CI: skip cotengra numpy install for some

.github/workflows/CI.yml

@@ -60,7 +60,7 @@ jobs:
  install: |
  apt-get update
  apt-get install -y --no-install-recommends python3 python3-pip
- pip3 install -U pip pytest numpy cotengra
+ pip3 install -U pip pytest # numpy cotengra
  run: |
  set -e
  pip3 install cotengrust --find-links dist --force-reinstall

tests/test_cotengrust.py

@@ -1,10 +1,33 @@
 import pytest
-import numpy as np
-from numpy.testing import assert_allclose
-import cotengra as ctg
+
+try:
+ import cotengra as ctg
+
+ ctg_missing = False
+except ImportError:
+ ctg_missing = True
+ ctg = None
+
 import cotengrust as ctgr
 
 
+requires_cotengra = pytest.mark.skipif(ctg_missing, reason="requires cotengra")
+
+
+@pytest.mark.parametrize("which", ["greedy", "optimal"])
+def test_basic_call(which):
+ inputs = [('a', 'b'), ('b', 'c'), ('c', 'd'), ('d', 'a')]
+ output = ('b', 'd')
+ size_dict = {'a': 2, 'b': 3, 'c': 4, 'd': 5}
+ path = {
+ "greedy": ctgr.optimize_greedy,
+ "optimal": ctgr.optimize_optimal,
+ }[
+ which
+ ](inputs, output, size_dict)
+ assert all(len(con) <= 2 for con in path)
+
+
 def find_output_str(lhs):
  tmp_lhs = lhs.replace(",", "")
  return "".join(s for s in sorted(set(tmp_lhs)) if tmp_lhs.count(s) == 1)
@@ -21,20 +44,16 @@ def eq_to_inputs_output(eq):
 
 
 def get_rand_size_dict(inputs, d_min=2, d_max=3):
+ import random
+
  size_dict = {}
  for term in inputs:
  for ix in term:
  if ix not in size_dict:
- size_dict[ix] = np.random.randint(d_min, d_max + 1)
+ size_dict[ix] = random.randint(d_min, d_max)
  return size_dict
 
 
-def build_arrays(inputs, size_dict):
- return [
- np.random.randn(*[size_dict[ix] for ix in term]) for term in inputs
- ]
-
-
 # these are taken from opt_einsum
 test_case_eqs = [
  # Test scalar-like operations
@@ -120,24 +139,26 @@ def build_arrays(inputs, size_dict):
 ]
 
 
+@requires_cotengra
 @pytest.mark.parametrize("eq", test_case_eqs)
 @pytest.mark.parametrize("which", ["greedy", "optimal"])
 def test_manual_cases(eq, which):
  inputs, output = eq_to_inputs_output(eq)
  size_dict = get_rand_size_dict(inputs)
- arrays = build_arrays(inputs, size_dict)
- expected = np.einsum(eq, *arrays, optimize=True)
  path = {
  "greedy": ctgr.optimize_greedy,
  "optimal": ctgr.optimize_optimal,
  }[
  which
  ](inputs, output, size_dict)
  assert all(len(con) <= 2 for con in path)
- tree = ctg.ContractionTree.from_path(inputs, output, size_dict, path=path)
- assert_allclose(tree.contract(arrays), expected)
+ tree = ctg.ContractionTree.from_path(
+ inputs, output, size_dict, path=path, check=True
+ )
+ assert tree.is_complete()
 
 
+@requires_cotengra
 @pytest.mark.parametrize("seed", range(10))
 @pytest.mark.parametrize("which", ["greedy", "optimal"])
 def test_basic_rand(seed, which):
@@ -151,22 +172,20 @@ def test_basic_rand(seed, which):
  d_max=3,
  seed=seed,
  )
- eq = ",".join(map("".join, inputs)) + "->" + "".join(output)
-
  path = {
  "greedy": ctgr.optimize_greedy,
  "optimal": ctgr.optimize_optimal,
  }[
  which
  ](inputs, output, size_dict)
  assert all(len(con) <= 2 for con in path)
- tree = ctg.ContractionTree.from_path(inputs, output, size_dict, path=path)
- arrays = [np.random.randn(*s) for s in shapes]
- assert_allclose(
- tree.contract(arrays), np.einsum(eq, *arrays, optimize=True)
+ tree = ctg.ContractionTree.from_path(
+ inputs, output, size_dict, path=path, check=True
  )
+ assert tree.is_complete()
 
 
+@requires_cotengra
 def test_optimal_lattice_eq():
  inputs, output, _, size_dict = ctg.utils.lattice_equation(
  [4, 5], d_max=3, seed=42