diff --git a/CITATION.cff b/CITATION.cff index 28316b0..70f9ada 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -8,7 +8,7 @@ type: software authors: - alias: nsboan email: nasserboan@gmail.com -repository-code: https://github.com/nasserboan/proj-clv +repository-code: https://github.com/nasserboan/clvkit abstract: >- Buy-till-you-die models for customer-base analysis in Python, fit by maximum likelihood and benchmarked against the estimates published with the original diff --git a/README.md b/README.md index 67ba76b..fde415a 100644 --- a/README.md +++ b/README.md @@ -15,18 +15,18 @@

- Python 3.13+ + Python 3.11+ Four dependencies - 238 tests passing + 254 tests passing Code style: ruff - Stars + Stars PyPI version - License + License

- r = 0.242595 against a published 0.243 · 4 dependencies · one scipy.optimize run · a DataFrame back
- BG/NBD fit on the CDNOW 1/10 systematic sample, 2,357 customers, 39-week calibration, which is the same data Fader, Hardie & Lee published r = .243, α = 4.414, a = .793, b = 2.426 on in 2005. The right-hand numbers are not transcribed: the test suite fits them on every push, and the build fails if any drifts past half a unit in the paper's last printed digit. The full table · six lines to run it. + A transaction log in, a per-customer CLV table back · six lines · 2.0 s wall clock · 14 packages
+ BG/NBD, Gamma-Gamma and discounted lifetime value on the CDNOW 1/10 systematic sample, 2,357 customers, imports included. The fitted parameters land on the estimates Fader, Hardie & Lee published on the same data in 2005, and they aren't transcribed: the test suite fits them on every push, and the build fails if any drifts past half a unit in the paper's last printed digit. The six lines · the reproduced estimates.

--- @@ -45,6 +45,37 @@ answer any of them, because they need statistical inference rather than a The BTYD / probability-model tradition (Fader, Hardie, et al.) answered these decades ago. The Python tooling for it is what went missing. +## The whole thing + +```python +import pandas as pd + +from clvkit import CLV, CustomerBase + +log = pd.read_csv("transactions.csv", parse_dates=["date"]) # customer_id, date, amount + +cb = CustomerBase.from_transactions(log, time_unit="W", collapse="D") +result = CLV().fit(cb).predict(horizon=52, discount_rate=0.001) + +# expected_purchases, discounted_expected_transactions, expected_spend, clv +result.to_pandas() # indexed by customer_id +result.plot() +``` + +That is the whole pipeline. On the CDNOW sample, 2,357 customers, it runs in +2.0 s of wall clock, imports included. + +`CustomerBase` is the one input currency: it turns the raw log into the RFM +sufficient statistic every model here consumes, and carries its own provenance +(`time_unit`, `collapse`, `observation_period_end`, `has_monetary`, +`on_negative`), so scoring a model fit in weeks against a base measured in +days is refused rather than quietly answered, and so is scoring it against a +base that kept a different set of purchases. The verbs are short and shared: +`fit` and `predict` everywhere, plus `probability_alive` on the transaction +models, where latent +attrition is the thing being estimated. Every result object plots itself and +hands you a DataFrame with `.to_pandas()`. + ## Why this exists Two libraries occupy this space, and each is out of reach in its own way. @@ -82,13 +113,11 @@ stays correct — CI checks both on every push. ## Install -Not on PyPI yet. Until it is: - ```console -uv add git+https://github.com/nasserboan/proj-clv +uv add clvkit ``` -Python 3.13+. Four runtime dependencies (numpy, scipy, pandas, matplotlib), and +Or `pip install clvkit`. Python 3.11+. Four runtime dependencies (numpy, scipy, pandas, matplotlib), and one optional extra, `dask`, for logs too big to summarise in pandas. See [Large transaction logs](#large-transaction-logs). @@ -105,33 +134,35 @@ one optional extra, `dask`, for logs too big to summarise in pandas. See Both are the library's own output on the CDNOW 1/10 sample. -## The whole thing +## Four questions, and the sentence to say out loud -```python -import pandas as pd +[`examples/start_here.ipynb`](examples/start_here.ipynb) is a router, not a +lesson. Four questions a business actually asks (what is a customer worth, is +this one gone or just quiet, who gets the retention budget, why do two +retention charts disagree), each answered on real data in a couple of cells, +each ending with the sentence you could say in a meeting. -from clvkit import CLV, CustomerBase +The first one runs the formula you probably use today, `average ticket × +frequency × margin`, against 39 weeks of CDNOW history neither method saw. The +formula overshoots by 61%. The fitted model comes in 16% low, wrong by a +quarter as much and in the safe direction, and the notebook hands you the +sentence that follows: "our current LTV number assumes nobody ever churns." -log = pd.read_csv("transactions.csv", parse_dates=["date"]) # customer_id, date, amount +The library talks the same way. `print(cb)` on the CDNOW base says +`52% bought once - the models see them only through the population, not their +own history`. The variant for a base like that, where a customer can drop out +after their first purchase, is [`MBGNBD`](#whats-in-the-box), one word away. -cb = CustomerBase.from_transactions(log, time_unit="W", collapse="D") -result = CLV().fit(cb).predict(horizon=52, discount_rate=0.001) +## Small enough to leave -# expected_purchases, discounted_expected_transactions, expected_spend, clv -result.to_pandas() # indexed by customer_id -result.plot() -``` +`uv add clvkit` puts 14 packages in a fresh environment, none of them a +compiler. The library itself is about 2,600 lines of BSD-3 Python on numpy, +scipy, pandas and matplotlib. -`CustomerBase` is the one input currency: it turns the raw log into the RFM -sufficient statistic every model here consumes, and carries its own provenance -(`time_unit`, `collapse`, `observation_period_end`, `has_monetary`, -`on_negative`), so scoring a model fit in weeks against a base measured in -days is refused rather than quietly answered, and so is scoring it against a -base that kept a different set of purchases. The verbs are short and shared: -`fit` and `predict` everywhere, plus `probability_alive` on the transaction -models, where latent -attrition is the thing being estimated. Every result object plots itself and -hands you a DataFrame with `.to_pandas()`. +That count is the exit plan. If I disappear, a team that depends on this can +vendor the lines into their own repo and maintain them. A promise to maintain +a library is worth what promises are worth; a codebase small enough to take +over is worth more. ## Reproduced estimates @@ -396,11 +427,8 @@ abandoned. This one is finished, not stalled — stopping here is the design. ## Example notebooks -Start with [`examples/start_here.ipynb`](examples/start_here.ipynb). It's a router, -not a lesson: four questions a business actually asks, each answered on real data -in a couple of cells, each ending with the sentence you could say in a meeting. -The first one runs your own `average ticket x frequency x margin` formula against -39 weeks of held-out CDNOW history and shows it overshooting by 61%. +Start with [`examples/start_here.ipynb`](examples/start_here.ipynb), the router +described [above](#four-questions-and-the-sentence-to-say-out-loud). The other two are the long versions. All three are executed top-to-bottom by CI with [`nbmake`](https://github.com/treebeardtech/nbmake), so a change that breaks diff --git a/docs/site/examples/from_lifetimes.rst