Dict, Any
|
||||
|
||||
def load_json_data(file_path: str) -> List[Dict[str, Any]]:
|
||||
"""Load dữ liệu từ file JSON"""
|
||||
try:
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
data = json.load(f)
|
||||
print(f"✅ Đã load {len(data)} samples từ {file_path}")
|
||||
return data
|
||||
except FileNotFoundError:
|
||||
print(f"❌ Không tìm thấy file: {file_path}")
|
||||
return []
|
||||
except json.JSONDecodeError as e:
|
||||
print(f"❌ Lỗi decode JSON: {e}")
|
||||
return []
|
||||
|
||||
def random_sample_data(data: List[Dict[str, Any]], seed: int, sample_ratio: float = 0.5, sample_size = None) -> List[Dict[str, Any]]:
|
||||
"""Random pick samples từ data với seed và tỷ lệ sampling cụ thể"""
|
||||
random.seed(seed)
|
||||
if sample_size is None:
|
||||
sample_size = int(len(data) * sample_ratio)
|
||||
sampled_data = random.sample(data, sample_size)
|
||||
print(f"🎲 Seed {seed}: Đã sample {len(sampled_data)}/{len(data)} samples ({sample_ratio*100:.1f}%)")
|
||||
return sampled_data
|
||||
|
||||
def save_json_data(data: List[Dict[str, Any]], file_path: str) -> bool:
|
||||
"""Lưu dữ liệu vào file JSON"""
|
||||
try:
|
||||
with open(file_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(data, f, ensure_ascii=False, indent=2)
|
||||
print(f"💾 Đã lưu {len(data)} samples vào {file_path}")
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"❌ Lỗi khi lưu file {file_path}: {e}")
|
||||
return False
|
||||
|
||||
def main():
|
||||
"""Hàm chính"""
|
||||
# Đường dẫn file input
|
||||
input_file = "label_data.json"
|
||||
|
||||
# Các seeds cần sử dụng
|
||||
seeds = [1997, 42, 2025]
|
||||
# seeds = [42]
|
||||
|
||||
# Tỷ lệ sampling (50%)
|
||||
|
||||
# 8, 9, 10
|
||||
sample_ratio = 0.5
|
||||
|
||||
# 11,12,13
|
||||
sample_ratio = 0.3
|
||||
|
||||
# 16
|
||||
# sample_ratio = 0.01
|
||||
|
||||
# 15, 17, 18
|
||||
sample_size = 200
|
||||
|
||||
print("🔄 Bắt đầu quá trình random sampling...")
|
||||
print("=" * 50)
|
||||
|
||||
# Load dữ liệu gốc
|
||||
original_data = load_json_data(input_file)
|
||||
if not original_data:
|
||||
print("❌ Không thể load dữ liệu. Thoát chương trình.")
|
||||
return
|
||||
|
||||
print(f"📊 Tổng số samples trong file gốc: {len(original_data)}")
|
||||
print(f"🎯 Sẽ sample {sample_ratio*100:.1f}% = {int(len(original_data) * sample_ratio)} samples")
|
||||
print("=" * 50)
|
||||
|
||||
# Thực hiện sampling với từng seed
|
||||
for seed in seeds:
|
||||
print(f"\n🌱 Xử lý với seed: {seed}")
|
||||
|
||||
# Random sample data
|
||||
sampled_data = random_sample_data(original_data, seed, sample_ratio, sample_size = sample_size)
|
||||
|
||||
# Tạo tên file output
|
||||
output_file = f"label_data_sample_seed_{seed}.json"
|
||||
|
||||
# Lưu file
|
||||
success = save_json_data(sampled_data, output_file)
|
||||
|
||||
if success:
|
||||
print(f"✅ Hoàn thành sampling với seed {seed}")
|
||||
else:
|
||||
print(f"❌ Lỗi khi xử lý seed {seed}")
|
||||
|
||||
print("\n" + "=" * 50)
|
||||
print("🎉 Hoàn thành toàn bộ quá trình!")
