Implement CLI and Hypothesis tests

CITATION.cff

@@ -0,0 +1,18 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it using the metadata below."
+
+# Basic information
+preferred-citation:
+  type: software
+  title: "gen_surv"
+  version: "1.0.0"
+  url: "https://github.com/DiogoRibeiro7/genSurvPy"
+  authors:
+    - family-names: Ribeiro
+      given-names: Diogo
+      orcid: "https://orcid.org/0009-0001-2022-7072"
+      affiliation: "ESMAD - Instituto Politécnico do Porto"
+      email: "[email protected]"
+  license: "MIT"
+  date-released: "2024-01-01"
+

README.md

@@ -28,6 +28,7 @@ poetry install
 - Easy integration with `pandas` and `NumPy`
 - Suitable for benchmarking survival algorithms and teaching
 - Accelerated Failure Time (Log-Normal) model generator
+- Command-line interface powered by `Typer`
 
 ## 🧪 Example
 
@@ -55,6 +56,15 @@ generate(model="thmm", n=100, qmat=[[0, 0.2, 0], [0.1, 0, 0.1], [0, 0.3, 0]],
          p0=[1.0, 0.0, 0.0], model_cens="exponential", cens_par=3.0)
 ```
 
+## ⌨️ Command-Line Usage
+
+Install the package and use ``python -m gen_surv`` to generate datasets without
+writing Python code:
+
+```bash
+python -m gen_surv dataset aft_ln --n 100 > data.csv
+```
+
 ## 🔧 Available Generators
 
 | Function     | Description                                |
@@ -115,3 +125,9 @@ expectations for participants in this project.
 ## 🤝 Contributing
 
 Please read [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on setting up your environment, running tests, and submitting pull requests.
+
+## 📑 Citation
+
+If you use **gen_surv** in your work, please cite it using the metadata in
+[`CITATION.cff`](CITATION.cff). Many reference managers can import this file
+directly.

TODO.md

@@ -4,11 +4,22 @@ This document outlines future enhancements, features, and ideas for improving th
 
 ---
 
+## ✨ Priority Items
+
+- [✅] Add property-based tests using Hypothesis to cover edge cases
+- [✅] Build a CLI for generating datasets from the terminal
+- [ ] Expand documentation with multilingual support and more usage examples
+- [ ] Implement Weibull and log-logistic AFT models and add visualization utilities
+- [✅] Provide CITATION metadata for proper referencing
+- [ ] Ensure all functions include Google-style docstrings with inline comments
+
+---
+
 ## 📦 1. Interface and UX
 
 - [✅] Create a `generate(..., return_type="df" | "dict")` interface
-- [ ] Add `__version__` using `importlib.metadata` or `poetry-dynamic-versioning`
-- [ ] Build a CLI with `typer` or `click`
+- [✅] Add `__version__` using `importlib.metadata` or `poetry-dynamic-versioning`
+- [✅] Build a CLI with `typer` or `click`
 - [✅] Add example notebooks or scripts for each model (`examples/` folder)
 
 ---
@@ -25,7 +36,7 @@ This document outlines future enhancements, features, and ideas for improving th
 ## 🧪 3. Testing and Quality
 
 - [✅] Add tests for each model (e.g., `test_tdcm.py`, `test_thmm.py`, `test_aft.py`)
-- [ ] Add property-based tests with `hypothesis`
+- [✅] Add property-based tests with `hypothesis`
 - [ ] Cover edge cases (e.g., invalid parameters, n=0, negative censoring)
 - [ ] Run tests on multiple Python versions (CI matrix)
 

docs/source/index.md

@@ -49,6 +49,14 @@ generate(model="thmm", n=100, qmat=[[0, 0.2, 0], [0.1, 0, 0.1], [0, 0.3, 0]],
          p0=[1.0, 0.0, 0.0], model_cens="exponential", cens_par=3.0)
 ```
 
+## ⌨️ Command-Line Usage
+
+Generate datasets directly from the terminal:
+
+```bash
+python -m gen_surv dataset aft_ln --n 100 > data.csv
+```
+
 ## 🔗 Project Links
 
 - [Source Code](https://github.com/DiogoRibeiro7/genSurvPy)

gen_surv/__init__.py

@@ -1 +1,17 @@
-from .interface import generate
+"""Top-level package for ``gen_surv``.
+
+This module exposes the :func:`generate` function and provides access to the
+package version via ``__version__``.
+"""
+
+from importlib.metadata import PackageNotFoundError, version
+
+from .interface import generate
+
+try:
+    __version__ = version("gen_surv")
+except PackageNotFoundError:  # pragma: no cover - fallback when package not installed
+    __version__ = "0.0.0"
+
+__all__ = ["generate", "__version__"]
+

