old_contents: #!/usr/bin/env python
  
  import numpy as np
  from sklearn import linear_model
  from sklearn import tree
  from sklearn import svm
  from sklearn import metrics
  from sklearn.neural_network import MLPClassifier
  
  FEATURE_NUMBER = 9
  
  # Read train and test data
  with open("../data/cancer.csv", "r") as f:
      train_dataset = np.loadtxt(f, delimiter=",")
      labels = train_dataset[:, FEATURE_NUMBER]
      features = train_dataset[:, 0:FEATURE_NUMBER]
  
  with open("../data/cancer_test.csv", "r") as f:
      test_dataset = np.loadtxt(f, delimiter=",")
      test_labels = test_dataset[:, FEATURE_NUMBER]
      test_features = test_dataset[:, 0:FEATURE_NUMBER]
  
  # Define the model
  #classifier = tree.DecisionTreeClassifier()
  #classifier = svm.SVC(C=1, kernel='linear')
  classifier = MLPClassifier(algorithm='sgd', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1, learning_rate_init=0.001, batch_size=64, max_iter=100, verbose=True)
  #classifier = MLPClassifier(algorithm='l-bfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)
  #classifier = MLPClassifier(algorithm='adam', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)
  
  # Train the model
  print("Start to train")
  model = classifier.fit(features, labels)
  print("Start to validate")
  predict_labels = model.predict(test_features)
  #fpr, tpr, thresholds = metrics.roc_curve(test_labels, predict_labels)
  auc = metrics.roc_auc_score(test_labels, predict_labels)
  accuracy = metrics.accuracy_score(test_labels, predict_labels)
  
  # Print the metrics
  print("Accuracy: {}, acu: {}".format(accuracy, auc))
  
  
new_contents: #!/usr/bin/env python
  
  import numpy as np
  from sklearn import linear_model
  from sklearn import tree
  from sklearn import svm
  from sklearn import metrics
  from sklearn.neural_network import MLPClassifier
  
  FEATURE_NUMBER = 9
  
  # Read train and test data
  with open("../data/cancer_train.csv", "r") as f:
    train_dataset = np.loadtxt(f, delimiter=",")
    train_labels = train_dataset[:, FEATURE_NUMBER]
    train_features = train_dataset[:, 0:FEATURE_NUMBER]
  
  with open("../data/cancer_test.csv", "r") as f:
    test_dataset = np.loadtxt(f, delimiter=",")
    test_labels = test_dataset[:, FEATURE_NUMBER]
    test_features = test_dataset[:, 0:FEATURE_NUMBER]
  
  # Define the model
  classifier = tree.DecisionTreeClassifier()
  #classifier = svm.SVC(C=1, kernel='linear')
  #classifier = MLPClassifier(algorithm='sgd', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1, learning_rate_init=0.001, batch_size=64, max_iter=100, verbose=False)
  #classifier = MLPClassifier(algorithm='l-bfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)
  #classifier = MLPClassifier(algorithm='adam', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)
  
  # Train the model
  print("Start to train")
  model = classifier.fit(train_features, train_labels)
  
  print("Start to validate")
  predict_labels = model.predict(test_features)
  auc = metrics.roc_auc_score(test_labels, predict_labels)
  accuracy = metrics.accuracy_score(test_labels, predict_labels)
  
  # Print the metrics
  print("Accuracy: {}, acu: {}".format(accuracy, auc))
  
subject: Format the scikit learn example