LogisticRegression(random_state=0, solver='lbfgs', multi_class='auto'),
),
]:
print("\n", "=" * len(name), "\n", name, '\n', "=" * len(name))
np.random.seed(seed=0)
clf_copy = copy.deepcopy(clf)
# Compute p(y=k), the ground truth class prior on the labels.
py = np.bincount(y_train) / float(len(y_train))
# Generate the noisy channel to characterize the label errors.
noise_matrix = generate_noise_matrix_from_trace(
K=num_classes,
trace=num_classes * avg_trace,
py=py,
frac_zero_noise_rates=frac_zero_noise_rates,
)
print_noise_matrix(noise_matrix)
# Create the noisy labels. This method is exact w.r.t. the noise_matrix.
y_train_with_errors = generate_noisy_labels(y_train, noise_matrix)
lnl_cv = GridSearch(
model=LearningWithNoisyLabels(clf),
param_grid=param_grid,
num_threads=4,
seed=0,
)
lnl_cv.fit(
X_train=X_train,
y_train=y_train_with_errors,
X_val=X_val,
y_val=y_val,
verbose=False,
)
linewidth=1)
_ = plt.scatter(
X_train[~idx_errors][:, 0][s[~idx_errors] != y_train[~idx_errors]],
X_train[~idx_errors][:, 1][s[~idx_errors] != y_train[~idx_errors]],
s=400,
facecolors='none',
edgecolors='black',
linewidth=2,
alpha=0.5)
_ = plt.title('Dataset after pruning detected label errors.', fontsize=30)
plt.show()
except:
print("Plotting is only supported in an iPython interface.")
print('The actual, latent, underlying noise matrix.')
print_noise_matrix(noise_matrix)
print('Our estimate of the noise matrix.')
print_noise_matrix(est_noise_matrix)
print()
print('The actual, latent, underlying joint distribution matrix.')
cleanlab.util.print_joint_matrix(true_joint_distribution_of_label_errors)
print('Our estimate of the joint distribution matrix.')
cleanlab.util.print_joint_matrix(est_joint)
print("Accuracy Comparison")
print("-------------------")
clf = LogisticRegression(solver='lbfgs', multi_class='auto')
baseline_score = accuracy_score(y_test, clf.fit(X_train, s).predict(X_test))
print("Logistic regression:", baseline_score)
rp = LearningWithNoisyLabels(seed=seed)
rp_score = accuracy_score(y_test, rp.fit(X_train, s, psx=psx).predict(X_test))
print("Logistic regression (+rankpruning):", rp_score)
def test_print_noise_matrix():
for m in [noise_matrix, noise_matrix_2, single_element]:
util.print_noise_matrix(noise_matrix)
assert (True)
# In[17]:
# Create noisy labels for both CIFAR-10 and CIFAR-100
# Store dictionary as json
import numpy as np
import pickle
from cleanlab import util
for cifar_dataset in ["cifar10"]: #, "cifar100"]:
data_path = '/datasets/datasets/{}/{}/'.format(cifar_dataset,
cifar_dataset)
for noise_amount in np.arange(0.2, 0.61, 0.2):
for frac_zero_noise_rates in np.arange(0, 0.61, 0.2):
# Print the noise matrix
rfn_base = '{}_noisy_labels__frac_zero_noise_rates__{}__noise_amount__{}'.format(
cifar_dataset,
"0.0" if frac_zero_noise_rates < 1e-4 else round(
frac_zero_noise_rates, 1),
"0.0" if noise_amount < 1e-4 else round(noise_amount, 1),
)
rfn = data_path + "noisy_labels/" + rfn_base
rfn_base = "{}_noise_matrix".format(
cifar_dataset) + "__" + "__".join(rfn_base.split("__")[1:])
rfn = data_path + "noisy_labels/" + rfn_base
with open(rfn + ".pickle", 'rb') as rf:
nm = pickle.load(rf)
actual_noise = 0.7 if abs(noise_amount -
0.6) < 1e-3 else noise_amount
print('Noise amount:', round(actual_noise, 3), "| Sparsity:",
round(frac_zero_noise_rates, 3))
util.print_noise_matrix(nm)
