def train_test_50(self):
global features, feature_names, target, target_names
labels = target_names[target]
is_setosa = labels == "setosa"
features_wo_setosa = features[~is_setosa]
labels_wo_setosa = labels[~is_setosa]
is_virginica = (labels_wo_setosa == "virginica")
# split the features in train and test
training = np.tile([False, True], 50)
testing = ~training
# display the resulting accuracy for both series
model_training = getModel(features_wo_setosa[training], is_virginica[training], feature_names)
accuracy_training = accuracy(model_training, features_wo_setosa[training], is_virginica[training])
accuracy_testing = accuracy(model_training, features_wo_setosa[testing], is_virginica[testing])
logging.info("Training Accuracy : %f ----Testing Accuracy = %f ", accuracy_training, accuracy_testing)
from main.ch02.utils import getModel, accuracy
data = load_iris()
features = data['data']
labels = data['target_names'][data['target']]
# We are going to remove the setosa examples as they are too easy:
is_setosa = (labels == 'setosa')
features = features[~is_setosa]
labels = labels[~is_setosa]
# Now we classify virginica vs non-virginica
is_virginica = (labels == 'virginica')
# Split the data in two: testing and training
testing = np.tile([True, False], 50) # testing = [True,False,True,False,True,False...]
# Training is the negation of testing: i.e., datapoints not used for testing,
# will be used for training
training = ~testing
model = getModel(features[training], is_virginica[training])
train_accuracy = accuracy(features[training], is_virginica[training], model)
test_accuracy = accuracy(features[testing], is_virginica[testing], model)
print('''\
Training accuracy was {0:.1%}.
Testing accuracy was {1:.1%} (N = {2}).
'''.format(train_accuracy, test_accuracy, testing.sum()))
