class_docstr: Suggests an architecture based on the Harmonica algorithm.
docstr: Randomly samples architectures, predict loss, and return minimal.
docstr_tok: ["Randomly","samples","architectures",",","predict","loss",",","and","return","minimal","."]
code: def get_good_architecture(self, num_samples, coefficients):
      """Randomly samples architectures, predict loss, and return minimal."""
      if self._seed:
          np.random.seed(seed=self._seed)
  
      assignments = []
      architectures = []
      for _ in range(num_samples):
          rand_arc = np.random.randint(
              len(self._block_indices),
              size=self._phoenix_spec.minimum_depth,
          )
          arc_list = [self._block_indices[i] for i in rand_arc]
          # Tranlsate to a valid architecture for Phoenix.
          arc_list = architecture_utils.fix_architecture_order(
              arc_list, self._phoenix_spec.problem_type
          )
          arc = np.array(arc_list)
          architectures.append(arc)
          # Translate to assignments to predict loss.
          assignments.append(
              self.translate_architecture_to_feature_assignment(arc)
          )
  
      # Expand to polynomial features.
      assignments = self.get_polynomial_expansion(
          assignments, self._degree
      )
      # Predict loss based on coefficient of the linear model
      predictions = np.matmul(assignments, coefficients)
      # Index of the minimal loos
      minimal_index = np.argmin(predictions)
      # Return the Phoenix architecutre for the minimal loss
      return architectures[minimal_index]
  
file_path: model_search/model_search/search/harmonica.py
imports: from absl import logging
  from model_search import block_builder
  from model_search.architecture import architecture_utils
  from model_search.search import common
  from model_search.search import search_algorithm
  import numpy
  from sklearn import linear_model
  from sklearn import preprocessing
label_desc: Use image node_id 5 for calling the Harmonica obj's underlying member method code with example usage: obj.get_good_architecture(num_samples, coefficients) and returns: architectures