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base_simulations_ntrain.py
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from sklearn.model_selection import train_test_split
from general_utilities import *
from sota_utilities import *
# to transform the data
from MRCpy.phi import \
BasePhi, \
RandomFourierPhi, \
RandomReLUPhi, \
ThresholdPhi
# to use cleanlab
from sklearn.linear_model import LogisticRegression
from cleanlab.classification import CleanLearning
from cleanlab.count import estimate_noise_matrices
def simulation_ntrain_mrc(X, Y, nvector, nrep, det, lambda0, phi='fourier', loss='0-1', rho1=0.2, rho2=0.2):
"""
Simulation for different training size for MRCs models.
Parameters
----------
X : `array`-like of shape (`n_samples` * `n_features`)
matrix representing the data (features)
Y : `array`-like of shape (`n_samples` * 1)
matrix representing the labels
nvector : `array`-like of shape (`number_training_size` * 1)
vector representing different training size
nrep : `int`
number of repetitions for the simulations
det : `bool`
True if we want to use the deterministic rule to predict
False if we want to use the probabilistic rule to predict
lambda0 : `float`
hyperparameter of MRC that controls the size of the set U:
lambda = lambda0 * std/ sqrt(ntrain)
Typically: lambda0 = 0.3, 0.7, 1
phi : `str` or `BasePhi` instance, default = 'fourier'
Type of feature mapping function to use for mapping the input data.
The currently available feature mapping methods are
'fourier', 'relu', 'threshold' and 'linear'.
loss : `str` {'0-1', 'log'}, default = '0-1'
Type of loss function to use for the risk minimization.
rho1 : `float`, default = 0.2
Noise rate on class 1
:math:` \\mathbb{P}(ytilde = 2 | ytrue = 1)`
rho2 : `float`, default = 0.2
Noise rate on class 2
:math:` \\mathbb{P}(ytilde = 1 | ytrue = 2)`
Returns
-------
summary_data: Pandas Dataframe
summary of all the simulation's setting
(loss, feat. mapp, rhos)
summary_results: Pandas Dataframe
summary of the main results
(errors of the methods, times and eventually bounds)
results_models: Pandas Dataframe
containing the detailed information regarding each modela of each repetition and training size
"""
n_samples, n_features = X.shape
results_models = pd.DataFrame(
{'rep': [],
'ntrain': [],
'mrc_clean': [],
'mrc_nocorr': [],
'mrc_back': []
}
)
T = np.array([[1 - rho1, rho2], [rho1, 1 - rho2]])
# corrupt the data with fixed seed
Ytilda = corruption(Y, T)
my_shape = (nrep, len(nvector))
tot_time1, tot_time2, tot_time3 = [np.zeros(my_shape) for _ in range(3)]
tot_err1, tot_err2, tot_err3 = [np.zeros(my_shape) for _ in range(3)]
tot_bd1, tot_bd2, tot_bd3 = [np.zeros(my_shape) for _ in range(3)]
for rep in range(nrep):
seed = rep
print("Rep:", rep)
# split data into training and test sets:
X_train, X_test, y_tilda_train, y_tilda_test, \
train_indx, test_indx = train_test_split(X, Ytilda, range(n_samples), test_size=0.2, random_state=seed,
stratify=Ytilda)
# retrieve the clean test and training set:
y_train = Y[train_indx]
y_test = Y[test_indx]
for i, ntrain in enumerate(nvector):
X_tr = X_train[0:ntrain, :]
y_tr = y_train[0:ntrain]
