Python accuracy示例

示例#1

# Part 1 - Perceptron Algorithm
#-------------------------------------------------------------------------------

toy_features, toy_labels = utils.load_toy_data('../../Data/toy_data.csv')

theta, theta_0 = lab2.perceptron(toy_features, toy_labels, T=5)

utils.plot_toy_results(toy_features, toy_labels, theta, theta_0)

#-------------------------------------------------------------------------------
# Part 2 - Classifying Reviews
#-------------------------------------------------------------------------------

theta, theta_0 = lab2.perceptron(train_bow_features, train_labels, T=5)

train_accuracy = lab2.accuracy(train_bow_features, train_labels, theta,
                               theta_0)
val_accuracy = lab2.accuracy(val_bow_features, val_labels, theta, theta_0)

print("Training accuracy: {:.4f}".format(train_accuracy))
print("Validation accuracy: {:.4f}".format(val_accuracy))

#-------------------------------------------------------------------------------
# Part 3 - Improving the Model
#-------------------------------------------------------------------------------

#-------------------------------------------------------------------------------
# Part 3.1 - Tuning the Hyperparameters
#-------------------------------------------------------------------------------

# Ts = [1, 5, 10, 15, 20]

示例#2

文件： main.py 项目： wesenu/IntroML

# Part 1 - Perceptron Algorithm
#-------------------------------------------------------------------------------

toy_features, toy_labels = utils.load_toy_data('../../Data/toy_data.csv')

theta, theta_0 = lab2.perceptron(toy_features, toy_labels, T=5)

utils.plot_toy_results(toy_features, toy_labels, theta, theta_0)

#-------------------------------------------------------------------------------
# Part 2 - Classifying Reviews
#-------------------------------------------------------------------------------

theta, theta_0 = lab2.perceptron(train_bow_features, train_labels, T=5)

train_accuracy = lab2.accuracy(train_bow_features, train_labels, theta,
                               theta_0)
val_accuracy = lab2.accuracy(val_bow_features, val_labels, theta, theta_0)

print("Training accuracy: {:.4f}".format(train_accuracy))  # 0.9850
print("Validation accuracy: {:.4f}".format(val_accuracy))  # 0.8943

#-------------------------------------------------------------------------------
# Part 3 - Improving the Model
#-------------------------------------------------------------------------------

#-------------------------------------------------------------------------------
# Part 3.1 - Tuning the Hyperparameters
#-------------------------------------------------------------------------------

Ts = [1, 5, 10, 15, 20]

示例#3

文件： main.py 项目： BimuratMukhtar/IntroML

# Part 1 - Perceptron Algorithm
#-------------------------------------------------------------------------------

# toy_features, toy_labels = utils.load_toy_data('../../Data/toy_data.csv')
#
# theta, theta_0 = lab2.perceptron(toy_features, toy_labels, T=5)
#
# utils.plot_toy_results(toy_features, toy_labels, theta, theta_0)

#-------------------------------------------------------------------------------
# Part 2 - Classifying Reviews
#-------------------------------------------------------------------------------

theta, theta_0 = lab2.perceptron(train_bow_features, train_labels, T=5)

train_accuracy = lab2.accuracy(train_bow_features, train_labels, theta, theta_0)
val_accuracy = lab2.accuracy(val_bow_features, val_labels, theta, theta_0)
#
# print("Training accuracy: {:.4f}".format(train_accuracy))
# print("Validation accuracy: {:.4f}".format(val_accuracy))

#-------------------------------------------------------------------------------
# Part 3 - Improving the Model
#-------------------------------------------------------------------------------

#-------------------------------------------------------------------------------
# Part 3.1 - Tuning the Hyperparameters
#-------------------------------------------------------------------------------

# Ts = [1, 5, 10, 15, 20]