docstr: Just tests inputs/outputs (not validity of result).
docstr_tok: ["Just","tests","inputs\/outputs","(","not","validity","of","result",")","."]
code: def test_integration_adaptive_graphical_lasso(self, params_in):
      """
      Just tests inputs/outputs (not validity of result).
      """
      n_features = 20
      n_samples = 25
      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
      )
  
      model = AdaptiveGraphicalLasso(**params_in)
      model.fit(X)
      assert model.estimator_ is not None
      assert model.lam_ is not None
  
      assert np.sum(model.lam_[np.diag_indices(n_features)]) == 0
  
      if params_in["method"] == "binary":
          uvals = set(model.lam_.flat)
          assert len(uvals) == 2
          assert 0 in uvals
          assert 1 in uvals
      elif (
          params_in["method"] == "inverse"
          or params_in["method"] == "inverse_squared"
      ):
          uvals = set(model.lam_.flat[model.lam_.flat != 0])
          assert len(uvals) > 0
  
imports: import numpy
  import pytest
  from inverse_covariance import QuicGraphicalLassoEBIC, AdaptiveGraphicalLasso, QuicGraphicalLassoCV
  from inverse_covariance.profiling import ClusterGraph
label_desc: Use image node_id 1 for calling the TestAdaptiveGraphicalLasso obj's underlying member method code with example usage: obj.test_integration_adaptive_graphical_lasso(params_in) without return types