class_docstr: Base Naive Bayes class.
class_docstr_tok: ["Base","Naive","Bayes","class","."]
docstr: Return probabilities using the log-likelihoods in mini-batchs setting.
docstr_tok: ["Return","probabilities","using","the","log-likelihoods","in","mini-batchs","setting","."]
code: def predict_proba_many(self, X: pd.DataFrame) -> pd.DataFrame:
      """Return probabilities using the log-likelihoods in mini-batchs setting."""
      jll = self.joint_log_likelihood_many(X)
      if jll.empty:
          return jll
      lse = pd.Series(special.logsumexp(jll, axis=1))
      return np.exp(jll.subtract(lse.values, axis="rows"))
  
file_path: river/river/naive_bayes/base.py
imports: from __future__ import annotations
  import abc
  import math
  import numpy
  import pandas
  from scipy import sparse, special
  from river import base
label_desc: Use image node_id 4 for calling the BaseNB obj's underlying member method code with example usage: obj.predict_proba_many(X) and returns: np, jll