def estimate_training_iterations(n_iterations=10,
learning_rate_range=tuple(
[0.001, 0.01, 0.1, 1.0])):
data = load_higgs_train()
def estimate_error(nn):
error_data = []
for i in range(n_iterations):
nn.train(train_epoch=1)
total_epochs, trn, tst = nn.estimate_error()
error_data.append([nn.learning_rate, total_epochs, trn, tst])
err_df = pd.DataFrame.from_records(error_data,
columns=[
'learning_rate', 'iteration',
'training_error', 'test_error'
])
return err_df
dfs = [estimate_error(NeuralNetwork(data, l)) for l in learning_rate_range]
df = pd.concat(dfs)
df['training_accuracy'] = 1 - df['training_error'] / 100
df['test_accuracy'] = 1 - df['test_error'] / 100
return df
def estimate_best_power():
"""
Run KNN classifier with multiple settings of
power parameter for distance metric
"""
p_range = {1: 'manhattan', 2: 'euclidean', 3: 'minkowski'}
data = load_higgs_train()
records = [[p_range[p]] + list(run_knn(data=data, power_parameter=p))
for p in p_range]
columns = ['metric', 'training_score', 'test_score']
df = pd.DataFrame.from_records(records, columns=columns, index=columns[0])
return df
def estimate_dataset_size():
data = load_higgs_train()
features, weights, labels = data
records = []
for n in range(1, 10):
f = features[:n * len(features)/10]
w = weights[:n * len(weights)/10]
l = labels[:n * len(labels)/10]
records.append([len(f)] + list(run_svm((f, w, l))))
columns = ['sample_size', 'training_score', 'test_score']
df = pd.DataFrame.from_records(records, columns=columns, index=columns[0])
return df
def estimate_best_learning_rate():
"""
Run Ada Boost classifier with multiple settings of
learning_rate and plot the accuracy function of learning rate
:return: the best learning rate setting
"""
learning_rate_range = np.arange(0.2, 2.0, 0.2)
data = load_higgs_train()
records = [[rate] + list(run_AdaBoost(data=data, learning_rate=rate))
for rate in learning_rate_range]
columns = ['learning_rate', 'training_score', 'test_score']
df = pd.DataFrame.from_records(records, columns=columns, index=columns[0])
return df
def estimate_best_n_neighbours():
"""
Run KNN classifier with multiple settings of
n_neighbours and plot the accuracy function of n_neighbours
:return: the best n_neighbours setting
"""
n_neighbours_range = np.arange(1, 26, 2)
data = load_higgs_train()
records = [[n_neighbours] + list(run_knn(data=data, n_neighbours=n_neighbours))
for n_neighbours in n_neighbours_range]
columns = ['n_neighbours', 'training_score', 'test_score']
df = pd.DataFrame.from_records(records, columns=columns, index=columns[0])
return df
def estimate_best_learning_rate():
"""
Run Ada Boost classifier with multiple settings of
learning_rate and plot the accuracy function of learning rate
:return: the best learning rate setting
"""
learning_rate_range = np.arange(0.2, 2.0, 0.2)
data = load_higgs_train()
records = [[rate] + list(run_AdaBoost(data=data, learning_rate=rate))
for rate in learning_rate_range]
columns = ['learning_rate', 'training_score', 'test_score']
df = pd.DataFrame.from_records(records, columns=columns, index=columns[0])
return df
def estimate_best_n_estimators():
"""
Run Ada Boost classifier with multiple settings of
n_estimators and plot the accuracy function of n_estimators
:return: the best n_estimators setting
"""
n_estimators_range = np.arange(30, 120, 5)
data = load_higgs_train()
records = [[n_estimator] + list(run_AdaBoost(data=data, n_estimators=n_estimator))
for n_estimator in n_estimators_range]
columns = ['n_estimators', 'training_score', 'test_score']
df = pd.DataFrame.from_records(records, columns=columns, index=columns[0])
return df
def estimate_best_n_estimators():
"""
Run Ada Boost classifier with multiple settings of
n_estimators and plot the accuracy function of n_estimators
:return: the best n_estimators setting
"""
n_estimators_range = np.arange(30, 120, 5)
