class_docstr: Represents datasets we use for expriments
docstr: protein dataset
docstr_tok: ["protein","dataset"]
code: def protein_dataset(self):
      """protein dataset"""
      path_protein = PATH_PREFIX + "/data/protein/"
      if not os.path.exists(path_protein):
          os.mkdir(path_protein)
          protein_url = "https://kdd.org/cupfiles/KDDCupData/2004/data_kddcup04.tar.gz"
          protein_file = (
              PATH_PREFIX + "/data/protein/data_kddcup04.tar.gz"
          )
          response = requests.get(protein_url, stream=True, timeout=100)
          if response.status_code == 200:
              with open(protein_file, "wb") as file_data:
                  file_data.write(response.raw.read())
          with tarfile.open(protein_file, "r:gz") as tar:
              tar.extractall(path_protein)
      x_train = np.loadtxt(PATH_PREFIX + "/data/protein/bio_train.dat")[
          :, 3:
      ]
      y_train = np.loadtxt(PATH_PREFIX + "/data/protein/bio_train.dat")[
          :, 2
      ]
      indx0 = np.nonzero(y_train == 0)[0]
      indx1 = np.nonzero(y_train == 1)[0]
      labels = y_train.copy()
      labels[indx0] = -1
      labels[indx1] = 1
      indx = np.arange(len(x_train))
      np.random.seed(3000)
      indx_sample = np.random.choice(indx, 50000, replace=False)
      np.random.seed(None)
      x_train = x_train[indx_sample]
      labels = labels[indx_sample]
      x_train = normalize_fvec(x_train)
      w_opt = self.find_optimal_classifier((x_train, labels))
      x_train = np.hstack(
          (np.ones(shape=(np.shape(x_train)[0], 1)), x_train)
      )
      return x_train, labels, w_opt
  
imports: import os
  import ssl
  import tarfile
  import urllib.request
  from my_logistic_regression import MyLogisticRegression
  import numpy
  import requests
  from sklearn import preprocessing
  import sklearn.datasets
  from sklearn.linear_model import LogisticRegression
  import torch
  from torchvision import datasets, transforms
label_desc: Use image node_id 5 for calling the Mydatasets obj's underlying member method code with example usage: obj.protein_dataset() and returns: x_train,  labels,  w_opt