TST: Better to test that warm_start runs only once after the prev model has converged

Author: MechCoder

sklearn/linear_model/tests/test_coordinate_descent.py

@@ -6,7 +6,6 @@
 
 import numpy as np
 from scipy import interpolate, sparse
-from copy import deepcopy
 
 from sklearn.utils.testing import assert_array_almost_equal
 from sklearn.utils.testing import assert_almost_equal
@@ -470,12 +469,23 @@ def test_precompute_invalid_argument():
 
 def test_warm_start_convergence():
     X, y, _, _ = build_dataset()
-    cold_model = ElasticNet(alpha=1e-3, tol=1e-5, max_iter=2000)
-    cold_model.fit(X, y)
-    warm_model = deepcopy(cold_model).set_params(warm_start=True)
-    warm_model.fit(X, y)
-    assert_greater(cold_model.n_iter_, warm_model.n_iter_)
-    assert_greater(warm_model.n_iter_, 1)
+    model = ElasticNet(alpha=1e-3, tol=1e-3).fit(X, y)
+    n_iter_reference = model.n_iter_
+
+    # This dataset is not trivial enough for the model to converge in one pass.
+    assert_greater(n_iter_reference, 2)
+
+    # Fit the same model again, using a cold start
+    model.fit(X, y)
+    n_iter_cold_start = model.n_iter_
+    assert_equal(n_iter_cold_start, n_iter_reference)
+
+    # Fit the same model again, using a warm start: the optimizer just perform
+    # a single pass before checking that it has already converged
+    model.set_params(warm_start=True)
+    model.fit(X, y)
+    n_iter_warm_start = model.n_iter_
+    assert_equal(n_iter_warm_start, 1)
 
 
 if __name__ == '__main__':