y_ti = y_tilda_train[0:ntrain]
mrc1 = my_mrc(phi, loss, det, lambda0, seed, 'classic', X_tr, y_tr, X_test, y_test)
mrc2 = my_mrc(phi, loss, det, lambda0, seed, 'classic', X_tr, y_ti, X_test, y_test)
mrc3 = my_mrc(phi, loss, det, lambda0, seed, 'backward', X_tr, y_ti, X_test, y_test, T, projection=True)
tot_time1[rep, i] = mrc1.time
tot_time2[rep, i] = mrc2.time
tot_time3[rep, i] = mrc3.time
tot_bd1[rep, i] = mrc1.upper_
tot_bd2[rep, i] = mrc2.upper_
tot_bd3[rep, i] = mrc3.upper_
tot_err1[rep, i] = mrc1.error
tot_err2[rep, i] = mrc2.error
tot_err3[rep, i] = mrc3.error
results_models = results_models._append(
{'rep': rep,
'ntrain': ntrain,
'mrc_clean': {'model': mrc1,
'noise_matrix_used_in_learning': None, 'noise_matrix_used_in_corruption': None,
'predicted_labels': [mrc1.y_pred], 'true_labels': [y_test]},
'mrc_nocorr': {'model': mrc2,
'noise_matrix_used_in_learning': None, 'noise_matrix_used_in_corruption': T,
'predicted_labels': [mrc2.y_pred], 'true_labels': [y_test]},
'mrc_back': {'model': mrc3,
'noise_matrix_used_in_learning': T, 'noise_matrix_used_in_corruption': T,
'predicted_labels': [mrc3.y_pred], 'true_labels': [y_test]}
}, ignore_index=True
)
summary_data = pd.DataFrame(
data={
'n_samples': '%d' % n_samples,
'n_attributes': '%d' % n_features,
'phi': phi,
'loss': loss,
'deterministic_rule': det,
'lambda0': lambda0,
'rho1': '%f' % rho1,
'rho2': '%f' % rho2,
'noise_matrix': [T],
'ntrain_vector': [nvector],
}
)
summary_results = pd.DataFrame(
{'mrc_clean': {'errors': tot_err1, 'bounds': tot_bd1, 'times': tot_time1},
'mrc_nocorr': {'errors': tot_err2, 'bounds': tot_bd2, 'times': tot_time2},
'mrc_back': {'errors': tot_err3, 'bounds': tot_bd3, 'times': tot_time3},
}
)
return summary_data, summary_results, results_models
def simulation_ntrain_lr(X, Y, nvector, nrep, phi='fourier', rho1=0.2, rho2=0.2, **phi_kwargs):
"""
Simulation for different training size for Logistic regression models.
Parameters
----------
X : `array`-like of shape (`n_samples` * `n_features`)
matrix representing the data (features)
Y : `array`-like of shape (`n_samples` * 1)
matrix representing the labels
nvector : `array`-like of shape (`number_training_size` * 1)
vector representing different training size
nrep : `int`
number of repetitions for the simulations
phi : `str` or `BasePhi` instance, default = 'fourier'
Type of feature mapping function to use for mapping the input data.
The currently available feature mapping methods are
'fourier', 'relu', 'threshold' and 'linear'.
rho1 : `float`, default = 0.2
Noise rate on class 1
:math:` \\mathbb{P}(ytilde = 2 | ytrue = 1)`
rho2 : `float`, default = 0.2
Noise rate on class 2
:math:` \\mathbb{P}(ytilde = 1 | ytrue = 2)`
Returns
-------
results : all the important variables
"""
n_samples, n_features = X.shape
classes_ = np.unique(Y)
n_classes = len(classes_)
# TRASFORMA I DATI CON FEAT. MAPPING
# Feature mappings
fit_intercept = False
random_state = False
if phi == 'fourier':
phi = RandomFourierPhi(n_classes=n_classes,
fit_intercept=fit_intercept,
random_state=random_state,
**phi_kwargs)
elif phi == 'linear':
phi = BasePhi(n_classes=n_classes,
