Fix post-0.4.0 release issues

CHANGELOG.md

@@ -2,6 +2,21 @@
 
 ## Unreleased
 
+### Fixed
+
+- `JitterSimulator.transform` before `fit` now raises instead of returning the
+  data unchanged, which would have scored as perfect jitter stability. This
+  affects the Rust `JitterSimulator` and `pypasteur.JitterSimulator`.
+- The `pasteur-model` README example passes `Some(1)` for the positive class
+  index, matching the 0.4.0 `Option<usize>` signature.
+- Positive-class-index errors from `OnnxModel::from_file` name the parameter
+  as well as the CLI flag.
+
+### Added
+
+- `pasteur-cli --version`.
+- `pypasteur.__version__`.
+
 ## 0.4.0
 
 ### Added

bindings/python-bindings/src/lib.rs

@@ -14,5 +14,13 @@ pub fn register_module(m: &Bound<'_, PyModule>) -> PyResult<()> {
 
 #[pymodule]
 fn pypasteur(_py: Python, m: &Bound<'_, PyModule>) -> PyResult<()> {
-    register_module(m)
+    register_module(m)?;
+    m.add("__version__", env!("CARGO_PKG_VERSION"))?;
+    // maturin's generated `__init__.py` re-exports with `import *`, which
+    // skips dunder names unless they are listed in `__all__`.
+    m.add(
+        "__all__",
+        ["BlackoutSimulator", "JitterSimulator", "__version__"],
+    )?;
+    Ok(())
 }

bindings/python-bindings/tests/smoke.py

@@ -1,9 +1,13 @@
 """Smoke test for an installed pypasteur wheel. Run: python tests/smoke.py"""
 
+import importlib.metadata
+
 import polars as pl
 
 import pypasteur
 
+assert pypasteur.__version__ == importlib.metadata.version("pypasteur"), pypasteur.__version__
+
 N = 20
 df = pl.DataFrame({"tsh": [float(i) for i in range(N)], "age": [40.0 + i for i in range(N)]})
 
@@ -22,4 +26,12 @@
 assert not out["tsh"].equals(df["tsh"]), "jitter left tsh unchanged"
 assert out["age"].equals(df["age"]), "jitter touched a column it shouldn't"
 
+unfitted = pypasteur.JitterSimulator("tsh", scale=1.0, random_state=42)
+try:
+    unfitted.transform(df)
+except ValueError as e:
+    assert "fit before transform" in str(e), e
+else:
+    raise AssertionError("transform before fit should raise")
+
 print("pypasteur smoke test OK")

cli/src/args.rs

@@ -6,6 +6,7 @@ use pasteur_core::TaskType;
 #[derive(Parser)]
 #[command(
     name = "pasteur-cli",
+    version,
     about = "Local Pasteur simulation, evaluation, and dataset-card generation (no Hub I/O)"
 )]
 pub struct Cli {

cli/tests/cli_local.rs

@@ -897,3 +897,17 @@ fn regression_targets_must_cover_the_cohort() {
     );
     let _ = fs::remove_dir_all(&root);
 }
+
+#[test]
+fn version_flag_prints_crate_version() {
+    let out = Command::new(pasteur_cli())
+        .arg("--version")
+        .output()
+        .unwrap();
+    assert!(out.status.success());
+    let stdout = String::from_utf8_lossy(&out.stdout);
+    assert_eq!(
+        stdout.trim(),
+        format!("pasteur-cli {}", env!("CARGO_PKG_VERSION"))
+    );
+}

core/src/simulate/jitter.rs

@@ -7,28 +7,36 @@ use super::Simulator;
 
 pub struct JitterSimulator {
     config: JitterConfig,
-    feature_std: f64,
+    /// Set by `fit`. `None` means unfitted, so `transform` refuses to run
+    /// instead of applying zero noise and reporting perfect stability.
+    feature_std: Option<f64>,
 }
 
 impl JitterSimulator {
     pub fn new(config: JitterConfig) -> Self {
         Self {
             config,
-            feature_std: 0.0,
+            feature_std: None,
         }
     }
 }
 
 impl Simulator for JitterSimulator {
     fn fit(&mut self, df: &DataFrame) -> Result<(), CoreError> {
         let series = df.column(&self.config.feature)?;
-        self.feature_std = series.as_materialized_series().std(1).unwrap_or(0.0);
+        self.feature_std = Some(series.as_materialized_series().std(1).unwrap_or(0.0));
         Ok(())
     }
 
     fn transform(&self, df: &DataFrame) -> Result<DataFrame, CoreError> {
+        let Some(feature_std) = self.feature_std else {
+            return Err(CoreError::InvalidConfig(format!(
+                "JitterSimulator for `{}` must be fit before transform",
+                self.config.feature
+            )));
+        };
         let n_rows = df.height();
-        let scale = self.config.scale * self.feature_std;
+        let scale = self.config.scale * feature_std;
 
         // Deterministic per-row SHA256-seeded draw (see `stable_normal_sample`
         // in rng.rs) instead of a single shared RNG stream — matches jitter.py's
@@ -87,4 +95,19 @@ mod tests {
             assert_ne!(a.get(i), orig.get(i));
         }
     }
+
+    #[test]
+    fn jitter_transform_before_fit_is_an_error() {
+        let df = df!["val" => [1.0, 2.0, 3.0]].unwrap();
+        let sim = JitterSimulator::new(JitterConfig {
+            feature: "val".to_string(),
+            scale: 1.0,
+            random_state: Some(0),
+        });
+        let err = sim.transform(&df).expect_err("unfitted jitter must fail");
+        assert!(
+            err.to_string().contains("must be fit before transform"),
+            "{err}"
+        );
+    }
 }

model/README.md

@@ -9,7 +9,7 @@ let model = OnnxModel::from_file(
     "model.onnx".as_ref(),
     "input",                 // ONNX input name
     feature_order,           // Vec<String>, the column order fed to the model
-    1,                       // positive class index
+    Some(1),                 // positive class index: binary only (None → 1); None for other tasks
     None,                    // null fill: None → metadata.json absent_sentinel → NaN
     None,                    // contract path: None → {model_dir}/metadata.json
 )?;

model/src/contract.rs

@@ -128,8 +128,8 @@ fn resolve_binary_spec(
     if let Some(n) = classes.as_ref().map(Vec::len).or(width) {
         if positive_class_index >= n {
             return Err(CoreError::InvalidConfig(format!(
-                "--positive-class-index {positive_class_index} is out of range for a model \
-                 with {n} output columns"
+                "positive class index {positive_class_index} (--positive-class-index) is out \
+                 of range for a model with {n} output columns"
             )));
         }
     }
@@ -152,8 +152,8 @@ fn resolve_multi_spec(
 ) -> Result<OutputSpec, CoreError> {
     if positive_class_index.is_some() {
         return Err(CoreError::InvalidConfig(format!(
-            "--positive-class-index selects one column of a binary model; this is a {task} \
-             model and every column is scored"
+            "a positive class index (--positive-class-index) selects one column of a binary \
+             model; this is a {task} model and every column is scored"
         )));
     }
     let Some(classes) = classes.filter(|c| !c.is_empty()) else {
@@ -217,8 +217,8 @@ fn resolve_regression_spec(
     let invalid = |msg: String| Err(CoreError::InvalidConfig(msg));
     if positive_class_index.is_some() {
         return invalid(
-            "--positive-class-index selects one column of a binary model; a regression \
-             model has one output, its predicted value"
+            "a positive class index (--positive-class-index) selects one column of a binary \
+             model; a regression model has one output, its predicted value"
                 .to_string(),
         );
     }