log_logistic update

ehrapy/tools/_sa.py

@@ -585,13 +585,22 @@ def log_logistic_aft(
     adata: AnnData,
     duration_col: str,
     *,
+    inplace: bool = True,
+    key_added_prefix: str | None = None,
     alpha: float = 0.05,
     fit_intercept: bool = True,
     penalizer: float | np.ndarray = 0.0,
     l1_ratio: float = 0.0,
     model_ancillary: bool = False,
-    event_col: str = None,
-    entry_col: str = None,
+    event_col: str | None = None,
+    ancillary: bool | pd.DataFrame | str | None = None,
+    show_progress: bool = False,
+    weights_col: str | None = None,
+    robust: bool = False,
+    initial_point=None,
+    entry_col: str | None = None,
+    formula: str | None = None,
+    fit_options: dict | None = None,
 ) -> LogLogisticAFTFitter:
     """Fit the log logistic accelerated failure time regression for the survival function.
     The Log-Logistic Accelerated Failure Time (AFT) survival regression model is a powerful statistical tool employed in the analysis of time-to-event data.
@@ -603,9 +612,29 @@ def log_logistic_aft(
     Args:
         adata: AnnData object with necessary columns `duration_col` and `event_col`.
         duration_col: Name of the column in the AnnData objects that contains the subjects’ lifetimes.
-        event_col: Name of the column in anndata that contains the subjects’ death observation.
+        inplace: Whether to modify the AnnData object in place.
+        key_added_prefix: Prefix to add to the column names in the AnnData object. An underscore will be added between the prefix and the column
+        alpha: The alpha value in the confidence intervals.
+         alpha: The alpha value in the confidence intervals.
+        fit_intercept: Whether to fit an intercept term in the model.
+        penalizer: Attach a penalty to the size of the coefficients during regression. This improves stability of the estimates and controls for high correlation between covariates.
+        l1_ratio: Specify what ratio to assign to a L1 vs L2 penalty. Same as scikit-learn. See penalizer above.
+        model_ancillary: set the model instance to always model the ancillary parameter with the supplied Dataframe. This is useful for grid-search optimization.
+        event_col: Name of the column in anndata that contains the subjects’ death observation.  1 if observed, 0 else (censored).
                    If left as None, assume all individuals are uncensored.
+        ancillary: Choose to model the ancillary parameters.
+            If None or False, explicitly do not fit the ancillary parameters using any covariates.
+            If True, model the ancillary parameters with the same covariates as ``df``.
+            If DataFrame, provide covariates to model the ancillary parameters. Must be the same row count as ``df``.
+            If str, should be a formula
+        show_progress: since the fitter is iterative, show convergence diagnostics. Useful if convergence is failing.
+        weights_col: The name of the column in DataFrame that contains the weights for each subject.
+        robust: Compute the robust errors using the Huber sandwich estimator, aka Wei-Lin estimate. This does not handle ties, so if there are high number of ties, results may significantly differ.
+        initial_point: set the starting point for the iterative solver.
         entry_col: Column denoting when a subject entered the study, i.e. left-truncation.
+        formula: Use an R-style formula for modeling the dataset. See formula syntax: https://matthewwardrop.github.io/formulaic/basic/grammar/
+            If a formula is not provided, all variables in the dataframe are used (minus those used for other purposes like event_col, etc.)
+        fit_options: Additional keyword arguments to pass into the estimator.
 
     Returns:
         Fitted LogLogisticAFTFitter.
@@ -617,26 +646,35 @@ def log_logistic_aft(
         >>> adata[:, ["censor_flg"]].X = np.where(adata[:, ["censor_flg"]].X == 0, 1, 0)
         >>> llf = ep.tl.log_logistic_aft(adata, "mort_day_censored", "censor_flg")
     """
+    df = _regression_model_data_frame_preparation(adata, duration_col, accept_zero_duration=False)
 
-    return _regression_model(
-        LogLogisticAFTFitter, adata, duration_col, event_col, entry_col, accept_zero_duration=False
+    log_logistic_aft = LogLogisticAFTFitter(
+        alpha=alpha,
+        fit_intercept=fit_intercept,
+        penalizer=penalizer,
+        l1_ratio=l1_ratio,
+        model_ancillary=model_ancillary,
     )
 
+    log_logistic_aft.fit(
+        df,
+        duration_col=duration_col,
+        event_col=event_col,
+        entry_col=entry_col,
+        ancillary=ancillary,
+        show_progress=show_progress,
+        weights_col=weights_col,
+        robust=robust,
+        initial_point=initial_point,
+        formula=formula,
+        fit_options=fit_options,
+    )
 
-def _regression_model(
-    model_class, adata: AnnData, duration_col: str, event_col: str, entry_col: str = None, accept_zero_duration=True
-):
-    """Convenience function for regression models."""
-    df = anndata_to_df(adata)
-    df = df.dropna()
-
-    if not accept_zero_duration:
-        df.loc[df[duration_col] == 0, duration_col] += 1e-5
-
-    model = model_class()
-    model.fit(df, duration_col, event_col, entry_col=entry_col)
+    # Add the results to the AnnData object
+    if inplace:
+        _regression_model_populate_adata(adata, log_logistic_aft.summary, key_added_prefix)
 
-    return model
+    return log_logistic_aft
 
 
 def _univariate_model(