fit_intercept=fit_intercept,
**phi_kwargs)
elif phi == 'threshold':
phi = ThresholdPhi(n_classes=n_classes,
fit_intercept=fit_intercept,
**phi_kwargs)
elif phi == 'relu':
phi = RandomReLUPhi(n_classes=n_classes,
fit_intercept=fit_intercept,
random_state=random_state,
**phi_kwargs)
elif not isinstance(phi, BasePhi):
raise ValueError('Unexpected feature mapping type ... ')
# Fit the feature mappings
phi.fit(X, Y)
X_feat = phi.transform(X)
results_models = pd.DataFrame(
{'rep': [],
'ntrain': [],
'lr_clean': [],
'lr_nocorr': [],
}
)
T = np.array([[1 - rho1, rho2], [rho1, 1 - rho2]])
Ytilda = corruption(Y, T)
my_shape = (nrep, len(nvector))
tot_time1, tot_time2 = [np.zeros(my_shape) for _ in range(2)]
tot_err1, tot_err2 = [np.zeros(my_shape) for _ in range(2)]
for rep in range(nrep):
seed = rep
print("Rep:", rep)
# split data into training and test sets:
X_train, X_test, y_tilda_train, y_tilda_test, \
train_indx, test_indx = train_test_split(X_feat, Ytilda, range(n_samples), test_size=0.2, random_state=seed,
stratify=Ytilda)
# retrieve the clean test and training set:
y_train = Y[train_indx]
y_test = Y[test_indx]
for i, ntrain in enumerate(nvector):
print("training size: ", ntrain)
X_tr = X_train[0:ntrain, :]
y_tr = y_train[0:ntrain]
y_ti = y_tilda_train[0:ntrain]
# LR on clean labels
lr1 = my_lr(X_tr, y_tr, X_test, y_test, max_iter=300)
# LR on noisy labels without correction
lr2 = my_lr(X_tr, y_ti, X_test, y_test, max_iter=300)
tot_time1[rep, i] = lr1.time
tot_time2[rep, i] = lr2.time
tot_err1[rep, i] = lr1.error
tot_err2[rep, i] = lr2.error
results_models = results_models._append(
{'rep': rep,
'ntrain': ntrain,
'lr_clean': {'model': lr1, 'noise_matrix_used_in_learning': None, 'noise_matrix_used_in_corruption': None, 'predicted_labels': [lr1.y_pred],
'true_labels': [y_test]},
'lr_nocorr': {'model': lr2, 'noise_matrix_used_in_learning': None, 'noise_matrix_used_in_corruption': [T], 'predicted_labels': [lr2.y_pred],
'true_labels': [y_test]},
}, ignore_index=True
)
summary_data = pd.DataFrame(
data={
'n_samples': '%d' % n_samples,
'n_attributes': '%d' % n_features,
'phi': phi,
'rho1': '%f' % rho1,
'rho2': '%f' % rho2,
'noise_matrix': [T],
'ntrain_vector': [nvector],
}
)
summary_results = pd.DataFrame(
{'lr_clean': {'errors': tot_err1, 'times': tot_time1},
'lr_nocorr': {'errors': tot_err2, 'times': tot_time2},
}
)
return summary_data, summary_results, results_models
def simulation_ntrain_natarajan(X, Y, nvector, nrep, phi='fourier', rho1=0.2, rho2=0.2, **phi_kwargs):
"""
Simulation for different training size for Natarajan Model.
Parameters
----------
X : `array`-like of shape (`n_samples` * `n_features`)
matrix representing the data (features)
Y : `array`-like of shape (`n_samples` * 1)
matrix representing the labels
nvector : `array`-like of shape (`number_training_size` * 1)
vector representing different training size
nrep : `int`
number of repetitions for the simulations
phi : `str` or `BasePhi` instance, default = 'fourier'
Type of feature mapping function to use for mapping the input data.
The currently available feature mapping methods are
'fourier', 'relu', 'threshold' and 'linear'.
