Merge pull request #706 from Mohit-Ak/fix/generate-data-zero-offset-collapse

Fix generate_data producing zero-variance outliers for some seeds

Author: yzhao062

pyod/test/test_data.py

@@ -84,6 +84,40 @@ def test_data_generate3(self):
         assert_allclose(y_train, y_train2)
         assert_allclose(y_test, y_test2)
 
+    def test_data_generate_outliers_have_spread(self):
+        # Regression test for GH #141: for certain seeds the internal offset
+        # was drawn as 0, which collapsed every outlier onto the origin
+        # (uniform(-0, 0) == 0) and produced zero-variance outliers. Sweep a
+        # range of seeds (41, 48 and 50 previously triggered the collapse)
+        # and confirm outliers always keep a non-zero spread.
+        for seed in range(60):
+            X, y = generate_data(
+                n_features=2,
+                contamination=0.05,
+                train_only=True,
+                random_state=seed,
+            )
+            outliers = X[y == 1]
+            assert outliers.var() > 0, \
+                "outliers collapsed to zero variance for random_state=%d" % seed
+
+    def test_data_generate_reproducibility(self):
+        # Golden values pinned from the pre-fix implementation for seeds whose
+        # offset was already non-zero. Redrawing only when the offset comes out
+        # as 0 leaves these untouched, so this guards against a future change
+        # silently altering long-standing fixed-seed output.
+        golden = {
+            0: ([5.059894904, 5.061739412], [-0.263919547, 3.009107520]),
+            1: ([3.401186423, 3.524852969], [0.181525489, 3.650202520]),
+            42: ([6.433658544, 5.509168303], [-3.206743915, -4.912722786]),
+        }
+        for seed, (first, last) in golden.items():
+            X, _ = generate_data(n_train=10, n_test=5, n_features=2,
+                                 contamination=0.2, train_only=True,
+                                 random_state=seed)
+            assert_allclose(X[0], first, rtol=0, atol=1e-9)
+            assert_allclose(X[-1], last, rtol=0, atol=1e-9)
+
     def test_data_generate_cluster(self):
         X_train, X_test, y_train, y_test = \
             generate_data_clusters(n_train=self.n_train,

pyod/utils/data.py

@@ -176,7 +176,15 @@ def generate_data(n_train=1000, n_test=500, n_features=2, contamination=0.1,
 
     # initialize a random state and seeds for the instance
     random_state = check_random_state(random_state)
+    # randint(low=offset) samples from [0, offset), so it can return 0. A zero
+    # offset collapses every outlier onto the origin (uniform(-0, 0) == 0) and
+    # yields zero-variance outliers for those seeds (see GH #141). Only redraw
+    # when that happens, so seeds that already produced a valid offset keep
+    # their existing output.
     offset_ = random_state.randint(low=offset)
+    if offset_ == 0:
+        offset_ = (1 if offset == 1
+                   else random_state.randint(low=1, high=offset))
     coef_ = random_state.random_sample() + 0.001  # in case of underflow
 
     if isinstance(contamination, (float, int)):