Merge d3fb550 into 0553483

.travis.yml

@@ -8,10 +8,10 @@ env:
   matrix:
     - export PANDAS_VERSION=0.23.4     && export NUMPY_VERSION=1.17.0
     - export PANDAS_VERSION=0.24.1     && export NUMPY_VERSION=1.17.0
-    - export PANDAS_VERSION=0.25.0     && export NUMPY_VERSION=1.17.0
-    - export NUMPY_VERSION=1.16.4      && export PANDAS_VERSION=0.25.0
-    - export NUMPY_VERSION=1.15.4      && export PANDAS_VERSION=0.25.0
-    - export NUMPY_VERSION=1.17.0      && export PANDAS_VERSION=0.25.0
+    - export PANDAS_VERSION=0.25.1     && export NUMPY_VERSION=1.17.0
+    - export NUMPY_VERSION=1.16.4      && export PANDAS_VERSION=0.25.1
+    - export NUMPY_VERSION=1.15.4      && export PANDAS_VERSION=0.25.1
+    - export NUMPY_VERSION=1.17.0      && export PANDAS_VERSION=0.25.1
 before_install:
   - ls
   # - sudo apt-get update

CHANGELOG.md

@@ -1,6 +1,15 @@
 ### Changelog
 
 
+#### 0.22.4
+
+##### New features
+ - Some performance improvements to regression models.
+
+##### Bug fixes
+ - Fixed issue where `concordance_index` would never exit if NaNs in dataset.
+
+
 #### 0.22.3
 
 ##### New features

lifelines/fitters/cox_time_varying_fitter.py

@@ -77,8 +77,6 @@ class CoxTimeVaryingFitter(BaseFitter):
         the strata provided
     standard_errors_: Series
         the standard errors of the estimates
-    score_: float
-        the concordance index of the model.
     baseline_cumulative_hazard_: DataFrame
     baseline_survival_: DataFrame
     """

lifelines/utils/concordance.py

@@ -263,4 +263,9 @@ def _preprocess_scoring_data(event_times, predicted_scores, event_observed):
         if event_observed.shape != event_times.shape:
             raise ValueError("Observed events must be 1-dimensional of same length as event times")
 
+    # check for NaNs
+    for a in [event_times, predicted_scores, event_observed]:
+        if np.isnan(a).any():
+            raise ValueError("NaNs detected in inputs, please correct or drop.")
+
     return event_times, predicted_scores, event_observed

tests/test_estimation.py

@@ -1392,6 +1392,34 @@ def test_BHF_fit_when_KMF_throws_an_error(self):
         bfh.fit(observations, entry=births)
 
 
+class TestParametricRegressionFitter:
+    @pytest.fixture
+    def rossi(self):
+        rossi = load_rossi()
+        rossi["_int"] = 1.0
+        return rossi
+
+    def test_custom_weibull_model_gives_the_same_data_as_implemented_weibull_model(self, rossi):
+        class CustomWeibull(ParametricRegressionFitter):
+
+            _fitted_parameter_names = ["lambda_", "rho_"]
+
+            def _cumulative_hazard(self, params, T, Xs):
+                lambda_ = anp.exp(anp.dot(Xs["lambda_"], params["lambda_"]))
+                rho_ = anp.exp(anp.dot(Xs["rho_"], params["rho_"]))
+
+                return (T / lambda_) ** rho_
+
+        cb = CustomWeibull()
+        wf = WeibullAFTFitter(fit_intercept=False)
+
+        cb.fit(rossi, "week", "arrest", regressors={"lambda_": rossi.columns, "rho_": ["_int"]})
+        wf.fit(rossi, "week", "arrest")
+
+        assert_frame_equal(cb.summary.loc["lambda_"], wf.summary.loc["lambda_"], check_less_precise=2)
+        npt.assert_allclose(cb.log_likelihood_, wf.log_likelihood_)
+
+
 class TestRegressionFitters:
     @pytest.fixture
     def rossi(self):