fix tests

lifelines/fitters/__init__.py

@@ -295,7 +295,7 @@ def __init__(self, *args, **kwargs):
 
     @property
     def AIC_(self) -> float:
-        return -2 * self.log_likelihood_ + 2 * self.params_.shape[0]
+        return -2 * self.log_likelihood_ + 2 * self._fitted_parameters_.shape[0]
 
     def _check_cumulative_hazard_is_monotone_and_positive(self, durations, values):
         class_name = self._class_name
@@ -1940,7 +1940,7 @@ def _compute_variance_matrix(self) -> np.array:
                 Some ways to possible ways fix this:
 
                 0. Are there any lifelines warnings outputted during the `fit`?
-                1. Inspect your DataFrame: does everything look as expected?
+                1. Inspect your DataFrame: does everything look as expected? Do you need to add/drop a constant (intercept) column?
                 2. Is there high-collinearity in the dataset? Try using the variance inflation factor (VIF) to find redundant variables.
                 3. Trying adding a small penalizer (or changing it, if already present). Example: `%s(penalizer=0.01).fit(...)`.
                 4. Are there any extreme outliers? Try modeling them or dropping them to see if it helps convergence.
@@ -2130,8 +2130,8 @@ def print_summary(self, decimals=2, style=None, **kwargs):
         sr = self.log_likelihood_ratio_test()
         footers = []
 
-        if CensoringType.is_right_censoring(self):
-            footers.apoend(("Concordance", "{:.{prec}f}".format(self.concordance_index_, prec=decimals)))
+        if utils.CensoringType.is_right_censoring(self):
+            footers.append(("Concordance", "{:.{prec}f}".format(self.concordance_index_, prec=decimals)))
 
         footers.extend(
             [

lifelines/tests/test_estimation.py

@@ -228,6 +228,14 @@ def _cumulative_hazard(self, params, times):
         assert coef - std > lower
         assert coef + std < upper
 
+    def test_AIC_on_models(self, known_parametric_univariate_fitters):
+        T = np.random.exponential(1, size=1000)
+
+        for fitter in known_parametric_univariate_fitters:
+            f = fitter().fit(T)
+            assert f.AIC_ > 0
+            npt.assert_allclose(f.AIC_, -2 * f.log_likelihood_ + 2 * f.summary.shape[0])
+
     def test_models_can_handle_really_large_duration_values(self, known_parametric_univariate_fitters):
         T1 = np.random.exponential(1e12, size=1000)
         T2 = np.random.exponential(1e12, size=1000)
@@ -1613,18 +1621,22 @@ def rossi(self):
         rossi["_int"] = 1.0
         return rossi
 
+    def test_AIC_on_models(self, rossi):
+        model = WeibullAFTFitter(fit_intercept=False).fit(rossi, "week", "arrest")
+        npt.assert_allclose(model.AIC_, -2 * model.log_likelihood_ + 2 * model.summary.shape[0])
+
     def test_penalizer_can_be_an_array(self, rossi):
 
-        wf_array = WeibullAFTFitter(penalizer=0.01 * np.ones(7)).fit(rossi, "week", "arrest")
-        wf_float = WeibullAFTFitter(penalizer=0.01).fit(rossi, "week", "arrest")
+        wf_array = WeibullAFTFitter(penalizer=0.01 * np.ones(7), fit_intercept=False).fit(rossi, "week", "arrest")
+        wf_float = WeibullAFTFitter(penalizer=0.01, fit_intercept=False).fit(rossi, "week", "arrest")
 
         assert_frame_equal(wf_array.summary, wf_float.summary)
 
     def test_penalizer_can_be_an_array_and_check_it_behaves_as_expected(self, rossi):
 
         penalty = np.array([0, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01])
-        wf_array = WeibullAFTFitter(penalizer=penalty).fit(rossi, "week", "arrest")
-        wf_float = WeibullAFTFitter(penalizer=0.01).fit(rossi, "week", "arrest")
+        wf_array = WeibullAFTFitter(penalizer=penalty, fit_intercept=False).fit(rossi, "week", "arrest")
+        wf_float = WeibullAFTFitter(penalizer=0.01, fit_intercept=False).fit(rossi, "week", "arrest")
 
         assert abs(wf_array.summary.loc[("lambda_", "fin"), "coef"]) > abs(wf_float.summary.loc[("lambda_", "fin"), "coef"])
 
@@ -2696,6 +2708,10 @@ def test_penalizer_can_be_an_array_and_check_it_behaves_as_expected(self, rossi)
 
         assert abs(cph_array.summary.loc["fin", "coef"]) > abs(cph_float.summary.loc["fin", "coef"])
 
+    def test_AIC_partial_(self, cph, rossi):
+        cph.fit(rossi, "week", "arrest")
+        npt.assert_allclose(cph.AIC_partial_, -2 * cph.log_likelihood_ + 2 * cph.summary.shape[0])
+
     def test_compute_followup_hazard_ratios(self, cph, cph_spline, rossi):
         cph.fit(rossi, "week", "arrest")
         cph.compute_followup_hazard_ratios(rossi, [15, 25, 35, 45])