Randomized SVD benchmark is broken

While updating the call of `fetch_openml`, I saw that the following benchmark is broken:
https://github.com/scikit-learn/scikit-learn/blob/main/benchmarks/bench_plot_randomized_svd.py

We should probably solve the issues.

COMMENT:
I think the first thing to do is to use named parameters:

```diff
diff --git a/benchmarks/bench_plot_randomized_svd.py b/benchmarks/bench_plot_randomized_svd.py
index c16e21c3e..a4480870b 100644
--- a/benchmarks/bench_plot_randomized_svd.py
+++ b/benchmarks/bench_plot_randomized_svd.py
@@ -281,9 +281,9 @@ def svd_timing(
         U, mu, V = randomized_svd(
             X,
             n_comps,
-            n_oversamples,
-            n_iter,
-            power_iteration_normalizer,
+            n_oversamples=n_oversamples,
+            n_iter=n_iter,
+            power_iteration_normalizer=power_iteration_normalizer,
             random_state=random_state,
             transpose=False,
         )
```

But then there are some infs in `Q = randomized_range_finder(...)`:

Excerpt from the output:
```
Getting dataset: lfw_people
 >>>>>> Benching sklearn and fbpca on lfw_people 13233 x 2914
.
.
.
n_iter = 6 on sklearn - none
... running SVD ...
/home/lesteve/dev/scikit-learn/sklearn/utils/extmath.py:152: RuntimeWarning: overflow encountered in matmul
  ret = a @ b
/home/lesteve/dev/scikit-learn/sklearn/utils/extmath.py:152: RuntimeWarning: invalid value encountered in matmul
  ret = a @ b
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~/dev/scikit-learn/benchmarks/bench_plot_randomized_svd.py in <module>
    510             % (dataset_name, X.shape[0], X.shape[1])
    511         )
--> 512         bench_a(
    513             X,
    514             dataset_name,

~/dev/scikit-learn/benchmarks/bench_plot_randomized_svd.py in bench_a(X, dataset_name, power_iter, n_oversamples, n_comps)
    351         for pm in ["none", "LU", "QR"]:
    352             print("n_iter = %d on sklearn - %s" % (pi, pm))
--> 353             U, s, V, time = svd_timing(
    354                 X,
    355                 n_comps,

~/dev/scikit-learn/benchmarks/bench_plot_randomized_svd.py in svd_timing(X, n_comps, n_iter, n_oversamples, power_iteration_normalizer, method)
    279         gc.collect()
    280         t0 = time()
--> 281         U, mu, V = randomized_svd(
    282             X,
    283             n_comps,

~/dev/scikit-learn/sklearn/utils/extmath.py in randomized_svd(M, n_components, n_oversamples, n_iter, power_iteration_normalizer, transpose, flip_sign, random_state)
    394         M = M.T
    395 
--> 396     Q = randomized_range_finder(
    397         M,
    398         size=n_random,

~/dev/scikit-learn/sklearn/utils/extmath.py in randomized_range_finder(A, size, n_iter, power_iteration_normalizer, random_state)
    243     # Sample the range of A using by linear projection of Q
    244     # Extract an orthonormal basis
--> 245     Q, _ = linalg.qr(safe_sparse_dot(A, Q), mode="economic")
    246     return Q
    247 

~/miniconda3/lib/python3.9/site-packages/scipy/linalg/decomp_qr.py in qr(a, overwrite_a, lwork, mode, pivoting, check_finite)
    125 
    126     if check_finite:
--> 127         a1 = numpy.asarray_chkfinite(a)
    128     else:
    129         a1 = numpy.asarray(a)

~/miniconda3/lib/python3.9/site-packages/numpy/lib/function_base.py in asarray_chkfinite(a, dtype, order)
    486     a = asarray(a, dtype=dtype, order=order)
    487     if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all():
--> 488         raise ValueError(
    489             "array must not contain infs or NaNs")
    490     return a

ValueError: array must not contain infs or NaNs
```

Labels: Bug