rho1 : `float`, default = 0.2
Noise rate on class 1
:math:` \\mathbb{P}(ytilde = 2 | ytrue = 1)`
rho2 : `float`, default = 0.2
Noise rate on class 2
:math:` \\mathbb{P}(ytilde = 1 | ytrue = 2)`
Returns
-------
results : all the important variables
"""
n_samples, n_features = X.shape
classes_ = np.unique(Y)
n_classes = len(classes_)
# TRASFORMA I DATI CON FEAT. MAPPING
# Feature mappings
fit_intercept = False
random_state = False
if phi == 'fourier':
phi = RandomFourierPhi(n_classes=n_classes,
fit_intercept=fit_intercept,
random_state=random_state,
**phi_kwargs)
elif phi == 'linear':
phi = BasePhi(n_classes=n_classes,
fit_intercept=fit_intercept,
**phi_kwargs)
elif phi == 'threshold':
phi = ThresholdPhi(n_classes=n_classes,
fit_intercept=fit_intercept,
**phi_kwargs)
elif phi == 'relu':
phi = RandomReLUPhi(n_classes=n_classes,
fit_intercept=fit_intercept,
random_state=random_state,
**phi_kwargs)
elif not isinstance(phi, BasePhi):
raise ValueError('Unexpected feature mapping type ... ')
# Fit the feature mappings
phi.fit(X, Y)
X_feat = phi.transform(X)
results_models = pd.DataFrame(
{'rep': [],
'ntrain': [],
'natarajan': [],
}
)
T = np.array([[1 - rho1, rho2], [rho1, 1 - rho2]])
Ytilda = corruption(Y, T)
my_shape = (nrep, len(nvector))
tot_time, tot_err = [np.zeros(my_shape) for _ in range(2)]
for rep in range(nrep):
seed = rep
print("Rep:", rep)
# split data into training and test sets:
X_train, X_test, y_tilda_train, y_tilda_test, \
train_indx, test_indx = train_test_split(X_feat, Ytilda, range(n_samples), test_size=0.2, random_state=seed,
stratify=Ytilda)
# retrieve the clean test and training set:
y_train = Y[train_indx]
y_test = Y[test_indx]
for i, ntrain in enumerate(nvector):
print("training size: ", ntrain)
X_tr = X_train[0:ntrain, :]
y_tr = y_train[0:ntrain]
y_ti = y_tilda_train[0:ntrain]
# NATARAJAN METHOD: modified logistic regression
nat = my_natarajan(X_tr, y_ti, X_test, y_test, T, regularization='ridge')
tot_time[rep, i] = nat.time
tot_err[rep, i] = nat.error
results_models = results_models._append(
{'rep': rep,
'ntrain': ntrain,
'natarajan': {'model': nat,
'noise_matrix_used_in_learning': [T], 'noise_matrix_used_in_corruption': [T],
'predicted_labels': [nat.y_pred], 'true_labels': [y_test]}
}, ignore_index=True
)
summary_data = pd.DataFrame(
data={
'n_samples': '%d' % n_samples,
'n_attributes': '%d' % n_features,
'phi': phi,
'rho1': '%f' % rho1,
'rho2': '%f' % rho2,
'noise_matrix': [T],
'ntrain_vector': [nvector],
}
)
summary_results = pd.DataFrame(
{'natarajan': {'errors': tot_err, 'times': tot_time},
}
)
return summary_data, summary_results, results_models
def simulation_ntrain_cleanlab(X, Y, nvector, nrep, phi='fourier', rho1=0.2, rho2=0.2, **phi_kwargs):
"""
Simulation for different training size and using T_estimated provided
by the library cleanlab (https://docs.cleanlab.ai/stable/index.html)
Parameters
----------
X : `array`-like of shape (`n_samples` * `n_features`)
matrix representing the data (features)
Y : `array`-like of shape (`n_samples` * 1)
matrix representing the labels
nvector : `array`-like of shape (`number_training_size` * 1)
vector representing different training size
nrep : `int`
number of repetitions for the simulations
phi : `str` or `BasePhi` instance, default = 'fourier'
Type of feature mapping function to use for mapping the input data.