|
||||
print("\n📁 Các file đã được tạo:")
|
||||
for seed in seeds:
|
||||
output_file = f"016_no_fine_tune_label_data_sample_seed_{seed}.json"
|
||||
if os.path.exists(output_file):
|
||||
print(f" ✅ {output_file}")
|
||||
else:
|
||||
print(f" ❌ {output_file} (không tồn tại)")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
138
filter/run_filter.sh
Normal file
138
filter/run_filter.sh
Normal file
@@ -0,0 +1,138 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Example script to run DBSCAN filtering
|
||||
# Make sure to update the paths according to your data
|
||||
|
||||
# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json"
|
||||
# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/dbscan_results.json"
|
||||
# OUTPUT_PATH="dbscan_filtered_results.json" #0.5 of data, center 0.5, 0.5 border
|
||||
# OUTPUT_PATH="dbscan_filtered_results_v2.json" #0.5 of data, 0.25 center, 0.75 border
|
||||
# OUTPUT_PATH="dbscan_filtered_results_v3.json" #0.75 of data, center 0.25 border 0.75
|
||||
|
||||
|
||||
|
||||
# echo "Running DBSCAN filtering..."
|
||||
# echo "Embeddings: $EMBEDDINGS_PATH"
|
||||
# echo "Clustering results: $CLUSTERING_RESULTS_PATH"
|
||||
# echo "Output: $OUTPUT_PATH"
|
||||
|
||||
# python dbscan.py \
|
||||
# --embeddings_path "$EMBEDDINGS_PATH" \
|
||||
# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \
|
||||
# --output_path "$OUTPUT_PATH" \
|
||||
# --create_filepath_list
|
||||
|
||||
# echo "Filtering completed!"
|
||||
|
||||
# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json"
|
||||
# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/gmm_final_results_bic_20250805_150636.json"
|
||||
# OUTPUT_PATH="gmm_best_by_BIC_filtered_results.json" #0.75 of data, center 0.25 border 0.75
|
||||
|
||||
|
||||
# echo "Running DBSCAN filtering..."
|
||||
# echo "Embeddings: $EMBEDDINGS_PATH"
|
||||
# echo "Clustering results: $CLUSTERING_RESULTS_PATH"
|
||||
# echo "Output: $OUTPUT_PATH"
|
||||
|
||||
# python dbscan.py \
|
||||
# --embeddings_path "$EMBEDDINGS_PATH" \
|
||||
# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \
|
||||
# --output_path "$OUTPUT_PATH" \
|
||||
# --create_filepath_list
|
||||
|
||||
|
||||
|
||||
# echo "Filtering completed!"
|
||||
|
||||
|
||||
|
||||
# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json"
|
||||
# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/dbscan_results.json"
|
||||
# OUTPUT_PATH="014_re_run_dbscan_filtered_results_temp.json" #0.75 of data, center 0.25 border 0.75
|
||||
# python dbscan_v2.py \
|
||||
# --embeddings_path "$EMBEDDINGS_PATH" \
|
||||
# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \
|
||||
# --output_path "$OUTPUT_PATH" \
|
||||
# --create_filepath_list
|
||||
|
||||
|
||||
# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json"
|
||||
# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/gmm_final_results_bic_20250805_150636.json"
|
||||
# OUTPUT_PATH="015_gmm_best_by_BIC_filtered_results_temp.json" #0.75 of data, center 0.25 border 0.75
|
||||
# python dbscan_v2.py \
|
||||
# --embeddings_path "$EMBEDDINGS_PATH" \
|
||||
# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \
|
||||
# --output_path "$OUTPUT_PATH" \
|
||||
# --create_filepath_list
|
||||
|
||||
# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json"
|
||||
# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/dbscan_results.json"
|
||||
# OUTPUT_DIR="dbscan_v3_out_clusters"
|
||||
|
||||
# # python dbscan_v3_only_one_cluster.py \
|
||||
# # --embeddings_path "$EMBEDDINGS_PATH" \
|
