cox_ph add all arguments

ehrapy/tools/_sa.py

@@ -3,7 +3,6 @@
 import warnings
 from typing import TYPE_CHECKING, Literal
 
-import numpy as np  # This package is implicitly used
 import pandas as pd
 import statsmodels.api as sm
 import statsmodels.formula.api as smf
@@ -23,6 +22,7 @@
 if TYPE_CHECKING:
     from collections.abc import Iterable
 
+    import numpy as np
     from anndata import AnnData
     from statsmodels.genmod.generalized_linear_model import GLMResultsWrapper
 
@@ -347,23 +347,43 @@ def anova_glm(result_1: GLMResultsWrapper, result_2: GLMResultsWrapper, formula_
     return dataframe
 
 
-def _regression_model(
-    model_class, adata: AnnData, duration_col: str, event_col: str, entry_col: str = None, accept_zero_duration=True
-):
+def _regression_model_data_frame_preparation(adata: AnnData, duration_col: str, 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)
-
-    return model
+    return df
 
 
-def cox_ph(adata: AnnData, duration_col: str, event_col: str, entry_col: str = None) -> CoxPHFitter:
+def cox_ph(
+    adata: AnnData,
+    duration_col: str,
+    *,
+    inplace: bool = True,
+    key_added_prefix: str | None = None,
+    alpha: float = 0.05,
+    label: str | None = None,
+    baseline_estimation_method: Literal["breslow", "spline", "piecewise"] = "breslow",
+    penalizer: float | np.ndarray = 0.0,
+    l1_ratio: float = 0.0,
+    strata: list[str] | str | None = None,
+    n_baseline_knots: int = 4,
+    knots: list[float] | None = None,
+    breakpoints: list[float] | None = None,
+    event_col: str = None,
+    weights_col: str | None = None,
+    cluster_col: str | None = None,
+    entry_col: str = None,
+    robust: bool = False,
+    formula: str = None,
+    batch_mode: bool = None,
+    show_progress: bool = False,
+    initial_point: np.ndarray | None = None,
+    fit_options: dict | None = None,
+) -> CoxPHFitter:
     """Fit the Cox’s proportional hazard for the survival function.
 
     The Cox proportional hazards model (CoxPH) examines the relationship between the survival time of subjects and one or more predictor variables.
@@ -376,7 +396,26 @@ def cox_ph(adata: AnnData, duration_col: str, event_col: str, entry_col: str = N
         duration_col: The name of the column in the AnnData objects that contains the subjects’ lifetimes.
         event_col: The name of the column in anndata that contains the subjects’ death observation.
                    If left as None, assume all individuals are uncensored.
+        inplace: Whether to modify the AnnData object in place.
+        alpha: The alpha value in the confidence intervals.
+        label: A string to name the column of the estimate.
+        baseline_estimation_method: The method used to estimate the baseline hazard. Options are 'breslow', 'spline', and 'piecewise'.
+        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.
+        strata: specify a list of columns to use in stratification. This is useful if a categorical covariate does not obey the proportional hazard assumption. This is used similar to the strata expression in R. See http://courses.washington.edu/b515/l17.pdf.
+        n_baseline_knots: Used when baseline_estimation_method="spline". Set the number of knots (interior & exterior) in the baseline hazard, which will be placed evenly along the time axis. Should be at least 2. Royston et. al, the authors of this model, suggest 4 to start, but any values between 2 and 8 are reasonable. If you need to customize the timestamps used to calculate the curve, use the knots parameter instead.
+        knots: When baseline_estimation_method="spline", this allows customizing the points in the time axis for the baseline hazard curve. To use evenly-spaced points in time, the n_baseline_knots parameter can be employed instead.
+        breakpoints: Used when baseline_estimation_method="piecewise". Set the positions of the baseline hazard breakpoints.
+        event_col: he name of the column in DataFrame that contains the subjects’ death observation. If left as None, assume all individuals are uncensored.
+        weights_col: The name of the column in DataFrame that contains the weights for each subject.
+        cluster_col: The name of the column in DataFrame that contains the cluster variable. Using this forces the sandwich estimator (robust variance estimator) to be used.
         entry_col: Column denoting when a subject entered the study, i.e. left-truncation.
+        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.
+        formula: an Wilkinson formula, like in R and statsmodels, for the right-hand-side. If left as None, all columns not assigned as durations, weights, etc. are used. Uses the library Formulaic for parsing.
+        batch_mode:  enabling batch_mode can be faster for datasets with a large number of ties. If left as None, lifelines will choose the best option.
+        show_progress: since the fitter is iterative, show convergence diagnostics. Useful if convergence is failing.
+        initial_point: set the starting point for the iterative solver.
+        fit_options: Additional keyword arguments to pass into the estimator.
 