data = load_higgs_train()
records = [[n_estimator] +
list(run_AdaBoost(data=data, n_estimators=n_estimator))
for n_estimator in n_estimators_range]
columns = ['n_estimators', 'training_score', 'test_score']
df = pd.DataFrame.from_records(records, columns=columns, index=columns[0])
return df
def estimate_hidden_units(hidden_units_range=xrange(1, 100), sample_size=None):
data = load_higgs_train(sample_size=sample_size)
def estimate_error(nn):
nn.train()
total_epochs, trn, tst = nn.estimate_error()
return [nn.n_hidden_units, total_epochs, trn, tst]
data = [estimate_error(NeuralNetwork(data, n_hidden_units=l)) for l in hidden_units_range]
df = pd.DataFrame.from_records(data, columns=['hidden_units', 'iteration', 'training_error', 'test_error'])
df['training_accuracy'] = 1 - df['training_error'] / 100
df['test_accuracy'] = 1 - df['test_error'] / 100
return df
def estimate_best_min_samples_split():
"""
Run the decision tree classifier with multiple settings of
min_sample_split and plot the accuracy function of min_sample_split
:return: the best min_sample_split setting
"""
min_split_range = xrange(2, 120, 2)
data = load_higgs_train()
records = [[min_sample] + list(run_decision_tree(data=data, criterion='gini', min_samples_split=min_sample))
+ list(run_decision_tree(data=data, criterion='entropy', min_samples_split=min_sample))
for min_sample in min_split_range]
columns = ['min_sample_split', 'gini_training_score', 'gini_test_score',
'entropy_training_score', 'entropy_test_score']
df = pd.DataFrame.from_records(records, columns=columns, index=columns[0])
return df
def estimate_best_c():
"""
Run svm classifier with multiple settings of
C and plot the accuracy function of C
:return: the best C setting
"""
c_range = [10**n for n in range(4)]
data = load_higgs_train()
records = [[c] + list(run_svm(data=data, regularization_term=c))
for c in c_range]
LOGGER.info('Performed evaluation of the C setting choice')
columns = ['C', 'training_score', 'test_score']
df = pd.DataFrame.from_records(records, columns=columns, index=columns[0])
LOGGER.info(df)
return df
def estimate_best_gamma():
"""
Run svm classifier with multiple settings of
gamma and plot the accuracy function of gamma
:return: the best gamma setting
"""
gamma_range = np.arange(0.0, 1.0, 0.2)
data = load_higgs_train()
records = [[gamma] + list(run_svm(data=data, gamma=gamma))
for gamma in gamma_range]
LOGGER.info('Performed evaluation of the gamma setting choice')
columns = ['gamma', 'training_score', 'test_score']
df = pd.DataFrame.from_records(records, columns=columns, index=columns[0])
LOGGER.info(df)
return df
def estimate_training_iterations(n_iterations=10, learning_rate_range=tuple([0.001, 0.01, 0.1, 1.0])):
data = load_higgs_train()
def estimate_error(nn):
error_data = []
for i in range(n_iterations):
nn.train(train_epoch=1)
total_epochs, trn, tst = nn.estimate_error()
error_data.append([nn.learning_rate, total_epochs, trn, tst])
err_df = pd.DataFrame.from_records(error_data, columns=['learning_rate', 'iteration', 'training_error', 'test_error'])
return err_df
dfs = [estimate_error(NeuralNetwork(data, l)) for l in learning_rate_range]
df = pd.concat(dfs)
df['training_accuracy'] = 1 - df['training_error'] / 100
df['test_accuracy'] = 1 - df['test_error'] / 100
return df
def estimate_hidden_units(hidden_units_range=xrange(1, 100), sample_size=None):
data = load_higgs_train(sample_size=sample_size)
def estimate_error(nn):
nn.train()
total_epochs, trn, tst = nn.estimate_error()
return [nn.n_hidden_units, total_epochs, trn, tst]
data = [
estimate_error(NeuralNetwork(data, n_hidden_units=l))
for l in hidden_units_range
]
df = pd.DataFrame.from_records(
data,
columns=['hidden_units', 'iteration', 'training_error', 'test_error'])
df['training_accuracy'] = 1 - df['training_error'] / 100
df['test_accuracy'] = 1 - df['test_error'] / 100
return df