The currently available feature mapping methods are
'fourier', 'relu', 'threshold' and 'linear'.
rho1 : `float`, default = 0.2
Noise rate on class 1
:math:` \\mathbb{P}(ytilde = 2 | ytrue = 1)`
rho2 : `float`, default = 0.2
Noise rate on class 2
:math:` \\mathbb{P}(ytilde = 1 | ytrue = 2)`
Returns
-------
summary_data: Pandas Dataframe
summary of all the simulation's setting
(loss, feat. mapp, rhos)
summary_results: Pandas Dataframe
summary of the main results
(errors of the methods, times and eventually bounds)
results_models: Pandas Dataframe
containing the detailed information regarding each modela of each repetition and training size
"""
n_samples, n_features = X.shape
classes_ = np.unique(Y)
n_classes = len(classes_)
# TRASFORMA I DATI CON FEAT. MAPPING
# Feature mappings
fit_intercept = False
random_state = False
if phi == 'fourier':
phi = RandomFourierPhi(n_classes=n_classes,
fit_intercept=fit_intercept,
random_state=random_state,
**phi_kwargs)
elif phi == 'linear':
phi = BasePhi(n_classes=n_classes,
fit_intercept=fit_intercept,
**phi_kwargs)
elif phi == 'threshold':
phi = ThresholdPhi(n_classes=n_classes,
fit_intercept=fit_intercept,
**phi_kwargs)
elif phi == 'relu':
phi = RandomReLUPhi(n_classes=n_classes,
fit_intercept=fit_intercept,
random_state=random_state,
**phi_kwargs)
elif not isinstance(phi, BasePhi):
raise ValueError('Unexpected feature mapping type ... ')
# Fit the feature mappings
phi.fit(X, Y)
X_feat = phi.transform(X)
results_models = pd.DataFrame(
{'rep': [],
'ntrain': [],
'cl': [],
}
)
T = np.array([[1 - rho1, rho2], [rho1, 1 - rho2]])
Ytilda = corruption(Y, T)
my_shape = (nrep, len(nvector))
# cleanlab method
tot_time_cl, tot_err_cl = [np.zeros(my_shape) for _ in range(2)]
for rep in range(nrep):
seed = rep
print("Rep:", rep)
X_train, X_test, y_tilda_train, y_tilda_test, \
train_indx, test_indx = train_test_split(X_feat, Ytilda, range(n_samples), test_size=0.2,
random_state=seed, stratify=Ytilda)
# retrieve the clean training and test sets:
y_train = Y[train_indx]
y_test = Y[test_indx]
for i, ntrain in enumerate(nvector):
print("training size: ", ntrain)
X_tr = X_train[0:ntrain, :]
y_tr = y_train[0:ntrain]
y_ti = y_tilda_train[0:ntrain]
# METHOD OF CLEANLAB
start_time = time.time()
cl = CleanLearning(clf=LogisticRegression(max_iter=400)) # any sklearn-compatible classifier
cl.fit(X_tr, y_ti)
end_time = time.time()
elapsed_time = end_time - start_time
y_pred = cl.predict(X_test)
cl.y_pred = y_pred
cl.error = np.average(y_pred != y_test)
cl.time = elapsed_time
tot_time_cl[rep, i] = cl.time
tot_err_cl[rep, i] = cl.error
results_models = results_models._append(
{'rep': rep,
'ntrain': ntrain,
'cl': {'model': cl, 'noise_matrix_used_in_learning': None,
'noise_matrix_used_in_corruption': T, 'predicted_labels': [cl.y_pred],
'true_labels': [y_test]}
}, ignore_index=True
)
summary_data = pd.DataFrame(
data={
'n_samples': '%d' % n_samples,
'n_attributes': '%d' % n_features,
'phi': phi,
'rho1': '%f' % rho1,
'rho2': '%f' % rho2,
'noise_matrix': [T],
'ntrain_vector': [nvector],
}
)
summary_results = pd.DataFrame(
data={
'cl': {'errors': tot_err_cl, 'times': tot_time_cl}
}
)
return summary_data, summary_results, results_models
def simulation_ntrain_mrc_est(X, Y, nvector, nrep, det, lambda0, phi='fourier', loss='0-1', rho1=0.2, rho2=0.2):
"""
Simulation for different training size and using MRC + T_estimated.