b/docs/site/examples/from_lifetimes.rst index e5d1399..2b0c745 100644 --- a/docs/site/examples/from_lifetimes.rst +++ b/docs/site/examples/from_lifetimes.rst @@ -4,7 +4,13 @@ From lifetimes If you have working ``lifetimes`` code, the same fit is shorter here, and the two knobs that quietly change the answer are named instead of hidden. ``lifetimes`` is archived and takes no new releases; clvkit fits the same four models on Python -3.13. +3.11+. + +Archived means nobody fixes it when the ground moves, and it already has. +setuptools 83 removed ``pkg_resources``, ``lifetimes.datasets`` stopped importing +with it, and the ``load_cdnow_summary()`` line that opens every old tutorial now +dies on a fresh install. The models themselves still fit; the on-ramp is what +broke. The whole migration, side by side ---------------------------------- diff --git a/docs/site/index.rst b/docs/site/index.rst index 48dfa1e..94c5d8c 100644 --- a/docs/site/index.rst +++ b/docs/site/index.rst @@ -9,7 +9,8 @@ clvkit Buy-till-you-die models for a business where nobody cancels a subscription, so you never observe a customer leaving — you only ever observe them not coming back -*yet*. Python 3.13+, four dependencies, no compiler. +*yet*. Six lines take a transaction log to a per-customer CLV table, in 2.0 s +of wall clock on the CDNOW sample. Python 3.11+, four dependencies, no compiler. .. code-block:: bash diff --git a/docs/site/install/index.rst b/docs/site/install/index.rst index 0cdc1a3..89cae8f 100644 --- a/docs/site/install/index.rst +++ b/docs/site/install/index.rst @@ -5,7 +5,7 @@ Install uv add clvkit # or pip install clvkit -Python 3.13+, four runtime dependencies — ``numpy``, ``scipy``, ``pandas`` and +Python 3.11+, four runtime dependencies — ``numpy``, ``scipy``, ``pandas`` and ``matplotlib`` — and no compiler. If ``uv`` is new to you, it's the fastest way in; ``pip install clvkit`` gets you the same package. diff --git a/docs/site/user_guide/getting_started.rst b/docs/site/user_guide/getting_started.rst index 41b8266..4be6732 100644 --- a/docs/site/user_guide/getting_started.rst +++ b/docs/site/user_guide/getting_started.rst @@ -11,7 +11,7 @@ Install uv add clvkit # or pip install clvkit -Python 3.13+, four runtime dependencies. If ``uv`` is new to you, it's +Python 3.11+, four runtime dependencies. If ``uv`` is new to you, it's the fastest way in; ``pip install clvkit`` gets you the same package. The one contract diff --git a/examples/cdnow_clv.ipynb b/examples/cdnow_clv.ipynb index aa94af3..8df77ab 100644 --- a/examples/cdnow_clv.ipynb +++ b/examples/cdnow_clv.ipynb @@ -1973,7 +1973,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "wrote /Users/nasserboan/projects/personal/proj-clv/examples/output/cdnow_clv.csv (192,544 bytes)\n" + "wrote examples/output/cdnow_clv.csv (192,544 bytes)\n" ] }, { @@ -2076,7 +2076,7 @@ "source": [ "csv_path = OUTPUT / \"cdnow_clv.csv\"\n", "result.to_pandas().to_csv(csv_path)\n", - "print(f\"wrote {csv_path} ({csv_path.stat().st_size:,} bytes)\")\n", + "print(f\"wrote {csv_path.relative_to(REPO)} ({csv_path.stat().st_size:,} bytes)\")\n", "\n", "result.to_pandas().nlargest(5, \"clv\").round(2)" ] diff --git a/examples/online_retail_ii_cohort.ipynb b/examples/online_retail_ii_cohort.ipynb index 4d545f9..6273354 100644 --- a/examples/online_retail_ii_cohort.ipynb +++ b/examples/online_retail_ii_cohort.ipynb @@ -417,7 +417,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "/Users/nasserboan/projects/personal/proj-clv/examples/data/online_retail_II.xlsx (46 MB)\n" + "examples/data/online_retail_II.xlsx (46 MB)\n" ] } ], @@ -448,7 +448,7 @@ " with zipfile.ZipFile(archive) as zf:\n", " zf.extract(\"online_retail_II.xlsx\", DATA)\n", "\n", - "print(f\"{workbook} ({workbook.stat().st_size / 1e6:.0f} MB)\")" + "print(f\"{workbook.relative_to(REPO)} ({workbook.stat().st_size / 1e6:.0f} MB)\")" ] }, { diff --git a/pyproject.toml b/pyproject.toml index 0efad2d..2d36c11 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ readme = "README.md" authors = [ { name = "nsboan", email = "nasserboan@gmail.com" } ] -requires-python = ">=3.13" +requires-python = ">=3.11" # BSD-3-Clause, matching numpy, scipy and pandas. Permissive, so a company can # embed this without a legal review, and the no-endorsement clause keeps the # project's name out of anyone else's marketing. @@ -14,9 +14,9 @@ license = "BSD-3-Clause" license-files = ["LICENSE"] dependencies = [ "matplotlib>=3.11.0", - "numpy>=2.5.1", + "numpy>=2.3", "pandas>=3.0.3", - "scipy>=1.18.0", + "scipy>=1.16", ] [project.optional-dependencies] @@ -70,3 +70,10 @@ docs = [ "sphinx-copybutton>=0.5.2", "sphinx-design>=0.7.0", ] + +# The runtime floor is 3.11 (validated: the suite passes on 3.11 at both the +# newest compatible set and the exact pinned minimums). Docs tooling is +# maintainer-only and sphinx>=9.1 needs 3.12, so the docs group keeps a +# higher floor rather than dragging the package's down. +[tool.uv.dependency-groups] +docs = { requires-python = ">=3.12" } diff --git a/src/clvkit/__init__.py b/src/clvkit/__init__.py index 60486a3..58e2e59 100644 --- a/src/clvkit/__init__.py +++ b/src/clvkit/__init__.py @@ -1,9 +1,13 @@ """clvkit — CLV engine and cohort-retention over a shared transaction-log contract.""" +from importlib.metadata import version as _version + from clvkit.clv import BGNBD, CLV, MBGNBD, GammaGamma from clvkit.cohort import CohortMatrix, CohortSurvival from clvkit.customer_base import CustomerBase +__version__ = _version("clvkit") + __all__ = [ "BGNBD", "CLV", diff --git a/uv.lock b/uv.lock index 8ea8304..34fd457 100644 --- a/uv.lock +++ b/uv.lock @@ -1,17 +1,16 @@ version = 1 revision = 3 -requires-python = ">=3.13" +requires-python = ">=3.11" resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform == 'darwin'", - "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "(python_full_version >= '3.14' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')", - "python_full_version < '3.14' and sys_platform == 'win32'", - "python_full_version < '3.14' and sys_platform == 'emscripten'", - "python_full_version < '3.14' and sys_platform == 'darwin'", - "python_full_version < '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "(python_full_version < '3.14' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.12' and sys_platform == 'win32'", + "python_full_version < '3.12' and sys_platform == 'emscripten'", + "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'", ] [[package]] @@ -19,7 +18,7 @@ name = "accessible-pygments" version = "0.0.5" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "pygments" }, + { name = "pygments", marker = "python_full_version >= '3.12'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/bc/c1/bbac6a50d02774f91572938964c582fff4270eee73ab822a4aeea4d8b11b/accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872", size = 1377899, upload-time = "2024-05-10T11:23:10.216Z" } wheels = [ @@ -76,8 +75,8 @@ name = "beautifulsoup4" version = "4.15.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "soupsieve" }, - { name = "typing-extensions" }, + { name = "soupsieve", marker = "python_full_version >= '3.12'" }, + { name = "typing-extensions", marker = "python_full_version >= '3.12'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/43/65/318323f98dbee45d42dff61d8f047181bc6f2268a9068cfad035a46be5af/beautifulsoup4-4.15.0.tar.gz", hash = "sha256:288e3ca7d54b06f2ac191970bc275c1939cb46d450b255bf6718b04aa37ab4f7", size = 632571, upload-time = "2026-06-07T16:44:20.453Z" } wheels = [ @@ -89,7 +88,7 @@ name = "bleach" version = "6.4.0" source = { 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