code: def test_decomposition(self):
      # Try to estimate the covariance from a specially crafted matrix.
      # The matrix is generated in the assumption that observations can be predicted by multiple factors.
      NUM_VARIABLE = 30
      NUM_OBSERVATION = 100
      NUM_FACTOR = 10
      EPS = 0.1
  
      estimator = StructuredCovEstimator(
          scale_return=False,
          assume_centered=True,
          num_factors=NUM_FACTOR,
      )
  
      F = np.random.rand(NUM_VARIABLE, NUM_FACTOR)
      B = np.random.rand(NUM_FACTOR, NUM_OBSERVATION)
      U = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE)
      X = (F @ B).T + U
  
      est_cov = estimator.predict(X, is_price=False)
      np_cov = np.cov(
          X.T
      )  # While numpy assume row means variable, qlib assume the other wise.
  
      delta = abs(est_cov - np_cov)
      if_identical = (delta < EPS).all()
  
      self.assertTrue(if_identical)
  
file_path: qlib/tests/test_structured_cov_estimator.py
imports: import unittest
  import numpy
  from scipy.linalg import sqrtm
  from qlib.model.riskmodel import StructuredCovEstimator
label_desc: Use image node_id 5 for calling the TestStructuredCovEstimator obj's underlying member method code with example usage: obj.test_decomposition() without return types