Merge pull request #1643 from CamDavidsonPilon/0.30.0

0.30.0

Author: CamDavidsonPilon

.travis.yml

@@ -1,11 +1,15 @@
 ## Changelog
 
+#### 0.30.0 - 2024-10-29
+ - update dependencies (numpy >= 1.14.0)
+ - fix for `decimal` kwarg not working in StatisticalResult
+
+
 #### 0.29.0 - 2024-06-25
  - update dependencies (pandas >= 2.1)
  - update dependencies (scipy >= 1.7)
 
 
-
 #### 0.28.0 - 2024-01-03
  - Fixes bins that are far into the future with using `survival_table_from_events`, see #1587
  - Removed `sklean_adaptor`. It was a terrible hack, and causing more confusion and support debt than I want. This cleans up our API and simplifies the library. ✨ There's no replacement, and I doubt I'll introduce one ✨

CHANGELOG.md

@@ -2991,7 +2991,11 @@ def __init__(self, strata, strata_values, n_baseline_knots=1, knots=None, *args,
 
     @staticmethod
     def _strata_labeler(stratum, i):
-        return "s%s_phi%d_" % (stratum, i)
+        try:
+            return "s%s_phi%d_" % (tuple(str(s) for s in stratum), i)
+        except:
+            # singleton
+            return "s%s_phi%d_" % (stratum, i)
 
     @property
     def _fitted_parameter_names(self):
@@ -3112,7 +3116,11 @@ def __init__(self, strata, strata_values, breakpoints, *args, **kwargs):
 
     @staticmethod
     def _strata_labeler(stratum, i):
-        return "s%s_lambda%d_" % (stratum, i)
+        try:
+            return "s%s_lambda%d_" % (tuple(str(s) for s in stratum), i)
+        except:
+            # singleton
+            return "s%s_lambda%d_" % (stratum, i)
 
     @property
     def _fitted_parameter_names(self):

lifelines/fitters/coxph_fitter.py

@@ -77,7 +77,7 @@ def __init__(self, p_value, test_statistic, name=None, test_name=None, **kwargs)
         kwargs["test_name"] = test_name
         self._kwargs = kwargs
 
-    def _print_specific_style(self, style, **kwargs):
+    def _print_specific_style(self, style, decimals=2, **kwargs):
         """
         Parameters
         -----------
@@ -87,11 +87,11 @@ def _print_specific_style(self, style, **kwargs):
 
         """
         if style == "html":
-            return self._html_print(**kwargs)
+            return self._html_print(decimals=decimals, **kwargs)
         elif style == "ascii":
-            return self._ascii_print(**kwargs)
+            return self._ascii_print(decimals=decimals, **kwargs)
         elif style == "latex":
-            return self._latex_print(**kwargs)
+            return self._latex_print(decimals=decimals, **kwargs)
         else:
             raise ValueError("style not available.")
 
@@ -110,27 +110,26 @@ def print_summary(self, decimals=2, style=None, **kwargs):
             multiple outputs.
 
         """
-        self.decimals=decimals
         if style is not None:
-            self._print_specific_style(style)
+            self._print_specific_style(style, decimals=decimals, **kwargs)
         else:
             try:
                 from IPython.display import display
 
                 display(self)
             except ImportError:
-                self._ascii_print()
+                self._ascii_print(decimals=decimals, **kwargs)
 
-    def _html_print(self, **kwargs):
-        print(self.to_html(**kwargs))
+    def _html_print(self, decimals=2, **kwargs):
+        print(self.to_html(decimals, **kwargs))
 
-    def _latex_print(self, **kwargs):
-        print(self.to_latex(**kwargs))
+    def _latex_print(self, decimals=2, **kwargs):
+        print(self.to_latex(decimals, **kwargs))
 
-    def _ascii_print(self, **kwargs):
-        print(self.to_ascii(**kwargs))
+    def _ascii_print(self, decimals=2, **kwargs):
+        print(self.to_ascii(decimals, **kwargs))
 
-    def to_html(self, **kwargs):
+    def to_html(self, decimals=2, **kwargs):
         extra_kwargs = dict(list(self._kwargs.items()) + list(kwargs.items()))
         summary_df = self.summary
 