gen_surv/__main__.py

@@ -1,30 +1,4 @@
-import argparse
-import pandas as pd
-from gen_surv.cphm import gen_cphm
-from gen_surv.cmm import gen_cmm
-from gen_surv.tdcm import gen_tdcm
-from gen_surv.thmm import gen_thmm
-
-def run_example(model: str):
-    if model == "cphm":
-        df = gen_cphm(n=10, model_cens="uniform", cens_par=1.0, beta=0.5, covar=2.0)
-    elif model == "cmm":
-        df = gen_cmm(n=10, model_cens="exponential", cens_par=1.0,
-                     beta=[0.5, 0.2, -0.1], covar=2.0, rate=[0.1, 1.0, 0.2, 1.0, 0.3, 1.0])
-    elif model == "tdcm":
-        df = gen_tdcm(n=10, dist="weibull", corr=0.5, dist_par=[1, 2, 1, 2],
-                      model_cens="uniform", cens_par=0.5, beta=[0.1, 0.2, 0.3], lam=1.0)
-    elif model == "thmm":
-        df = gen_thmm(n=10, model_cens="uniform", cens_par=0.5,
-                      beta=[0.1, 0.2, 0.3], covar=1.0, rate=[0.5, 0.6, 0.7])
-    else:
-        raise ValueError(f"Unknown model: {model}")
-
-    print(df)
+from gen_surv.cli import app
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Run gen_surv model example.")
-    parser.add_argument("model", choices=["cphm", "cmm", "tdcm", "thmm"],
-                        help="Model to run (cphm, cmm, tdcm, thmm)")
-    args = parser.parse_args()
-    run_example(args.model)
+    app()

gen_surv/cli.py

@@ -0,0 +1,36 @@
+import csv
+from typing import Optional
+import typer
+from gen_surv.interface import generate
+
+app = typer.Typer(help="Generate synthetic survival datasets.")
+
+@app.command()
+def dataset(
+    model: str = typer.Argument(
+        ..., help="Model to simulate [cphm, cmm, tdcm, thmm, aft_ln]"
+    ),
+    n: int = typer.Option(100, help="Number of samples"),
+    output: Optional[str] = typer.Option(
+        None, "-o", help="Output CSV file. Prints to stdout if omitted."
+    ),
+) -> None:
+    """Generate survival data and optionally save to CSV.
+
+    Args:
+        model: Identifier of the generator to use.
+        n: Number of samples to create.
+        output: Optional path to save the CSV file.
+
+    Returns:
+        None
+    """
+    df = generate(model=model, n=n)
+    if output:
+        df.to_csv(output, index=False)
+        typer.echo(f"Saved dataset to {output}")
+    else:
+        typer.echo(df.to_csv(index=False))
+
+if __name__ == "__main__":
+    app()

gen_surv/interface.py

@@ -6,6 +6,9 @@
     >>> df = generate(model="cphm", n=100, model_cens="uniform", cens_par=1.0, beta=0.5, covar=2.0)
 """
 
+from typing import Any
+import pandas as pd
+
 from gen_surv.cphm import gen_cphm
 from gen_surv.cmm import gen_cmm
 from gen_surv.tdcm import gen_tdcm
@@ -22,13 +25,13 @@
 }
 
 
-def generate(model: str, **kwargs):
-    """
-    Generic interface to generate survival data from various models.
+def generate(model: str, **kwargs: Any) -> pd.DataFrame:
+    """Generate survival data from a specific model.
 
-    Parameters:
-        model (str): One of ["cphm", "cmm", "tdcm", "thmm"]
-        **kwargs: Arguments forwarded to the selected model generator.
+    Args:
+        model: Name of the generator to run. Must be one of ``cphm``, ``cmm``,
+            ``tdcm``, ``thmm`` or ``aft_ln``.
+        **kwargs: Arguments forwarded to the chosen generator.
 
     Returns:
         pd.DataFrame: Simulated survival data.

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "gen_surv"
-version = "1.0.1"
+version = "1.0.0"
 description = "A Python package for simulating survival data, inspired by the R package genSurv"
 authors = ["Diogo Ribeiro <[email protected]>"]
 license = "MIT"
@@ -16,12 +16,14 @@ numpy = "^1.26"
 pandas = "^2.2.3"
 pytest-cov = "^6.1.1"
 invoke = "^2.2.0"
+typer = "^0.12.3"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.3.5"
 python-semantic-release = "^9.21.0"
 mypy = "^1.15.0"
 invoke = "^2.2.0"
+hypothesis = "^6.98"
 
 
 [tool.poetry.group.docs.dependencies]

tasks.py

@@ -5,6 +5,7 @@
 
 
 
+
 @task
 def test(c: Context) -> None:
     """
@@ -49,6 +50,33 @@ def test(c: Context) -> None:
             print(stderr_output)
 
