Metadata-Version: 2.4
Name: worlds-data-filter
Version: 0.1.1
Summary: The World's Data Filter — find the most valuable data, first with a universal SDK/CLI that ranks, filters, and subsets heterogeneous data by information gain, novelty, and quality.
Author: The World's Data Company
License: Apache License
        Version 2.0, January 2004
        http://www.apache.org/licenses/
        
        TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
        1. Definitions.
        
        "License" shall mean the terms and conditions for use, reproduction, and
        distribution as defined by Sections 1 through 9 of this document.
        
        "Licensor" shall mean the copyright owner or entity authorized by the
        copyright owner that is granting the License.
        
        "Legal Entity" shall mean the union of the acting entity and all other
        entities that control, are controlled by, or are under common control with
        that entity. For the purposes of this definition, "control" means (i) the
        power, direct or indirect, to cause the direction or management of such
        entity, whether by contract or otherwise, or (ii) ownership of fifty percent
        (50%) or more of the outstanding shares, or (iii) beneficial ownership of such
        entity.
        
        "You" (or "Your") shall mean an individual or Legal Entity exercising
        permissions granted by this License.
        
        "Source" form shall mean the preferred form for making modifications,
        including but not limited to software source code, documentation source, and
        configuration files.
        
        "Object" form shall mean any form resulting from mechanical transformation or
        translation of a Source form, including but not limited to compiled object
        code, generated documentation, and conversions to other media types.
        
        "Work" shall mean the work of authorship, whether in Source or Object form,
        made available under the License, as indicated by a copyright notice that is
        included in or attached to the work (an example is provided in the Appendix
        below).
        
        "Derivative Works" shall mean any work, whether in Source or Object form,
        that is based on (or derived from) the Work and for which the editorial
        revisions, annotations, elaborations, or other modifications represent, as a
        whole, an original work of authorship. For the purposes of this License,
        Derivative Works shall not include works that remain separable from, or merely
        link (or bind by name) to the interfaces of, the Work and Derivative Works
        thereof.
        
        "Contribution" shall mean any work of authorship, including the original
        version of the Work and any modifications or additions to that Work or
        Derivative Works thereof, that is intentionally submitted to Licensor for
        inclusion in the Work by the copyright owner or by an individual or Legal
        Entity authorized to submit on behalf of the copyright owner. For the purposes
        of this definition, "submitted" means any form of electronic, verbal, or
        written communication sent to the Licensor or its representatives, including
        but not limited to communication on electronic mailing lists, source code
        control systems, and issue tracking systems that are managed by, or on behalf
        of, the Licensor for the purpose of discussing and improving the Work, but
        excluding communication that is conspicuously marked or otherwise designated
        in writing by the copyright owner as "Not a Contribution."
        
        "Contributor" shall mean Licensor and any individual or Legal Entity on
        behalf of whom a Contribution has been received by Licensor and subsequently
        incorporated within the Work.
        
        2. Grant of Copyright License. Subject to the terms and conditions of this
        License, each Contributor hereby grants to You a perpetual, worldwide,
        non-exclusive, no-charge, royalty-free, irrevocable copyright license to
        reproduce, prepare Derivative Works of, publicly display, publicly perform,
        sublicense, and distribute the Work and such Derivative Works in Source or
        Object form.
        
        3. Grant of Patent License. Subject to the terms and conditions of this
        License, each Contributor hereby grants to You a perpetual, worldwide,
        non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this
        section) patent license to make, have made, use, offer to sell, sell, import,
        and otherwise transfer the Work, where such license applies only to those
        patent claims licensable by such Contributor that are necessarily infringed by
        their Contribution(s) alone or by combination of their Contribution(s) with
        the Work to which such Contribution(s) was submitted. If You institute patent
        litigation against any entity (including a cross-claim or counterclaim in a
        lawsuit) alleging that the Work or a Contribution incorporated within the Work
        constitutes direct or contributory patent infringement, then any patent
        licenses granted to You under this License for that Work shall terminate as of
        the date such litigation is filed.
        
        4. Redistribution. You may reproduce and distribute copies of the Work or
        Derivative Works thereof in any medium, with or without modifications, and in
        Source or Object form, provided that You meet the following conditions:
        
        (a) You must give any other recipients of the Work or Derivative Works a copy
        of this License; and
        
        (b) You must cause any modified files to carry prominent notices stating that
        You changed the files; and
        
        (c) You must retain, in the Source form of any Derivative Works that You
        distribute, all copyright, patent, trademark, and attribution notices from the
        Source form of the Work, excluding those notices that do not pertain to any
        part of the Derivative Works; and
        
        (d) If the Work includes a "NOTICE" text file as part of its distribution,
        then any Derivative Works that You distribute must include a readable copy of
        the attribution notices contained within such NOTICE file, excluding those
        notices that do not pertain to any part of the Derivative Works, in at least
        one of the following places: within a NOTICE text file distributed as part of
        the Derivative Works; within the Source form or documentation, if provided
        along with the Derivative Works; or, within a display generated by the
        Derivative Works, if and wherever such third-party notices normally appear.
        The contents of the NOTICE file are for informational purposes only and do not
        modify the License. You may add Your own attribution notices within Derivative
        Works that You distribute, alongside or as an addendum to the NOTICE text from
        the Work, provided that such additional attribution notices cannot be
        construed as modifying the License.
        