||||
# # --clustering_results_path "$CLUSTERING_RESULTS_PATH" \
|
||||
# # --output_dir "$OUTPUT_DIR" \
|
||||
|
||||
# python dbscan_only_one_cluster.py \
|
||||
# --embeddings_path "$EMBEDDINGS_PATH" \
|
||||
# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \
|
||||
# --save_full_clusters \
|
||||
# --clusters_output_dir per_clusters
|
||||
|
||||
# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json"
|
||||
# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/dbscan_results.json"
|
||||
# OUTPUT_PATH="026_dbscan_v3_filtered_results_temp.json"
|
||||
|
||||
# python dbscan_v3.py \
|
||||
# --embeddings_path "$EMBEDDINGS_PATH" \
|
||||
# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \
|
||||
# --output_path "$OUTPUT_PATH" \
|
||||
# --create_filepath_list \
|
||||
# --selection_ratio 1.0 --center_ratio 0.5 --border_ratio 0.5
|
||||
|
||||
# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json"
|
||||
# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/dbscan_results.json"
|
||||
# OUTPUT_PATH="027_dbscan_v3_filtered_results_temp.json"
|
||||
|
||||
# python dbscan_v3.py \
|
||||
# --embeddings_path "$EMBEDDINGS_PATH" \
|
||||
# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \
|
||||
# --output_path "$OUTPUT_PATH" \
|
||||
# --create_filepath_list \
|
||||
# --selection_ratio 1.0 --center_ratio 0.25 --border_ratio 0.75
|
||||
|
||||
# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json"
|
||||
# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/dbscan_results.json"
|
||||
# OUTPUT_PATH="019_dbscan_v3_filtered_results_temp.json"
|
||||
|
||||
# python dbscan_v3.py \
|
||||
# --embeddings_path "$EMBEDDINGS_PATH" \
|
||||
# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \
|
||||
# --output_path "$OUTPUT_PATH" \
|
||||
# --create_filepath_list \
|
||||
# --selection_ratio 0.12 --center_ratio 0.25 --border_ratio 0.75
|
||||
|
||||
|
||||
# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json"
|
||||
# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/gmm_final_results_bic_20250805_150636.json"
|
||||
# OUTPUT_PATH="028_gmm_best_by_BIC_filtered_results.json" #0.75 of data, center 0.25 border 0.75
|
||||
|
||||
# python dbscan_v3.py \
|
||||
# --embeddings_path "$EMBEDDINGS_PATH" \
|
||||
# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \
|
||||
# --output_path "$OUTPUT_PATH" \
|
||||
# --create_filepath_list \
|
||||
# --selection_ratio 1.0 --center_ratio 0.25 --border_ratio 0.75
|
||||
|
||||
|
||||
EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json"
|
||||
CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/dbscan_results.json"
|
||||
OUTPUT_PATH="029_dbscan_v3_filtered_results_temp_30.json" #0.75 of data, center 0.25 border 0.75
|
||||
|
||||
python dbscan_v3.py \
|
||||
--embeddings_path "$EMBEDDINGS_PATH" \
|
||||
--clustering_results_path "$CLUSTERING_RESULTS_PATH" \
|
||||
--output_path "$OUTPUT_PATH" \
|
||||
--create_filepath_list \
|
||||
--selection_ratio 0.6 --center_ratio 0.5 --border_ratio 0.5
|
||||
12
filter/shuffle.py
Normal file
12
filter/shuffle.py
Normal file
@@ -0,0 +1,12 @@
|
||||
import random
|
||||
|
||||
import json
|
||||
|
||||
with open('/home/nguyendc/sonnh/embedding-clustering/filter/008_label_data_sample_seed_1997.json', 'r') as f:
|
||||
data = json.load(f)
|
||||
|
||||
random.seed(8272025)
|
||||
random.shuffle(data)
|
||||
|
||||
with open('/home/nguyendc/sonnh/embedding-clustering/filter/032_shuffle_008.json', 'w') as f:
|
||||
json.dump(data, f)
|
||||
Reference in New Issue
Block a user