docstr: ...Test LogReg prediction
          
docstr_tok: ["...","Test","LogReg","prediction"]
code: def test_predict(self):
      """...Test LogReg prediction"""
      labels_mappings = [
          {-1: -1.0, 1: 1.0},
          {-1: 1.0, 1: -1.0},
          {-1: 1, 1: 0},
          {-1: 0, 1: 1},
          {-1: "cat", 1: "dog"},
      ]
  
      for labels_mapping in labels_mappings:
          X, y = Test.get_train_data(
              n_features=12, n_samples=300, nnz=0
          )
          y = np.vectorize(labels_mapping.get)(y)
  
          learner = LogisticRegression(random_state=32789, tol=1e-9)
          learner.fit(X, y)
  
          X_test, y_test = Test.get_train_data(
              n_features=12, n_samples=5, nnz=0
          )
          predicted_y = [1.0, 1.0, -1.0, 1.0, 1.0]
          predicted_y = np.vectorize(labels_mapping.get)(predicted_y)
          np.testing.assert_array_equal(
              learner.predict(X_test), predicted_y
          )
  
imports: import itertools
  import unittest
  import numpy
  from sklearn.metrics import roc_auc_score
  from tick.base.inference import InferenceTest
  from tick.linear_model import SimuLogReg, LogisticRegression
  from tick.simulation import weights_sparse_gauss
  from tick.preprocessing.features_binarizer import FeaturesBinarizer
  from tick.prox import ProxZero, ProxL1, ProxL2Sq, ProxElasticNet, ProxTV, ProxBinarsity
label_desc: Use image node_id 16 for calling the Test obj's underlying member method code with example usage: obj.test_predict() without return types