        You may add Your own copyright statement to Your modifications and may provide
        additional or different license terms and conditions for use, reproduction, or
        distribution of Your modifications, or for any such Derivative Works as a
        whole, provided Your use, reproduction, and distribution of the Work otherwise
        complies with the conditions stated in this License.
        
        5. Submission of Contributions. Unless You explicitly state otherwise, any
        Contribution intentionally submitted for inclusion in the Work by You to the
        Licensor shall be under the terms and conditions of this License, without any
        additional terms or conditions. Notwithstanding the above, nothing herein
        shall supersede or modify the terms of any separate license agreement you may
        have executed with Licensor regarding such Contributions.
        
        6. Trademarks. This License does not grant permission to use the trade names,
        trademarks, service marks, or product names of the Licensor, except as
        required for reasonable and customary use in describing the origin of the Work
        and reproducing the content of the NOTICE file.
        
        7. Disclaimer of Warranty. Unless required by applicable law or agreed to in
        writing, Licensor provides the Work (and each Contributor provides its
        Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
        KIND, either express or implied, including, without limitation, any warranties
        or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
        PARTICULAR PURPOSE. You are solely responsible for determining the
        appropriateness of using or redistributing the Work and assume any risks
        associated with Your exercise of permissions under this License.
        
        8. Limitation of Liability. In no event and under no legal theory, whether in
        tort (including negligence), contract, or otherwise, unless required by
        applicable law (such as deliberate and grossly negligent acts) or agreed to in
        writing, shall any Contributor be liable to You for damages, including any
        direct, indirect, special, incidental, or consequential damages of any
        character arising as a result of this License or out of the use or inability
        to use the Work (including but not limited to damages for loss of goodwill,
        work stoppage, computer failure or malfunction, or any and all other
        commercial damages or losses), even if such Contributor has been advised of
        the possibility of such damages.
        
        9. Accepting Warranty or Additional Liability. While redistributing the Work
        or Derivative Works thereof, You may choose to offer, and charge a fee for,
        acceptance of support, warranty, indemnity, or other liability obligations
        and/or rights consistent with this License. However, in accepting such
        obligations, You may act only on Your own behalf and on Your sole
        responsibility, not on behalf of any other Contributor, and only if You agree
        to indemnify, defend, and hold each Contributor harmless for any liability
        incurred by, or claims asserted against, such Contributor by reason of your
        accepting any such warranty or additional liability.
        
        END OF TERMS AND CONDITIONS
        
        APPENDIX: How to apply the Apache License to your work.
        
        To apply the Apache License to your work, attach the following boilerplate
        notice, with the fields enclosed by brackets "[]" replaced with your own
        identifying information. (Don't include the brackets!)  The text should be
        enclosed in the appropriate comment syntax for the file format. We also
        recommend that a file or class name and description of purpose be included on
        the same "printed page" as the copyright notice for easier identification
        within third-party archives.
        
        Copyright 2025 The World's Data Company
        
        Licensed under the Apache License, Version 2.0 (the "License");
        you may not use this file except in compliance with the License.
        You may obtain a copy of the License at
        
        	http://www.apache.org/licenses/LICENSE-2.0
        
        Unless required by applicable law or agreed to in writing, software
        distributed under the License is distributed on an "AS IS" BASIS,
        WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
        See the License for the specific language governing permissions and
        limitations under the License.
        
Project-URL: Homepage, https://worlddatafilter.com
Project-URL: Source, https://github.com/worlddataco/worlds-data-filter
Keywords: data,filter,information gain,subset selection,RAG,ETL,quality
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.24
Requires-Dist: scipy>=1.10
Requires-Dist: pydantic>=2.4
Requires-Dist: pydantic-settings>=2.0
Requires-Dist: typer>=0.12
Requires-Dist: orjson>=3.9
Provides-Extra: text
Requires-Dist: scikit-learn>=1.3; extra == "text"
Provides-Extra: api
Requires-Dist: fastapi>=0.110; extra == "api"
Requires-Dist: uvicorn[standard]>=0.23; extra == "api"
Provides-Extra: dev
Requires-Dist: ruff>=0.5; extra == "dev"
Requires-Dist: pytest>=7.4; extra == "dev"
Requires-Dist: pytest-cov>=4.1; extra == "dev"
Requires-Dist: pre-commit>=3.5; extra == "dev"
Requires-Dist: mypy>=1.8; extra == "dev"
Dynamic: license-file

# The World’s Data Filter™ — find the most valuable data, first.

**Surface your highest-value records with *information gain*, *novelty*, and *quality* scoring.**  
A universal SDK + CLI that ranks and subsets **text, JSONL, CSV, logs, and mixed corpora** so you see the signal first.  
Built on submodular selection (facility location), stable embeddings, diversity, and fast heuristics.