T_estimated is provided by the library cleanlab (https://docs.cleanlab.ai/stable/index.html)
Parameters
----------
X : `array`-like of shape (`n_samples` * `n_features`)
matrix representing the data (features)
Y : `array`-like of shape (`n_samples` * 1)
matrix representing the labels
nvector : `array`-like of shape (`number_training_size` * 1)
vector representing different training size
nrep : `int`
number of repetitions for the simulations
det : `bool`
True if we want to use the deterministic rule to predict
False if we want to use the probabilistic rule to predict
lambda0 : `float`
hyperparameter of MRC that controls the size of the set U:
lambda = lambda0 * std/ sqrt(ntrain)
Typically: lambda0 = 0.3, 0.7, 1
phi : `str` or `BasePhi` instance, default = 'fourier'
Type of feature mapping function to use for mapping the input data.
The currently available feature mapping methods are
'fourier', 'relu', 'threshold' and 'linear'.
loss : `str` {'0-1', 'log'}, default = '0-1'
Type of loss function to use for the risk minimization.
rho1 : `float`, default = 0.2
Noise rate on class 1
:math:` \\mathbb{P}(ytilde = 2 | ytrue = 1)`
rho2 : `float`, default = 0.2
Noise rate on class 2
:math:` \\mathbb{P}(ytilde = 1 | ytrue = 2)`
Returns
-------
summary_data: Pandas Dataframe
summary of all the simulation's setting
(loss, feat. mapp, rhos)
summary_results: Pandas Dataframe
summary of the main results
(errors of the methods, times and eventually bounds)
results_models: Pandas Dataframe
containing the detailed information regarding each modela of each repetition and training size
"""
n_samples, n_features = X.shape
classes_ = np.unique(Y)
n_classes = len(classes_)
results_models = pd.DataFrame(
{'rep': [],
'ntrain': [],
'mrc_back_est': [],
}
)
T = np.array([[1 - rho1, rho2], [rho1, 1 - rho2]])
Ytilda = corruption(Y, T)
my_shape = (nrep, len(nvector))
# mrc + T estimated
tot_time_mrc, tot_err_mrc, tot_bd_mrc = [np.zeros(my_shape) for _ in range(3)]
train_ratio = 0.6
validation_ratio = 0.30
test_ratio = 0.10
for rep in range(nrep):
seed = rep
print("Rep:", rep)
# train is now 75% of the entire data set
X_train, X_rem, y_tilda_train, y_tilda_rem, \
train_indx, rem_indx = train_test_split(X, Ytilda, range(n_samples), test_size=1 - train_ratio,
random_state=seed, stratify=Ytilda)
# test is now 10% of the initial data set & validation is now 15% of the initial data set
X_val, X_test, y_tilda_val, y_tilda_test, \
val_indx, test_indx = train_test_split(X_rem, y_tilda_rem, range(len(y_tilda_rem)),
test_size=test_ratio / (test_ratio + validation_ratio),
random_state=seed, stratify=y_tilda_rem)
# Retrieve the clean training and test sets:
y_rem = Y[rem_indx]
y_test = y_rem[test_indx]
# Estimate T = P(obs_lab|true_lab) using cleanlab on the validation set
T_est = estimate_noise_matrices(X_val, y_tilda_val, clf=LogisticRegression(max_iter=500), cv_n_folds=5,
thresholds=None, converge_latent_estimates=True, seed=None, clf_kwargs={},
validation_func=None)[0]
print('Estimated T: ', T_est)
for i, ntrain in enumerate(nvector):
X_tr = X_train[0:ntrain, :]
y_ti = y_tilda_train[0:ntrain]
# MRC on noisy labels with backward correction on T estimated
mrc = my_mrc(phi, loss, det, lambda0, seed, 'backward', X_tr, y_ti, X_test, y_test, T=T_est, projection=True)
tot_time_mrc[rep, i] = mrc.time
tot_bd_mrc[rep, i] = mrc.upper_
tot_err_mrc[rep, i] = mrc.error
results_models = results_models._append(
{'rep': rep,
'ntrain': ntrain,
'mrc_back_est': {'model': mrc, 'noise_matrix_used_in_learning': T_est,
'noise_matrix_used_in_corruption': T, 'predicted_labels': [mrc.y_pred],
'true_labels': [y_test]},
}, ignore_index=True
)
summary_data = pd.DataFrame(
data={
'n_samples': '%d' % n_samples,
'n_attributes': '%d' % n_features,
'phi': phi,
'loss': loss,
'deterministic_rule': det,
'lambda0': lambda0,
'rho1': '%f' % rho1,
'rho2': '%f' % rho2,
'noise_matrix': [T],
'ntrain_vector': [nvector],
}
)
summary_results = pd.DataFrame(
data={
'mrc_back_est': {'errors': tot_err_mrc, 'times': tot_time_mrc, 'bounds': tot_bd_mrc},
}
)
return summary_data, summary_results, results_models
def simulation_ntrain_lrcleansed(X, Y, nvector, nrep, phi='fourier', rho1=0.2, rho2=0.2, **phi_kwargs):
"""
Simulation for different training size and using cleansed labels for Logistic Regression.
