revert import changes in survival example

Author: Badr-MOUFAD

examples/plot_survival_analysis.py

@@ -15,15 +15,7 @@
 # Let's first generate synthetic data on which to run the Cox estimator,
 # using ``skglm`` data utils.
 #
-import warnings
-import time
-from lifelines import CoxPHFitter
-import pandas as pd
-import numpy as np
-from skglm.solvers import ProxNewton
-from skglm.penalties import L1
-from skglm.datafits import Cox
-import matplotlib.pyplot as plt
+
 from skglm.utils.data import make_dummy_survival_data
 
 n_samples, n_features = 500, 100
@@ -43,6 +35,7 @@
 # * ``s`` indicates the observations censorship and follows a Bernoulli(0.5) distribution
 #
 # Let's inspect the data quickly:
+import matplotlib.pyplot as plt
 
 fig, axes = plt.subplots(
     1, 3,
@@ -67,7 +60,9 @@
 # Todo so, we need to combine a Cox datafit and a :math:`\ell_1` penalty
 # and solve the resulting problem using skglm Proximal Newton solver ``ProxNewton``.
 # We set the intensity of the :math:`\ell_1` regularization to ``alpha=1e-2``.
-
+from skglm.penalties import L1
+from skglm.datafits import Cox
+from skglm.solvers import ProxNewton
 
 # regularization intensity
 alpha = 1e-2
@@ -94,6 +89,9 @@
 # %%
 # Let's solve the problem with ``lifelines`` through its ``CoxPHFitter``
 # estimator and compare the objectives found by the two packages.
+import numpy as np
+import pandas as pd
+from lifelines import CoxPHFitter
 
 # format data
 stacked_y_X = np.hstack((y, X))
@@ -127,6 +125,8 @@
 # let's compare their execution time. To get the evolution of the suboptimality
 # (objective - optimal objective) we run both estimators with increasing number of
 # iterations.
+import time
+import warnings
 
 warnings.filterwarnings('ignore')