     Returns:
         Fitted CoxPHFitter.
@@ -388,10 +427,80 @@ def cox_ph(adata: AnnData, duration_col: str, event_col: str, entry_col: str = N
         >>> adata[:, ["censor_flg"]].X = np.where(adata[:, ["censor_flg"]].X == 0, 1, 0)
         >>> cph = ep.tl.cox_ph(adata, "mort_day_censored", "censor_flg")
     """
-    return _regression_model(CoxPHFitter, adata, duration_col, event_col, entry_col)
+    df = _regression_model_data_frame_preparation(adata, duration_col)
+    cox_ph = CoxPHFitter(
+        alpha=alpha,
+        label=label,
+        strata=strata,
+        baseline_estimation_method=baseline_estimation_method,
+        penalizer=penalizer,
+        l1_ratio=l1_ratio,
+        n_baseline_knots=n_baseline_knots,
+        knots=knots,
+        breakpoints=breakpoints,
+    )
+    cox_ph.fit(
+        df,
+        duration_col=duration_col,
+        event_col=event_col,
+        entry_col=entry_col,
+        robust=robust,
+        initial_point=initial_point,
+        weights_col=weights_col,
+        cluster_col=cluster_col,
+        batch_mode=batch_mode,
+        formula=formula,
+        fit_options=fit_options,
+        show_progress=show_progress,
+    )
+
+    # Add the results to the AnnData object
+    if inplace:
+        if key_added_prefix is None:
+            key_added_prefix = ""
+        else:
+            key_added_prefix = key_added_prefix + "_"
+
+        cox_ph_summary = cox_ph.summary
+        print(cox_ph_summary)
+
+        full_results = pd.DataFrame(index=adata.var.index)
+
+        # Populate with CoxPH summary data
+        for key in cox_ph_summary.columns:
+            full_results[key_added_prefix + key] = cox_ph_summary[key]
+
+        # Add a boolean column indicating rows populated by this function
+        full_results[key_added_prefix + "cox_ph_populated"] = full_results.notna().any(axis=1)
+
+        # Assign results back to adata.var
+        for col in full_results.columns:
+            adata.var[col] = full_results[col]
+
+    return cox_ph
 
 
-def weibull_aft(adata: AnnData, duration_col: str, event_col: str, entry_col: str = None) -> WeibullAFTFitter:
+def weibull_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 = True,
+    event_col: str | None = None,
+    ancillary: bool | pd.DataFrame | 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,
+) -> WeibullAFTFitter:
     """Fit the Weibull accelerated failure time regression for the survival function.
 
     The Weibull Accelerated Failure Time (AFT) survival regression model is a statistical method used to analyze time-to-event data,
@@ -417,10 +526,22 @@ def weibull_aft(adata: AnnData, duration_col: str, event_col: str, entry_col: st
         >>> adata[:, ["censor_flg"]].X = np.where(adata[:, ["censor_flg"]].X == 0, 1, 0)
         >>> aft = ep.tl.weibull_aft(adata, "mort_day_censored", "censor_flg")
     """
+
     return _regression_model(WeibullAFTFitter, adata, duration_col, event_col, entry_col, accept_zero_duration=False)
 
 
-def log_logistic_aft(adata: AnnData, duration_col: str, event_col: str, entry_col: str = None) -> LogLogisticAFTFitter:
+def log_logistic_aft(
+    adata: AnnData,
+    duration_col: str,
+    *,
+    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,
+) -> 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.
     This model operates under the assumption that the logarithm of survival time adheres to a log-logistic distribution, offering a flexible framework for understanding the impact of covariates on survival times.
@@ -450,6 +571,22 @@ def log_logistic_aft(adata: AnnData, duration_col: str, event_col: str, entry_co
     )
 
 
+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)
+
+    return model
+
+
 def _univariate_model(
     adata: AnnData,
     duration_col: str,