The cleansed labels are obtained using the library cleanlab (https://docs.cleanlab.ai/stable/index.html)
Parameters
----------
X : `array`-like of shape (`n_samples` * `n_features`)
matrix representing the data (features)
Y : `array`-like of shape (`n_samples` * 1)
matrix representing the labels
nvector : `array`-like of shape (`number_training_size` * 1)
vector representing different training size
nrep : `int`
number of repetitions for the simulations
phi : `str` or `BasePhi` instance, default = 'fourier'
Type of feature mapping function to use for mapping the input data.
The currently available feature mapping methods are
'fourier', 'relu', 'threshold' and 'linear'.
loss : `str` {'0-1', 'log'}, default = '0-1'
Type of loss function to use for the risk minimization.
rho1 : `float`, default = 0.2
Noise rate on class 1
:math:` \\mathbb{P}(ytilde = 2 | ytrue = 1)`
rho2 : `float`, default = 0.2
Noise rate on class 2
:math:` \\mathbb{P}(ytilde = 1 | ytrue = 2)`
Returns
-------
summary_data: Pandas Dataframe
summary of all the simulation's setting
(loss, feat. mapp, rhos)
summary_results: Pandas Dataframe
summary of the main results
(errors of the methods, times and eventually bounds)
results_models: Pandas Dataframe
containing the detailed information regarding each modela of each repetition and training size
results_cleanlab: Pandas Dataframe
containing the detailed information regarding the cleaning process of the labels, of each rep.
"""
n_samples, n_features = X.shape
classes_ = np.unique(Y)
n_classes = len(classes_)
# TRASFORMA I DATI CON FEAT. MAPPING
# Feature mappings
fit_intercept = False
random_state = False
if phi == 'fourier':
phi = RandomFourierPhi(n_classes=n_classes,
fit_intercept=fit_intercept,
random_state=random_state,
**phi_kwargs)
elif phi == 'linear':
phi = BasePhi(n_classes=n_classes,
fit_intercept=fit_intercept,
**phi_kwargs)
elif phi == 'threshold':
phi = ThresholdPhi(n_classes=n_classes,
fit_intercept=fit_intercept,
**phi_kwargs)
elif phi == 'relu':
phi = RandomReLUPhi(n_classes=n_classes,
fit_intercept=fit_intercept,
random_state=random_state,
**phi_kwargs)
elif not isinstance(phi, BasePhi):
raise ValueError('Unexpected feature mapping type ... ')
# Fit the feature mappings
phi.fit(X, Y)
X_feat = phi.transform(X)
results_models = pd.DataFrame(
{'rep': [],
'ntrain': [],
'lr_cleansed': [],
}
)
T = np.array([[1 - rho1, rho2], [rho1, 1 - rho2]])
Ytilda = corruption(Y, T)
my_shape = (nrep, len(nvector))
# log regression + cleansed labels
tot_time_lr, tot_err_lr = [np.zeros(my_shape) for _ in range(2)]
for rep in range(nrep):
seed = rep
print("Rep:", rep)
# train is now 75% of the entire data set
X_train, X_test, y_tilda_train, y_tilda_test, \
train_indx, test_indx = train_test_split(X_feat, Ytilda, range(n_samples), test_size=0.2,
random_state=seed, stratify=Ytilda)
# retrieve the clean test and training sets:
y_test = Y[test_indx]
y_train = Y[train_indx]
# clean the train noisy labels using cleanlab:
cl_rep, y_cleansed_train = clean_labels(X_train, y_tilda_train)
