text: import numpy as np
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.svm import SVC, SVR, NuSVR, LinearSVR
  
  from sklearn.metrics import roc_auc_score
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn import preprocessing
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.ensemble import RandomForestClassifier
  
  train = pd.read_csv('./input/jigsaw-toxic-comment-classification-challenge/train_preprocessed.csv')
  test = pd.read_csv('./input/jigsaw-toxic-comment-classification-challenge/test_preprocessed.csv')
  submission = pd.read_csv('./input/jigsaw-toxic-comment-classification-challenge/sample_submission.csv')
  test_label = pd.read_csv('./input/jigsaw-toxic-comment-classification-challenge/test_labels.csv')
  
  X_train = train["comment_text"].fillna("fillna").values
  list_classes = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]
  y_train = train[list_classes].values
  # X_test = test["comment_text"].fillna("fillna").values
  
  X_features = np.load("./model/feature_matrix.npy")
  
  X_features = np.array(X_features, dtype=np.float)
  
  X_features = preprocessing.scale(X_features)
  
  # min_max_scaler = preprocessing.MinMaxScaler()
  # X_features= min_max_scaler.fit_transform(X_features)
  
  y_train = np.array(y_train, dtype=np.int)
  
  # np.savetxt("1.txt",y_train)
  
  # y_train = MultiLabelBinarizer().fit_transform(y_train)
  
  # np.savetxt("2.txt",y_train)
  
  X_tra, X_test, y_tra, y_test = train_test_split(X_features, y_train, train_size=0.95, random_state=233)
  
  classifier = SVC(gamma='auto', kernel='rbf', max_iter=300)
  # cls = DecisionTreeClassifier()
  classifier = OneVsRestClassifier(classifier)
  
  # train
  classifier.fit(X_tra, y_tra)
  
  # predict
  predictions = classifier.predict(X_test)
  score=roc_auc_score(y_test,predictions)
  
  print("\n ROC-AUC - score: %.6f \n" % score)
  