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# coding: utf-8
# ## Pruning
#
# #### Contains methods for estimating the latent indices of all label errors.
from __future__ import (
print_function, absolute_import, division, unicode_literals, with_statement,
)
from sklearn.preprocessing import MultiLabelBinarizer
import multiprocessing
import sys
import time
from cleanlab.util import value_counts, round_preserving_row_totals
import numpy as np
# tqdm is a module used to print time-to-complete when multiprocessing is used.
# This module is not necessary, and therefore is not a package dependency, but
# when installed it improves user experience for large datsets.
try:
import tqdm
tqdm_exists = True
except ImportError as e:
tqdm_exists = False
import warnings
w = '''If you want to see estimated completion times
while running methods in cleanlab.pruning, install tqdm
via "pip install tqdm".'''
warnings.warn(w)
# Leave at least this many examples in each class after
# pruning, regardless if noise estimates are larger.
MIN_NUM_PER_CLASS = 5
# For python 2/3 compatibility, define pool context manager
# to support the 'with' statement in Python 2
if sys.version_info[0] == 2:
from contextlib import contextmanager
@contextmanager
def multiprocessing_context(*args, **kwargs):
pool = multiprocessing.Pool(*args, **kwargs)
yield pool
pool.terminate()
else:
multiprocessing_context = multiprocessing.Pool
# Multiprocessing Helper functions
def _make_global(
_s,
_s_counts,
_prune_count_matrix,
_psx,
_multi_label,
): # pragma: no cover
'''Shares memory objects across child processes.
ASSUMES none of these will change!'''
global s, s_counts, prune_count_matrix, psx, multi_label
s = _s
s_counts = _s_counts
prune_count_matrix = _prune_count_matrix
psx = _psx
multi_label = _multi_label
def _prune_by_class(k):
"""multiprocessing Helper function that assumes globals
and produces a mask for class k for each example by
removing the examples with *smallest probability* of
belonging to their given class label.
Parameters
----------
k : int (between 0 and num classes - 1)
The class of interest."""
if s_counts[k] > MIN_NUM_PER_CLASS: # Don't prune if not MIN_NUM_PER_CLASS
num_errors = s_counts[k] - prune_count_matrix[k][k]
# Get rank of smallest prob of class k for examples with noisy label k
s_filter = np.array([k in l for l in s]) if multi_label else s == k
class_probs = psx[:, k]
rank = np.partition(class_probs[s_filter], num_errors)[num_errors]
# noise_mask = noise_mask | ((s_filter) & (psx[:,k] < threshold))
return ((s_filter) & (class_probs < rank))
else:
return np.zeros(len(s), dtype=bool)
def _prune_by_count(k):
"""multiprocessing Helper function that assumes globals
and produces a mask for class k for each example by
removing the example with noisy label k having *largest margin*,
where
margin of example := prob of given label - max prob of non-given labels
Parameters
----------
k : int (between 0 and num classes - 1)
The true, hidden label class of interest."""
noise_mask = np.zeros(len(psx), dtype=bool)
psx_k = psx[:, k]
K = len(s_counts)
if s_counts[k] <= MIN_NUM_PER_CLASS: # Don't prune if not MIN_NUM_PER_CLASS
return np.zeros(len(s), dtype=bool)
for j in range(K): # j is true label index (k is noisy label index)
num2prune = prune_count_matrix[j][k]
# Only prune for noise rates, not diagonal entries
if k != j and num2prune > 0:
# num2prune'th largest p(true class k) - p(noisy class k)
# for x with true label j
margin = psx[:, j] - psx_k
s_filter = np.array([k in l for l in s]) if multi_label else s == k
cut = -np.partition(-margin[s_filter], num2prune - 1)[num2prune - 1]
noise_mask = noise_mask | ((s_filter) & (margin >= cut))
return noise_mask
def _self_confidence(args): # pragma: no cover
"""multiprocessing Helper function that assumes global
psx and computes the self confidence (prob of given label)
for an example (row in psx) given the example index idx
and its label l.
np.mean(psx[]) enables this code to work for multi-class l."""
(idx, l) = args
return np.mean(psx[idx, l])
def multiclass_crossval_predict(pyx, labels):
'''Returns an numpy 2D array of one-hot encoded
multiclass predictions. Each row in the array
provides the predictions for a particular example.
The boundary condition used to threshold predictions
is computed by maximizing the F1 ROC curve.
Parameters
----------
pyx : np.array (shape (N, K))
P(label=k|x) is a NxK matrix with K probs for each of N examples.