number_true_issues = np.count_nonzero(y_train != y_tilda_train)
print(f'The exact number of label errors is {number_true_issues}')
for i, ntrain in enumerate(nvector):
X_tr = X_train[0:ntrain, :]
y_cl = y_cleansed_train[0:ntrain]
# LR on cleansed labels
lr = my_lr(X_tr, y_cl, X_test, y_test, max_iter=300)
tot_time_lr[rep, i] = lr.time
tot_err_lr[rep, i] = lr.error
results_models = results_models._append(
{'rep': rep,
'ntrain': ntrain,
'lr_cleansed': {'model': lr, 'noise_matrix_used_in_learning': None,
'noise_matrix_used_in_corruption': T, 'predicted_labels': [lr.y_pred],
'true_labels': [y_test]},
}, ignore_index=True
)
results_cleanlab = pd.DataFrame(
data={
'rep': rep,
'number_true_issues': number_true_issues,
'number_found_issues': cl_rep.number_found_issues,
'noisy_labels': [y_tilda_train],
'true_labels': [y_train],
'cleansed_labels': [y_cleansed_train],
'labels_issues': cl_rep
}
)
summary_data = pd.DataFrame(
data={
'n_samples': '%d' % n_samples,
'n_attributes': '%d' % n_features,
'phi': phi,
'rho1': '%f' % rho1,
'rho2': '%f' % rho2,
'noise_matrix': [T],
'ntrain_vector': [nvector],
}
)
summary_results = pd.DataFrame(
data={
'lr_cleansed': {'errors': tot_err_lr, 'times': tot_time_lr, 'bounds': None},
}
)
return summary_data, summary_results, results_models, results_cleanlab
def simulation_ntrain_mrccleansed(X, Y, nvector, nrep, det, lambda0, phi='fourier', loss='0-1', rho1=0.2, rho2=0.2):
"""
Simulation for different training size and using cleansed labels on MRC.
The cleansed labels are obtained using the library cleanlab (https://docs.cleanlab.ai/stable/index.html)
Parameters
----------
X : `array`-like of shape (`n_samples` * `n_features`)
matrix representing the data (features)
Y : `array`-like of shape (`n_samples` * 1)
matrix representing the labels
nvector : `array`-like of shape (`number_training_size` * 1)
vector representing different training size
nrep : `int`
number of repetitions for the simulations
det : `bool`
True if we want to use the deterministic rule to predict
False if we want to use the probabilistic rule to predict
lambda0 : `float`
hyperparameter of MRC that controls the size of the set U:
lambda = lambda0 * std/ sqrt(ntrain)
Typically: lambda0 = 0.3, 0.7, 1
phi : `str` or `BasePhi` instance, default = 'fourier'
Type of feature mapping function to use for mapping the input data.
The currently available feature mapping methods are
'fourier', 'relu', 'threshold' and 'linear'.
loss : `str` {'0-1', 'log'}, default = '0-1'
Type of loss function to use for the risk minimization.
rho1 : `float`, default = 0.2
Noise rate on class 1
:math:` \\mathbb{P}(ytilde = 2 | ytrue = 1)`
rho2 : `float`, default = 0.2
Noise rate on class 2
:math:` \\mathbb{P}(ytilde = 1 | ytrue = 2)`
Returns
-------
summary_data: Pandas Dataframe
summary of all the simulation's setting
(loss, feat. mapp, rhos)
summary_results: Pandas Dataframe
summary of the main results
(errors of the methods, times and eventually bounds)
results_models: Pandas Dataframe
containing the detailed information regarding each modela of each repetition and training size