# Source code for cleanlab.count

```
# Copyright (C) 2017-2022 Cleanlab Inc.
# This file is part of cleanlab.
#
# cleanlab is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# cleanlab is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with cleanlab. If not, see <https://www.gnu.org/licenses/>.
"""
Methods for estimating latent structures used for confident learning, including:
* Latent prior of the unobserved, error-less labels: `py`: ``p(y)``
* Latent noisy channel (noise matrix) characterizing the flipping rates: `nm`: ``P(given label | true label)``
* Latent inverse noise matrix characterizing the flipping process: `inv`: ``P(true label | given label)``
* Latent `confident_joint`, an un-normalized matrix that counts the confident subset of label errors under the joint distribution for true/given label
"""
from sklearn.linear_model import LogisticRegression as LogReg
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import confusion_matrix
import sklearn.base
import numpy as np
import warnings
from typing import Tuple, Union
from cleanlab.internal.util import (
value_counts,
clip_values,
clip_noise_rates,
round_preserving_row_totals,
append_extra_datapoint,
train_val_split,
get_num_classes,
is_torch_dataset,
is_tensorflow_dataset,
)
from cleanlab.internal.latent_algebra import (
compute_inv_noise_matrix,
compute_py,
compute_noise_matrix_from_inverse,
)
from cleanlab.internal.validation import (
assert_valid_inputs,
labels_to_array,
)
[docs]def num_label_issues(
labels,
pred_probs,
confident_joint=None,
) -> int:
"""Estimates the number of label issues in the `labels` of a dataset.
This method is **more accurate** than ``sum(find_label_issues())`` because its computed using only
the trace of the confident joint, ignoring all off-diagonals (which are used by `find_label_issues` and are harder to
estimate). Here, we sum over only diagonal elements in the joint (which have more data
are more constrained, and therefore easier to compute).
TL;DR: use this method to get the most accurate estimate of number of label issues when you
don't need the indices of the label issues.
You can use this method to label issues by using `num_label_issues` as the cutoff threshold
used with ranking/scoring functions from :py:mod:`cleanlab.rank` with `num_label_issues`. There
are two cases when you should use this approach instead of :py:func:`filter.find_label_issues <cleanlab.filter.find_label_issues>`:
1. As we add more label and data quality scoring functions in :py:mod:`cleanlab.rank`, this approach will always work.
2. If you have a custom score to rank your data by label quality and you just need to know the cut-off of likely label issues.
Parameters
----------
labels : np.ndarray
An array of shape ``(N,)`` of noisy labels, i.e. some labels may be erroneous.
Elements must be in the set 0, 1, ..., K-1, where K is the number of classes.
pred_probs : np.ndarray
An array of shape ``(N, K)`` of model-predicted probabilities,
``P(label=k|x)``. Each row of this matrix corresponds
to an example `x` and contains the model-predicted probabilities that
`x` belongs to each possible class, for each of the K classes. The
columns must be ordered such that these probabilities correspond to
class 0, 1, ..., K-1. `pred_probs` should have been computed using 3 (or
higher) fold cross-validation.
confident_joint : np.ndarray, optional
An array of shape ``(K, K)`` representing the confident joint, the matrix used for identifying label issues, which
estimates a confident subset of the joint distribution of the noisy and true labels, ``P_{noisy label, true label}``.
Entry ``(j, k)`` in the matrix is the number of examples confidently counted into the pair of ``(noisy label=j, true label=k)`` classes.
The `confident_joint` can be computed using :py:func:`count.compute_confident_joint <cleanlab.count.compute_confident_joint>`.
If not provided, it is computed from the given (noisy) `labels` and `pred_probs`.
Returns
-------
num_issues : int
The estimated number of examples with label issues in the dataset.