This is the probability distribution over all K classes, for each
pyx should have been computed out of sample (holdout or crossval).
labels : list of lists (length N)
These are multiclass labels. Each list in the list contains all the
labels for that example.'''
from sklearn.metrics import f1_score
boundaries = np.arange(0.05, 0.9, .05)
labels_one_hot = MultiLabelBinarizer().fit_transform(labels)
f1s = [f1_score(
labels_one_hot, (pyx > boundary).astype(np.uint8), average='micro',
) for boundary in boundaries]
boundary = boundaries[np.argmax(f1s)]
pred = (pyx > boundary).astype(np.uint8)
return pred
def get_noise_indices(
s,
psx,
inverse_noise_matrix=None,
confident_joint=None,
frac_noise=1.0,
num_to_remove_per_class=None,
prune_method='prune_by_noise_rate',
sorted_index_method=None,
multi_label=False,
n_jobs = None,
verbose=0,
):
'''Returns the indices of most likely (confident) label errors in s. The
number of indices returned is specified by frac_of_noise. When
frac_of_noise = 1.0, all "confident" estimated noise indices are returned.
Parameters
----------
s : np.array
A binary vector of labels, s, which may contain mislabeling. "s" denotes
the noisy label instead of \tilde(y), for ASCII encoding reasons.
psx : np.array (shape (N, K))
P(s=k|x) is a matrix with K (noisy) probabilities for each of the N
examples x.
This is the probability distribution over all K classes, for each
example, regarding whether the example has label s==k P(s=k|x).
psx should have been computed using 3+ fold cross-validation.
inverse_noise_matrix : np.array of shape (K, K), K = number of classes
A conditional probablity matrix of the form P(y=k_y|s=k_s) representing
the estimated fraction observed examples in each class k_s, that are
mislabeled examples from every other class k_y. If None, the
inverse_noise_matrix will be computed from psx and s.
Assumes columns of inverse_noise_matrix sum to 1.
confident_joint : np.array (shape (K, K), type int) (default: None)
A K,K integer matrix of count(s=k, y=k). Estimatesa a confident
subset of the joint disribution of the noisy and true labels P_{s,y}.
Each entry in the matrix contains the number of examples confidently
counted into every pair (s=j, y=k) classes.
frac_noise : float
When frac_of_noise = 1.0, return all "confident" estimated noise indices.
Value in range (0, 1] that determines the fraction of noisy example
indices to return based on the following formula for example class k.
frac_of_noise * number_of_mislabeled_examples_in_class_k, or equivalently
frac_of_noise * inverse_noise_rate_class_k * num_examples_with_s_equal_k
num_to_remove_per_class : list of int of length K (# of classes)
e.g. if K = 3, num_to_remove_per_class = [5, 0, 1] would return
the indices of the 5 most likely mislabeled examples in class s = 0,
and the most likely mislabeled example in class s = 1.
***Only set this parameter if prune_method == 'prune_by_class'
You may use with prune_method == 'prune_by_noise_rate', but
if num_to_remove_per_class == k, then either k-1, k, or k+1
examples may be removed for any class. This is because noise rates
are floats, and rounding may cause a one-off. If you need exactly
'k' examples removed from every class, you should use 'prune_by_class'.
prune_method : str (default: 'prune_by_noise_rate')
Posible Values: 'prune_by_class', 'prune_by_noise_rate', or 'both'.
Method used for pruning.
1. 'prune_by_noise_rate': works by removing examples with
*high probability* of being mislabeled for every non-diagonal
in the prune_counts_matrix (see pruning.py).
2. 'prune_by_class': works by removing the examples with *smallest
probability* of belonging to their given class label for every class.
3. 'both': Finds the examples satisfying (1) AND (2) and
removes their set conjunction.
sorted_index_method : str [None, 'prob_given_label', 'normalized_margin']
If None, returns a boolean mask (true if example at index is label error)
If not None, returns an array of the label error indices
(instead of a bool mask) where error indices are ordered by the either:
'normalized_margin' := normalized margin (p(s = k) - max(p(s != k)))
'prob_given_label' := [psx[i][labels[i]] for i in label_errors_idx]
multi_label : bool
If true, s should be an iterable (e.g. list) of iterables, containing a
list of labels for each example, instead of just a single label.
n_jobs : int
Number of processing threads used by multiprocessing. Default None
sets to the number of processing threads on your CPU.
Set this to 1 to REMOVE parallel processing (if its causing issues).
verbose : int
If 0, no print statements. If 1, prints when multiprocessing happens.'''
