code: def test_binary_calibration_evaluator():
      np.random.seed(42)
      probs = np.linspace(1e-10, 1.0 - 1e-10, 100000)
      target = np.random.binomial(n=1, p=probs, size=100000)
  
      predictions = pd.DataFrame(
          {"target": target, "prediction": probs}
      )
  
      eval_fn = expected_calibration_error_evaluator(
          prediction_column="prediction",
          target_column="target",
          eval_name="eval_name",
          n_bins=100,
          bin_choice="count",
      )
  
      result_count = eval_fn(predictions)
  
      assert result_count["eval_name"] < 0.1
  
      eval_fn = expected_calibration_error_evaluator(
          prediction_column="prediction",
          target_column="target",
          eval_name="eval_name",
          n_bins=100,
          bin_choice="prob",
      )
  
      result_prob = eval_fn(predictions)
  
      assert result_prob["eval_name"] < 0.1
  
      assert (
          abs(result_count["eval_name"] - result_prob["eval_name"])
          < 1e-3
      )
  
imports: import sys
  import string
  import numpy
  import pandas
  import pytest
  from fklearn.validation.evaluators import auc_evaluator, brier_score_evaluator, combined_evaluators, correlation_evaluator, expected_calibration_error_evaluator, fbeta_score_evaluator, hash_evaluator, logloss_evaluator, mean_prediction_evaluator, mse_evaluator, permutation_evaluator, pr_auc_evaluator, precision_evaluator, r2_evaluator, recall_evaluator, roc_auc_evaluator, spearman_evaluator, linear_coefficient_evaluator, ndcg_evaluator, split_evaluator, temporal_split_evaluator, exponential_coefficient_evaluator, logistic_coefficient_evaluator
label_desc: Use image node_id 12 for calling a global function with example usage: test_binary_calibration_evaluator() without return types