"""
if confident_joint is None:
confident_joint = compute_confident_joint(labels=labels, pred_probs=pred_probs)
# Normalize confident joint so that it estimates the joint, p(labels,y)
joint = confident_joint / float(np.sum(confident_joint))
frac_issues = 1.0 - joint.trace()
num_issues = np.rint(frac_issues * len(labels)).astype(int)
return num_issues
[docs]def calibrate_confident_joint(confident_joint, labels, *, multi_label=False) -> np.ndarray:
"""Calibrates any confident joint estimate ``P(label=i, true_label=j)`` such that
``np.sum(cj) == len(labels)`` and ``np.sum(cj, axis = 1) == np.bincount(labels)``.
In other words, this function forces the confident joint to have the
true noisy prior ``p(labels)`` (summed over columns for each row) and also
forces the confident joint to add up to the total number of examples.
This method makes the confident joint a valid counts estimate
of the actual joint of noisy and true labels.
Parameters
----------
confident_joint : np.ndarray
An array of shape ``(K, K)`` representing the confident joint, the matrix used for identifying label issues, which
estimates a confident subset of the joint distribution of the noisy and true labels, ``P_{noisy label, true label}``.
Entry ``(j, k)`` in the matrix is the number of examples confidently counted into the pair of ``(noisy label=j, true label=k)`` classes.
The `confident_joint` can be computed using :py:func:`count.compute_confident_joint <cleanlab.count.compute_confident_joint>`.
If not provided, it is computed from the given (noisy) `labels` and `pred_probs`.
labels : np.ndarray
A discrete vector of noisy labels, i.e. some labels may be erroneous.
*Format requirements*: for dataset with K classes, labels must be in 0, 1, ..., K-1.
All the classes (0, 1, ..., and K-1) MUST be present in ``labels``, such that:
``len(set(labels)) == pred_probs.shape[1]`` for standard multi-class classification with single-labeled data (e.g. ``labels = [1,0,2,1,1,0...]``).
For multi-label classification where each example can belong to multiple classes(e.g. ``labels = [[1,2],[1],[0],..]``),
your labels should instead satisfy: ``len(set(k for l in labels for k in l)) == pred_probs.shape[1])``.
multi_label : bool, optional
If ``True``, labels should be an iterable (e.g. list) of iterables, containing a
list of labels for each example, instead of just a single label.
The multi-label setting supports classification tasks where an example has 1 or more labels.
Example of a multi-labeled `labels` input: ``[[0,1], [1], [0,2], [0,1,2], [0], [1], ...]``.
The major difference in how this is calibrated versus single-label is that
the total number of errors considered is based on the number of labels,
not the number of examples. So, the calibrated `confident_joint` will sum
to the number of total labels.
Returns
-------
calibrated_cj : np.ndarray
An array of shape ``(K, K)`` of type float representing a valid
estimate of the joint *counts* of noisy and true labels.
"""
if multi_label:
label_counts = value_counts([x for lst in labels for x in lst])
else:
label_counts = value_counts(labels)
# Calibrate confident joint to have correct p(labels) prior on noisy labels.
calibrated_cj = (confident_joint.T / confident_joint.sum(axis=1) * label_counts).T
# Calibrate confident joint to sum to:
# The number of examples (for single labeled datasets)
# The number of total labels (for multi-labeled datasets)
calibrated_cj = calibrated_cj / np.sum(calibrated_cj) * sum(label_counts)
return round_preserving_row_totals(calibrated_cj)
[docs]def estimate_joint(labels, pred_probs, *, confident_joint=None, multi_label=False) -> np.ndarray:
"""
Estimates the joint distribution of label noise ``P(label=i, true_label=j)`` guaranteed to:
* Sum to 1
* Satisfy ``np.sum(joint_estimate, axis = 1) == p(labels)``
Parameters
----------
labels : np.ndarray
An array of shape ``(N,)`` of noisy labels, i.e. some labels may be erroneous.
Elements must be in the set 0, 1, ..., K-1, where K is the number of classes.