# Set-up number of multiprocessing threads
if n_jobs is None:
n_jobs = multiprocessing.cpu_count()
else:
assert (n_jobs >= 1)
# Number of examples in each class of s
if multi_label:
s_counts = value_counts([i for l in s for i in l])
else:
s_counts = value_counts(s)
# Number of classes s
K = len(psx.T)
# Boolean set to true if dataset is large
big_dataset = K * len(s) > 1e8
# Ensure labels are of type np.array()
s = np.asarray(s)
if confident_joint is None:
from cleanlab.latent_estimation import compute_confident_joint
confident_joint = compute_confident_joint(
s=s,
psx=psx,
multi_label=multi_label,
)
# Leave at least MIN_NUM_PER_CLASS examples per class.
# NOTE prune_count_matrix is transposed (relative to confident_joint)
prune_count_matrix = keep_at_least_n_per_class(
prune_count_matrix=confident_joint.T,
n=MIN_NUM_PER_CLASS,
frac_noise=frac_noise,
)
if num_to_remove_per_class is not None:
# Estimate joint probability distribution over label errors
psy = prune_count_matrix / np.sum(prune_count_matrix, axis=1)
noise_per_s = psy.sum(axis=1) - psy.diagonal()
# Calibrate s.t. noise rates sum to num_to_remove_per_class
tmp = (psy.T * num_to_remove_per_class / noise_per_s).T
np.fill_diagonal(tmp, s_counts - num_to_remove_per_class)
prune_count_matrix = round_preserving_row_totals(tmp)
# Make static data global for use in multiprocessing or sub-functions
_make_global(s, s_counts, prune_count_matrix, psx, multi_label)
# Peform Pruning with threshold probabilities from BFPRT algorithm in O(n)
# Operations are parallelized across all CPU processes
if prune_method == 'prune_by_class' or prune_method == 'both':
if n_jobs > 1: # parallelize
with multiprocessing_context(n_jobs) as p:
if verbose:
print('Parallel processing label errors by class.')
sys.stdout.flush()
if big_dataset and tqdm_exists:
noise_masks_per_class = list(
tqdm.tqdm(p.imap(_prune_by_class, range(K)), total=K),
)
else:
noise_masks_per_class = p.map(_prune_by_class, range(K))
else: # n_jobs = 1, so no parallelization
noise_masks_per_class = [_prune_by_class(k) for k in range(K)]
label_errors_mask = np.stack(noise_masks_per_class).any(axis=0)
if prune_method == 'both':
label_errors_mask_by_class = label_errors_mask
if prune_method == 'prune_by_noise_rate' or prune_method == 'both':
if n_jobs > 1: # parallelize
with multiprocessing_context(n_jobs) as p:
if verbose:
print('Parallel processing label errors by noise rate.')
sys.stdout.flush()
if big_dataset and tqdm_exists:
noise_masks_per_class = list(
tqdm.tqdm(p.imap(_prune_by_count, range(K)), total=K)
)
else:
noise_masks_per_class = p.map(_prune_by_count, range(K))
else: # n_jobs = 1, so no parallelization
noise_masks_per_class = [_prune_by_count(k) for k in range(K)]
label_errors_mask = np.stack(noise_masks_per_class).any(axis=0)
if prune_method == 'both':
label_errors_mask = label_errors_mask & label_errors_mask_by_class
# Remove label errors if given label == model prediction
if multi_label:
pred = multiclass_crossval_predict(psx, s)
s = MultiLabelBinarizer().fit_transform(s)
else:
pred = psx.argmax(axis=1)
for i, pred_label in enumerate(pred):
if multi_label and np.all(pred_label == s[i]) or \
not multi_label and pred_label == s[i]:
label_errors_mask[i] = False
if sorted_index_method is not None:
er = order_label_errors(label_errors_mask, psx, s, sorted_index_method)
return er
return label_errors_mask
def keep_at_least_n_per_class(prune_count_matrix, n, frac_noise=1.0):
'''Make sure every class has at least n examples after removing noise.
Functionally, increase each column, increases the diagonal term #(y=k,s=k)
of prune_count_matrix until it is at least n, distributing the amount
increased by subtracting uniformly from the rest of the terms in the
column. When frac_of_noise = 1.0, return all "confidently" estimated
noise indices, otherwise this returns frac_of_noise fraction of all
the noise counts, with diagonal terms adjusted to ensure column
totals are preserved.
Parameters
----------
prune_count_matrix : np.array of shape (K, K), K = number of classes
A counts of mislabeled examples in every class. For this function.
NOTE prune_count_matrix is transposed relative to confident_joint.
n : int
Number of examples to make sure are left in each class.
frac_noise : float
When frac_of_noise = 1.0, return all estimated noise indices.