 
+@task
+def check_version(c: Context) -> None:
+    """Validate that ``pyproject.toml`` matches the latest git tag.
+
+    This task runs the ``scripts/check_version_match.py`` helper using Poetry
+    and reports whether the version numbers are aligned.
+
+    Args:
+        c: Invoke context used to run shell commands.
+
+    Returns:
+        None
+    """
+    if not isinstance(c, Context):
+        raise TypeError(f"Expected Invoke Context, got {type(c).__name__!r}")
+
+    # Execute the version check script with Poetry.
+    cmd = "poetry run python scripts/check_version_match.py"
+    result = c.run(cmd, warn=True, pty=False)
+
+    # Report based on the exit code from the script.
+    if result.ok:
+        print("✔️  pyproject version matches the latest git tag.")
+    else:
+        print("❌  Version mismatch detected.")
+        print(result.stderr)
+
 @task
 def docs(c: Context) -> None:
     """

tests/test_aft_property.py

@@ -0,0 +1,22 @@
+from hypothesis import given, strategies as st
+from gen_surv.aft import gen_aft_log_normal
+
+@given(
+    n=st.integers(min_value=1, max_value=20),
+    sigma=st.floats(min_value=0.1, max_value=2.0, allow_nan=False, allow_infinity=False),
+    cens_par=st.floats(min_value=0.1, max_value=10.0, allow_nan=False, allow_infinity=False),
+    seed=st.integers(min_value=0, max_value=1000)
+)
+def test_gen_aft_log_normal_properties(n, sigma, cens_par, seed):
+    df = gen_aft_log_normal(
+        n=n,
+        beta=[0.5, -0.2],
+        sigma=sigma,
+        model_cens="uniform",
+        cens_par=cens_par,
+        seed=seed
+    )
+    assert df.shape[0] == n
+    assert set(df["status"].unique()).issubset({0, 1})
+    assert (df["time"] >= 0).all()
+    assert df.filter(regex="^X[0-9]+$").shape[1] == 2

tests/test_bivariate.py

@@ -0,0 +1,26 @@
+import numpy as np
+from gen_surv.bivariate import sample_bivariate_distribution
+import pytest
+
+
+def test_sample_bivariate_exponential_shape():
+    """Exponential distribution should return an array of shape (n, 2)."""
+    result = sample_bivariate_distribution(5, "exponential", 0.0, [1.0, 1.0])
+    assert isinstance(result, np.ndarray)
+    assert result.shape == (5, 2)
+
+
+def test_sample_bivariate_invalid_dist():
+    """Unsupported distributions should raise ValueError."""
+    with pytest.raises(ValueError):
+        sample_bivariate_distribution(10, "invalid", 0.0, [1, 1])
+
+def test_sample_bivariate_exponential_param_length_error():
+    """Exponential distribution with wrong param length should raise ValueError."""
+    with pytest.raises(ValueError):
+        sample_bivariate_distribution(5, "exponential", 0.0, [1.0])
+
+def test_sample_bivariate_weibull_param_length_error():
+    """Weibull distribution with wrong param length should raise ValueError."""
+    with pytest.raises(ValueError):
+        sample_bivariate_distribution(5, "weibull", 0.0, [1.0, 1.0])

tests/test_cli.py

@@ -0,0 +1,45 @@
+import pandas as pd
+from gen_surv.cli import dataset
+import runpy
+
+
+def test_cli_dataset_stdout(monkeypatch, capsys):
+    """Dataset command prints CSV to stdout when no output file is given."""
+
+    def fake_generate(model: str, n: int):
+        return pd.DataFrame({"time": [1.0], "status": [1], "X0": [0.1], "X1": [0.2]})
+
+    # Patch the generate function used in the CLI to avoid heavy computation.
+    monkeypatch.setattr("gen_surv.cli.generate", fake_generate)
+    # Call the command function directly to sidestep Click argument parsing
+    dataset(model="cphm", n=1, output=None)
+    captured = capsys.readouterr()
+    assert "time,status,X0,X1" in captured.out
+
+
+def test_main_entry_point(monkeypatch):
+    """Running the module as a script should invoke the CLI app."""
+
+    called = []
+
+    def fake_app():
+        called.append(True)
+
+    # Patch the CLI app before the module is executed
+    monkeypatch.setattr("gen_surv.cli.app", fake_app)
+    monkeypatch.setattr("sys.argv", ["gen_surv", "dataset", "cphm"])
+    runpy.run_module("gen_surv.__main__", run_name="__main__")
+    assert called
+
+def test_cli_dataset_file_output(monkeypatch, tmp_path):
+    """Dataset command writes CSV to file when output path is provided."""
+
+    def fake_generate(model: str, n: int):
+        return pd.DataFrame({"time": [1.0], "status": [1], "X0": [0.1], "X1": [0.2]})
+
+    monkeypatch.setattr("gen_surv.cli.generate", fake_generate)
+    out_file = tmp_path / "out.csv"
+    dataset(model="cphm", n=1, output=str(out_file))
+    assert out_file.exists()
+    content = out_file.read_text()
+    assert "time,status,X0,X1" in content