@@ -141,14 +140,14 @@ def to_html(self, **kwargs):
         header_df = pd.DataFrame.from_records(headers).set_index(0)
         header_html = header_df.to_html(header=False, notebook=True, index_names=False)
 
-        summary_html = summary_df.to_html(float_format=format_floats(self.decimals), formatters={**{"p": format_p_value(self.decimals)}})
+        summary_html = summary_df.to_html(float_format=format_floats(decimals), formatters={**{"p": format_p_value(decimals)}})
 
         return header_html + summary_html
 
-    def to_latex(self, **kwargs):
+    def to_latex(self, decimals=2, **kwargs):
         # This is using the new Style object in Pandas. Previously df.to_latex was giving a warning.
         s = self.summary.style
-        s = s.format(precision=self.decimals)
+        s = s.format(precision=decimals)
         return s.to_latex()
 
     @property
@@ -173,7 +172,7 @@ def summary(self):
         df["-log2(p)"] = -utils.quiet_log2(df["p"])
         return df
 
-    def to_ascii(self, **kwargs):
+    def to_ascii(self, decimals=2, **kwargs):
         extra_kwargs = dict(list(self._kwargs.items()) + list(kwargs.items()))
         meta_data = self._stringify_meta_data(extra_kwargs)
 
@@ -183,7 +182,7 @@ def to_ascii(self, **kwargs):
         s += "\n" + meta_data + "\n"
         s += "---\n"
         s += df.to_string(
-            float_format=format_floats(self.decimals), index=self.name is not None, formatters={"p": format_p_value(self.decimals)}
+            float_format=format_floats(decimals), index=self.name is not None, formatters={"p": format_p_value(decimals)}
         )
 
         return s
@@ -836,7 +835,7 @@ def multivariate_logrank_test(
     assert abs(Z_j.sum()) < 10e-8, "Sum is not zero."  # this should move to a test eventually.
 
     # compute covariance matrix
-    factor = (((n_i - d_i) / (n_i - 1)).replace([np.inf, np.nan], 1)) * d_i / n_i ** 2
+    factor = (((n_i - d_i) / (n_i - 1)).replace([np.inf, np.nan], 1)) * d_i / n_i**2
     n_ij["_"] = n_i.values
     V_ = (n_ij.mul(w_i, axis=0)).mul(np.sqrt(factor), axis="index").fillna(0)  # weighted V_
     V = -np.dot(V_.T, V_)
@@ -924,7 +923,7 @@ def proportional_hazard_test(
     def compute_statistic(times, resids, n_deaths):
         demeaned_times = times - times.mean()
         T = (demeaned_times.values[:, None] * resids.values).sum(0) ** 2 / (
-            n_deaths * (fitted_cox_model.standard_errors_ ** 2) * (demeaned_times ** 2).sum()
+            n_deaths * (fitted_cox_model.standard_errors_**2) * (demeaned_times**2).sum()
         )
         return T
 

lifelines/statistics.py

@@ -3320,7 +3320,7 @@ def test_strata_estimation_is_same_if_using_trivial_strata(self, rossi):
 
         assert_frame_equal(
             cph.summary.loc[[("beta_", "Intercept"), ("phi1_", "Intercept")]].reset_index(drop=True),
-            trivial_strata_cph.summary.loc[[("beta_", "Intercept"), ("sa_phi1_", "Intercept")]].reset_index(drop=True),
+            trivial_strata_cph.summary.loc[[("beta_", "Intercept"), ("s('a',)_phi1_", "Intercept")]].reset_index(drop=True),
             atol=0.05,
         )
 

lifelines/tests/test_estimation.py

@@ -1,4 +1,4 @@
 # -*- coding: utf-8 -*-
 from __future__ import unicode_literals
 
-__version__ = "0.29.0"
+__version__ = "0.30.0"

lifelines/version.py

@@ -1,4 +1,4 @@
-numpy>=1.14.0,<2.0
+numpy>=1.14.0
 scipy>=1.7.0
 pandas>=2.1
 matplotlib>=3.0