Project: s41467-024-55291-x | Function: TextClassification.train (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Ensemble CNN training for nuclear medicine text classification

**KEY TERMS:** {
  "Theranostic Genome": "Thereby, we describe the *Theranostic Genome* , the part of the human genome whose expression can be utilized for combined therapeutic and diagnostic applications",
  "Theranostic": "Theranostic drugs represent a new paradigm for precision oncology ^8",
  "Thereby": "Thereby, we describe the *Theranostic Genome* , the part of the human genome whose expression can be utilized for combined therapeutic and diagnostic applications"
}

**CONTEXT CODE:**         reference “Fawcett, Tom. (2004). ROC Graphs: Notes and Practical Considerations for Researchers. Machine
        Learning. 31. 1-38.”, having a computational cost of :math:`O(N \\log N)` with :math:`N` being the test sample
        size

        :param texts: texts used for training metrics computation
        :type texts: list(str)
        :param cats: categories corresponding to the input texts used as gold standard to compute training metrics
        :type cats: list(str)

        :return: ROC AUC value
        :rtype: float
        """

        scores = [self.textcat(self.model.tokenizer(text)).cats["POSITIVE_NUCL_MED"] for text in texts]
        sort_idx = list(reversed([i[0] for i in sorted(enumerate(scores), key=lambda x: x[1])]))

        n_pos = 0
        n_neg = 0
        n_neg_eq = 0
        n_pos_eq = 0
        n_pos_gr = 0
        auc = 0
        for i, j in enumerate(sort_idx):

            if i != 0 and scores[j] == scores[sort_idx[i-1]]:
                if cats[sort_idx[i-1]]["cats"]["POSITIVE_NUCL_MED"] == 1.0 and cats[sort_idx[i-1]]["cats"]["NEGATIVE_NUCL_MED"] == 0.0:
                    n_pos_eq += 1
                elif cats[sort_idx[i-1]]["cats"]["POSITIVE_NUCL_MED"] == 0.0 and cats[sort_idx[i-1]]["cats"]["NEGATIVE_NUCL_MED"] == 1.0:
                    n_neg_eq += 1
            else:
                n_neg_eq = 0
                n_pos_eq = 0

            if cats[j]["cats"]["POSITIVE_NUCL_MED"] == 1.0 and cats[j]["cats"]["NEGATIVE_NUCL_MED"] == 0.0:
                n_pos_eq += 1
                n_pos += 1
            elif cats[j]["cats"]["POSITIVE_NUCL_MED"] == 0.0 and cats[j]["cats"]["NEGATIVE_NUCL_MED"] == 1.0:
                n_neg_eq += 1
                n_neg += 1

            auc += n_neg_eq*n_pos_gr + (n_pos_eq*n_neg_eq)/2.0

            if cats[j]["cats"]["POSITIVE_NUCL_MED"] == 1.0 and cats[j]["cats"]["NEGATIVE_NUCL_MED"] == 0.0:
                n_pos_gr += 1

        auc /= (n_pos*n_neg)

        return auc

    @timer('Training TC model...\n')
    def train(self, add_pipe=True, n_iter=10):
        """
        Trains spaCy's TextCategorizer model

        :param add_pipe: adds new "textcat" pipe to the loaded spaCy model if `True`, defaults to `True`
        :type add_pipe: bool, optional
        :param n_iter: number of iterations for the TextCategorizer model training, defaults to 10
        :type n_iter: int, optional
        """
        raise NotImplementedError("Function implementation has been masked for TextClassifier.train")

    @timer('Saving TC model to disk... ')
    def savemodel(self, output_path='texcat_model'):
        """
        Saves the text classifying model

        :param output_path: path to the directory that will contain the text classifying model files, defaults to "textcat_model"
        :type output_path: str, optional
        """
        self.output_path = os.path.abspath(output_path)
        self.model.to_disk(self.output_path)

Complete the function implementation replacing "raise NotImplementedError".