Project: s41467-024-47661-2 | Function: 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:** mAFiA logistic regression classifier training algorithm implementing the core machine learning pipeline for m6A modification detection using balanced sampling, cross-entropy loss optimization, and precision-recall curve analysis

**KEY TERMS:** {
  "binary_model": "logistic regression classifier implementing P(m6A) = σ(wi·x + bi)",
  "unm_nts": "unmodified nucleotide training samples (label = 0)",
  "mod_nts": "m6A-modified nucleotide training samples (label = 1)",
  "x_train": "768-dimensional feature vectors (x) from RODAN convolution layer",
  "y_train": "binary modification labels (yn)",
  "decision_function": "linear combination wi·x + bi before sigmoid activation",
  "precision_recall_curve": "performance evaluation for binary classification"
}

**CONTEXT CODE:** import os
from glob import glob
import numpy as np
import random
from random import sample
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler, MaxAbsScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_recall_curve, auc
import pickle


class MotifClassifier:
    def __init__(self, motif, classifier_type, scaler):
        self.motif = motif
        self.classifier_type = classifier_type
        self.scaler = scaler
        self.train_nts = []
        self.test_nts = []
        self.precision = []
        self.recall = []
        self.thresholds = []
        self.auc = -1

        if classifier_type == 'svm':
            clf = SVC(gamma='auto', random_state=0, max_iter=1000)
        elif classifier_type == 'logistic_regression':
            clf = LogisticRegression(random_state=0, max_iter=1000)
        else:
            clf = None

        if scaler == 'MaxAbs':
            self.binary_model = make_pipeline(MaxAbsScaler(), clf)
        elif scaler == 'Standard':
            self.binary_model = make_pipeline(StandardScaler(), clf)
        else:
            self.binary_model = clf

    def train(self, unm_nts, mod_nts, frac_test_split=0.25):
        """mAFiA logistic regression classifier training algorithm implementing the core machine learning pipeline for m6A modification detection using balanced sampling, cross-entropy loss optimization, and precision-recall curve analysis

        :param unm_nts: unmodified nucleotide training samples (label = 0)
        :param mod_nts: m6A-modified nucleotide training samples (label = 1)
        :param frac_test_split: fraction of data for test split
        """
        # MASKED: mAFiA logistic regression classifier training algorithm implementing the core machine learning pipeline for m6A modification detection using balanced sampling, cross-entropy loss optimization, and precision-recall curve analysis
        raise NotImplementedError("This function is masked for testing purposes.")

    def test(self, test_nts, mod_thresh=0.5):
        print(f'Testing {len(test_nts)} NTs...')
        test_features = [nt.feature for nt in test_nts]
        mod_probs = self.binary_model.predict_proba(test_features)[:, 1]
        for this_nt, this_mod_prob in zip(test_nts, mod_probs):
            this_nt.mod_prob = this_mod_prob
        predictions = np.int32(mod_probs > mod_thresh)
        avg_mod_ratio = np.mean(predictions)
        print(f'Predicted mod. ratio {avg_mod_ratio:.2f}')
        return avg_mod_ratio

    def save(self, out_model_path, draw_prc=False):
        with open(out_model_path, 'wb') as h_out:
            pickle.dump(self, h_out, pickle.HIGHEST_PROTOCOL)

        # if draw_prc:
        #     out_img_path = out_model_path.replace('.pkl', '.png')
        #     plt.figure(figsize=(5, 5))
        #     plt.plot(self.recall, self.precision, '-')
        #     plt.xlabel('Recall')
        #     plt.ylabel('Precision')
        #     plt.ylim([0, 1.05])
        #     plt.title(f'{self.motif}\n{len(self.train_nts)} train NTs, {len(self.test_nts)} test NTs\n AUC = {self.auc:.2f}')
        #     plt.savefig(out_img_path, bbox_inches='tight')
        #     plt.close('all')


def load_motif_classifiers(classifier_dir):
    print('Loading motif classifiers...')
    classifier_paths = glob(os.path.join(classifier_dir, '*.pkl'))
    motif_classifiers = {}
    for this_path in classifier_paths:
        with open(this_path, 'rb') as h_in:
            this_motif_classifier = pickle.load(h_in)
        motif_classifiers[this_motif_classifier.motif] = this_motif_classifier
    classifier_motifs = list(motif_classifiers.keys())
    print('Target motifs: ', ', '.join(classifier_motifs))

    return motif_classifiers


Complete the function implementation replacing "raise NotImplementedError".