All the classes (0, 1, ..., and K-1) MUST be present in ``labels``, such that:
``len(set(labels)) == pred_probs.shape[1]`` for standard multi-class classification with single-labeled data (e.g. ``labels = [1,0,2,1,1,0...]``).
For multi-label classification where each example can belong to multiple classes(e.g. ``labels = [[1,2],[1],[0],..]``),
your labels should instead satisfy: ``len(set(k for l in labels for k in l)) == pred_probs.shape[1])``.
pred_probs : np.ndarray
An array of shape ``(N, K)`` of model-predicted probabilities,
``P(label=k|x)``. Each row of this matrix corresponds
to an example `x` and contains the model-predicted probabilities that
`x` belongs to each possible class, for each of the K classes. The
columns must be ordered such that these probabilities correspond to
class 0, 1, ..., K-1. `pred_probs` should have been computed using 3 (or
higher) fold cross-validation.
confident_joint : np.ndarray, optional
An array of shape ``(K, K)`` representing the confident joint, the matrix used for identifying label issues, which
estimates a confident subset of the joint distribution of the noisy and true labels, ``P_{noisy label, true label}``.
Entry ``(j, k)`` in the matrix is the number of examples confidently counted into the pair of ``(noisy label=j, true label=k)`` classes.
The `confident_joint` can be computed using :py:func:`count.compute_confident_joint <cleanlab.count.compute_confident_joint>`.
If not provided, it is computed from the given (noisy) `labels` and `pred_probs`.
multi_label : bool, optional
If ``True``, labels should be an iterable (e.g. list) of iterables, containing a
list of labels for each example, instead of just a single label.
The multi-label setting supports classification tasks where an example has 1 or more labels.
Example of a multi-labeled `labels` input: ``[[0,1], [1], [0,2], [0,1,2], [0], [1], ...]``.
Returns
-------
confident_joint_distribution : np.ndarray
An array of shape ``(K, K)`` representing an
estimate of the true joint distribution of noisy and true labels.
"""
if confident_joint is None:
calibrated_cj = compute_confident_joint(
labels,
pred_probs,
calibrate=True,
multi_label=multi_label,
)
else:
calibrated_cj = calibrate_confident_joint(confident_joint, labels, multi_label=multi_label)
assert isinstance(calibrated_cj, np.ndarray)
return calibrated_cj / float(np.sum(calibrated_cj))
def _compute_confident_joint_multi_label(
labels,
pred_probs,
*,
thresholds=None,
calibrate=True,
return_indices_of_off_diagonals=False,
) -> Union[np.ndarray, Tuple[np.ndarray, list]]:
"""Computes the confident joint for multi_labeled data. Thus,
input `labels` is a list of lists (or list of iterable).
This is intended as a helper function. You should probably
be using `compute_confident_joint(multi_label=True)` instead.
The MAJOR DIFFERENCE in how this is computed versus single_label,
is the total number of errors considered is based on the number
of labels, not the number of examples. So, the confident_joint
will have larger values.
See `compute_confident_joint` docstring for more info.
Parameters
----------
labels : list of list/iterable (length N)
These are multiclass labels. Each list in the list contains
all the labels for that example. This method will fail if labels
is not a list of lists (or a list of np.ndarrays or iterable).
pred_probs : np.ndarray (shape (N, K))
P(label=k|x) is a matrix with K model-predicted probabilities.
Each row of this matrix corresponds to an example `x` and contains the model-predicted
probabilities that `x` belongs to each possible class.
The columns must be ordered such that these probabilities correspond to class 0, 1, 2,..., K-1.
