docstr: Just tests inputs/outputs (not validity of result).
docstr_tok: ["Just","tests","inputs\/outputs","(","not","validity","of","result",")","."]
code: def test_integration_quic_graph_lasso_cv(self, params_in):
      """
      Just tests inputs/outputs (not validity of result).
      """
      n_features = 30
      n_samples = 30
      cov, prec, adj = ClusterGraph(
          n_blocks=1, chain_blocks=False, seed=1
      ).create(n_features, 0.8)
      prng = np.random.RandomState(2)
      X = prng.multivariate_normal(
          np.zeros(n_features), cov, size=n_samples
      )
  
      ma = ModelAverage(**params_in)
      ma.fit(X)
  
      n_examples, n_features = X.shape
  
      assert ma.proportion_.shape == (n_features, n_features)
      assert len(ma.estimators_) == ma.n_trials
      assert len(ma.subsets_) == ma.n_trials
      if not ma.penalization == "subsampling":
          assert len(ma.lams_) == ma.n_trials
      else:
          assert len(ma.lams_) == ma.n_trials
          assert ma.lams_[0] is None
  
      for eidx, e in enumerate(ma.estimators_):
          assert isinstance(e, params_in["estimator"].__class__)
  
          # sklearn doesnt have this but ours do
          if hasattr(e, "is_fitted"):
              assert e.is_fitted is True
  
          # check that all lambdas used where different
          if not ma.penalization == "subsampling" and eidx > 0:
              if hasattr(e, "lam"):
                  prev_e = ma.estimators_[eidx - 1]
                  assert np.linalg.norm((prev_e.lam - e.lam).flat) > 0
  
      if ma.normalize is True:
          assert np.max(ma.proportion_) <= 1.0
      else:
          assert np.max(ma.proportion_) <= ma.n_trials
  
      assert np.min(ma.proportion_) >= 0.0
      assert np.max(ma.proportion_) > 0.0
  
file_path: skggm/inverse_covariance/tests/model_average_test.py
imports: import numpy
  import pytest
  from sklearn.covariance import GraphicalLassoCV
  from inverse_covariance import QuicGraphicalLassoCV, QuicGraphicalLasso, ModelAverage
  from inverse_covariance.profiling import ClusterGraph
label_desc: Use image node_id 1 for calling the TestModelAverage obj's underlying member method code with example usage: obj.test_integration_quic_graph_lasso_cv(params_in) without return types