class_docstr: Intent classifier which uses a Logistic Regression underneath
class_docstr_tok: ["Intent","classifier","which","uses","a","Logistic","Regression","underneath"]
code: def _predict_proba(self, X):  # pylint: disable=C0103
      import numpy as np
  
      self.classifier._check_proba()  # pylint: disable=W0212
  
      prob = self.classifier.decision_function(X)
      prob *= -1
      np.exp(prob, prob)
      prob += 1
      np.reciprocal(prob, prob)
      if prob.ndim == 1:
          return np.vstack([1 - prob, prob]).T
      return prob
  
imports: from __future__ import unicode_literals
  import json
  import logging
  from builtins import range, str, zip
  from pathlib import Path
  from snips_nlu.common.log_utils import DifferedLoggingMessage, log_elapsed_time
  from snips_nlu.common.utils import check_persisted_path, fitted_required, json_string
  from snips_nlu.constants import LANGUAGE, RES_PROBA
  from snips_nlu.dataset import validate_and_format_dataset
  from snips_nlu.exceptions import LoadingError, _EmptyDatasetUtterancesError
  from snips_nlu.intent_classifier.featurizer import Featurizer
  from snips_nlu.intent_classifier.intent_classifier import IntentClassifier
  from snips_nlu.intent_classifier.log_reg_classifier_utils import build_training_data, get_regularization_factor, text_to_utterance
  from snips_nlu.pipeline.configs import LogRegIntentClassifierConfig
  from snips_nlu.result import intent_classification_result
label_desc: Use image node_id 7 for calling the LogRegIntentClassifier obj's underlying member method code with example usage: obj._predict_proba(X) and returns: prob, np