Value in range (0, 1] that determines the fraction of noisy example
indices to return based on the following formula for example class k.
frac_of_noise * number_of_mislabeled_examples_in_class_k, or
frac_of_noise * inverse_noise_rate_class_k * num_examples_s_equal_k
Output
------
prune_count_matrix : np.array of shape (K, K), K = number of classes
Number of examples to remove from each class, for every other class.'''
K = len(prune_count_matrix)
prune_count_matrix_diagonal = np.diagonal(prune_count_matrix)
# Set diagonal terms less than n, to n.
new_diagonal = np.maximum(prune_count_matrix_diagonal, n)
# Find how much diagonal terms were increased.
diff_per_col = new_diagonal - prune_count_matrix_diagonal
# Count non-zero, non-diagonal items per column
# np.maximum(*, 1) makes this never 0 (we divide by this next)
num_noise_rates_per_col = np.maximum(
np.count_nonzero(prune_count_matrix, axis=0) - 1.,
1.,
)
# Uniformly decrease non-zero noise rates by the same amount
# that the diagonal items were increased
new_mat = prune_count_matrix - diff_per_col / num_noise_rates_per_col
# Originally zero noise rates will now be negative, fix them back to zero
new_mat[new_mat < 0] = 0
# Round diagonal terms (correctly labeled examples)
np.fill_diagonal(new_mat, new_diagonal)
# Reduce (multiply) all noise rates (non-diagonal) by frac_noise and
# increase diagonal by the total amount reduced in each column
# to preserve column counts.
new_mat = reduce_prune_counts(new_mat, frac_noise)
# These are counts, so return a matrix of ints.
return round_preserving_row_totals(new_mat)
def reduce_prune_counts(prune_count_matrix, frac_noise=1.0):
'''Reduce (multiply) all prune counts (non-diagonal) by frac_noise and
increase diagonal by the total amount reduced in each column to
preserve column counts.
Parameters
----------
prune_count_matrix : np.array of shape (K, K), K = number of classes
A counts of mislabeled examples in every class. For this function, it
does not matter what the rows or columns are, but the diagonal terms
reflect the number of correctly labeled examples.
frac_noise : float
When frac_of_noise = 1.0, return all estimated noise indices.
Value in range (0, 1] that determines the fraction of noisy example
indices to return based on the following formula for example class k.
frac_of_noise * number_of_mislabeled_examples_in_class_k, or
frac_of_noise * inverse_noise_rate_class_k * num_examples_s_equal_k'''
new_mat = prune_count_matrix * frac_noise
np.fill_diagonal(new_mat, prune_count_matrix.diagonal())
np.fill_diagonal(new_mat, prune_count_matrix.diagonal() +
np.sum(prune_count_matrix - new_mat, axis=0))
# These are counts, so return a matrix of ints.
return new_mat.astype(int)
def order_label_errors(
label_errors_bool,
psx,
labels,
sorted_index_method='normalized_margin',
):
'''Sorts label errors by normalized margin.
See https://arxiv.org/pdf/1810.05369.pdf (eqn 2.2)
eg. normalized_margin = prob_label - max_prob_not_label
Parameters
----------
label_errors_bool : np.array (bool)
Contains True if the index of labels is an error, o.w. false
psx : np.array (shape (N, K))
P(s=k|x) is a matrix with K probabilities for all N examples x.
This is the probability distribution over all K classes, for each
example, regarding whether the example has label s==k P(s=k|x). psx
should computed using 3 (or higher) fold cross-validation.
labels : np.array
A binary vector of labels, which may contain label errors.
sorted_index_method : str ['normalized_margin', 'prob_given_label']
Method to order label error indices (instead of a bool mask), either:
'normalized_margin' := normalized margin (p(s = k) - max(p(s != k)))
'prob_given_label' := [psx[i][labels[i]] for i in label_errors_idx]
Returns
-------
label_errors_idx : np.array (int)
Return the index integers of the label errors, ordered by
the normalized margin.
'''
# Convert bool mask to index mask
label_errors_idx = np.arange(len(labels))[label_errors_bool]
# self confidence is the holdout probability that an example
# belongs to its given class label
self_confidence = np.array(
[np.mean(psx[i][labels[i]]) for i in label_errors_idx]
)
if sorted_index_method == 'prob_given_label':
return label_errors_idx[np.argsort(self_confidence)]
else: # sorted_index_method == 'normalized_margin'
margin = self_confidence - psx[label_errors_bool].max(axis=1)
return label_errors_idx[np.argsort(margin)]
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