Replacing csd in sample selection with california because the larger …

…dataset means a few redundant samples, which is not good for stable testing
scikit-learn-contrib · May 16, 2022 · ae695e9 · ae695e9
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diff --git a/examples/Selectors-Pipelines.ipynb b/examples/Selectors-Pipelines.ipynb
@@ -154,40 +154,6 @@
     "plt.ylabel('Predicted Values')\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-11-29T15:19:18.681434Z",
-     "iopub.status.busy": "2021-11-29T15:19:18.681006Z",
-     "iopub.status.idle": "2021-11-29T15:19:18.682770Z",
-     "shell.execute_reply": "2021-11-29T15:19:18.683119Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(442, 10)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/tests/test_sample_simple_cur.py b/tests/test_sample_simple_cur.py
@@ -3,14 +3,15 @@
 import numpy as np
 from sklearn import exceptions
 
-from skcosmo.datasets import load_csd_1000r as load
+from sklearn.datasets import fetch_california_housing as load
 from skcosmo.sample_selection import CUR
 
 
 class TestCUR(unittest.TestCase):
     def setUp(self):
-        self.X, self.y = load(return_X_y=True)
-        self.n_select = 20
+        self.X, _ = load(return_X_y=True)
+        self.X = self.X[:1000]
+        self.n_select = min(20, min(self.X.shape) // 2)
 
     def test_bad_transform(self):
         selector = CUR(n_to_select=2)