> Company: **The World’s Data Company** • Product: **The World’s Data Filter™**

---

## ✨ What it does

- **Universal features** — pluggable extractors for text, JSON/CSV/tabular, and generic blobs.
- **Information Gain** — greedy **facility‑location** selection to cover the dataset with minimal redundancy.
- **Novelty** — distances from dataset centroid / past cache to prioritize new signal.
- **Quality filters** — language/length heuristics for text; null/variance checks for tabular; duplicate/similarity suppression.
- **Explainable** — scores per item: `coverage_gain`, `novelty`, `quality`, and a `value_score` aggregate.
- **SDK & CLI** — embed in Python or run as `wdf` from the terminal.
- **Deterministic** — stable SHA‑256–based embeddings by default (swap for your own encoder at any time).
- **No heavy models** — NumPy/Scipy core; scikit‑learn is optional (`[text]` extra) for TF‑IDF.

> Year 2 roadmap: *The World’s Data Index* (persistent vector/metadata store) — this repo stays the stateless filter/selector.

---

## 🚀 Quickstart (Windows / macOS / Linux)

```bash
# 1) Create a virtualenv (Python 3.10+)
python -m venv .venv
# Windows
.\.venv\Scripts\Activate.ps1
# macOS/Linux
# source .venv/bin/activate

# 2) Install
pip install -U pip
pip install -e .[dev]            # add [text] for TF-IDF utilities if you like

# 3) Run the demo
wdf score examples/news.jsonl --text-field text --out scores.csv
wdf filter examples/news.jsonl --text-field text --k 10 --out selected.jsonl --explain
```

Outputs:
- `scores.csv` — per‑item `coverage_gain, novelty, quality, value_score`
- `selected.jsonl` — the top‑K items by the chosen criterion (default: `value_score`) with explanations included by default (disable via `--no-explain`)

---

## 🧠 How it works (high level)

### Feature extraction (adapters)
- **Text** → deterministic hash embedding (384‑d) or optional TF‑IDF.
- **JSONL/CSV** → flattened key/value signals, basic stats (NA ratio, variance), and hash embedding of important string fields.
- **Generic files** → filename, size, MIME guess, byte histograms (lightweight), hash embedding of content bytes.

Each item yields a vector `x_i` (unit‑normalized) and auxiliary quality features.

### Scoring
- **Facility Location (coverage)**  
  \(F(S)=\sum_j \max_{i\in S} \text{sim}(x_i, x_j)\) — select items that best cover the rest.  
  Greedy selection approximates the optimum and doubles as a *redundancy filter*.
- **Novelty**  
  Distance from dataset centroid (or *past cache*) highlights unusual / new items.
- **Quality**  
  Text heuristics (language guess, length, printable ratio), tabular health (missing‑ness, low variance), duplicate checks.

### Value score (combined)
`value_score = w_cov * coverage_gain + w_nov * novelty + w_quality * quality`  
Weights configurable in CLI/SDK.

---

## 🧰 CLI usage

```bash
# Score a JSONL corpus (one object per line) with a 'text' field
wdf score examples/news.jsonl --text-field text --out scores.csv

# Filter top-K by value score (explain is on by default)
wdf select examples/news.jsonl --text-field text --k 50 --out selected.jsonl

# Prefer compact JSONL (disable explanations)
wdf select examples/news.jsonl --text-field text --k 50 --out selected.jsonl --no-explain

# From a CSV (choose a text column)
wdf score examples/sample.csv --csv --text-field body --id-field id --out scores.csv

# Tune weights + disable novelty
wdf filter examples/news.jsonl --text-field text --k 20 --w-cov 0.8 --w-nov 0.0 --w-qual 0.2 --out selected.jsonl
```

**Input types supported today**
- `.jsonl` (id, text, and/or arbitrary fields)  
- `.csv` (choose columns)  
- Directory of `.txt` files (`--dir`)  
- Anything else you can adapt via a custom extractor (see `worlddatafilter/extractors/base.py`).

> You can register your own extractor in ~20 lines — the SDK passes through `meta` and `text` to downstream systems.

---

## 📦 Python SDK

```python
from worlddatafilter import WorldDataFilter, loaders

docs = loaders.load_jsonl("examples/news.jsonl", text_field="text")
wdf = WorldDataFilter()
scores = wdf.score(docs)      # list of ItemScore
selected = wdf.select(docs, k=25, weights=dict(cov=0.7, nov=0.2, qual=0.1))
```

---

## 🧪 Tests & Quality

```bash
ruff check .
pytest -q
```

---

## 🔌 Optional extras

- `pip install -e .[text]` → scikit‑learn TF‑IDF utilities.
- `pip install -e .[api]`  → simple FastAPI server exposing `/score` & `/filter` (coming soon).

---

## 📄 License

Apache License 2.0 © The World’s Data Company