`pred_probs` must be out-of-sample (ideally should have been computed using 3+ fold cross-validation).
thresholds : iterable (list or np.ndarray) of shape (K, 1) or (K,)
P(label^=k|label=k). If an example has a predicted probability "greater" than
this threshold, it is counted as having true_label = k. This is
not used for filtering/pruning, only for estimating the noise rates using
confident counts. This value should be between 0 and 1. Default is None.
calibrate : bool, default = True
Calibrates confident joint estimate P(label=i, true_label=j) such that
np.sum(cj) == len(labels) and np.sum(cj, axis = 1) == np.bincount(labels).
return_indices_of_off_diagonals: bool, default = False
If true returns indices of examples that were counted in off-diagonals
of confident joint as a baseline proxy for the label issues. This
sometimes works as well as filter.find_label_issues(confident_joint)."""
# Assumes all classes are represented in labels: [0, 1, 2, ... num_classes - 1]
unique_classes = range(get_num_classes(labels=labels, pred_probs=pred_probs))
if thresholds is None:
# the avg probability of class given that the label is represented.
thresholds = get_confident_thresholds(labels, pred_probs, multi_label=True)
# N x K matrix. Create mask for every example if for each class, prob >= threshold
pred_probs_bool = pred_probs >= thresholds
# Compute confident joint
# K x N matrix. (no need to avoid collisions for multi-label, double counting is okay!)
k_in_l = np.array([[k in lst for lst in labels] for k in unique_classes])
# K x K matrix. Num examples with given label k where prob >= threshold for each (true) label k
confident_joint = np.array([pred_probs_bool[k_in_l[k]].sum(axis=0) for k in unique_classes])
# Guarantee at least one correctly labeled example is represented in every class
np.fill_diagonal(confident_joint, confident_joint.diagonal().clip(min=1))
if calibrate:
confident_joint = calibrate_confident_joint(confident_joint, labels, multi_label=True)
if return_indices_of_off_diagonals:
# Todo add more tests and consider refactoring for efficiency (combine with confident_joint)
# Convert boolean mask of k_in_l to indices of examples that include class k in the labels
indices_k_in_l = [np.arange(len(labels))[k_in_l[k]] for k in unique_classes]
# Find boolean mask where the thresholds exceed for non-given class, for each class k
issues_mask = [
np.any(np.delete(pred_probs_bool, k, axis=1)[k_in_l[k]], axis=1) for k in unique_classes
]
# Map boolean mask to the indices of the examples, for each class k
indices_of_issues_list = [indices_k_in_l[k][issues_mask[k]] for k in unique_classes]
# Combine error indices for each class k into a single set of all the indices of errors
indices_of_issues = np.asarray(
list(set([z for lst in indices_of_issues_list for z in lst]))
)
return confident_joint, sorted(indices_of_issues)
return confident_joint
[docs]def compute_confident_joint(
labels,
pred_probs,
*,
thresholds=None,
calibrate=True,
multi_label=False,
return_indices_of_off_diagonals=False,
) -> Union[np.ndarray, Tuple[np.ndarray, list]]:
"""Estimates the confident counts of latent true vs observed noisy labels
for the examples in our dataset. This array of shape ``(K, K)`` is called the **confident joint**
and contains counts of examples in every class, confidently labeled as every other class.
These counts may subsequently be used to estimate the joint distribution of true and noisy labels
(by normalizing them to frequencies).
Important: this function assumes that `pred_probs` are out-of-sample
holdout probabilities. This can be :ref:`done with cross validation <pred_probs_cross_val>`. If
the probabilities are not computed out-of-sample, overfitting may occur.
Parameters
----------
labels : np.ndarray
An array of shape ``(N,)`` of noisy labels, i.e. some labels may be erroneous.
Elements must be in the set 0, 1, ..., K-1, where K is the number of classes.
``len(set(labels)) == pred_probs.shape[1]`` for standard multi-class classification with single-labeled data (e.g. ``labels = [1,0,2,1,1,0...]``).
For multi-label classification where each example can belong to multiple classes(e.g. ``labels = [[1,2],[1],[0],..]``),
your labels should instead satisfy: ``len(set(k for l in labels for k in l)) == pred_probs.shape[1])``.
pred_probs : np.ndarray, optional
An array of shape ``(N, K)`` of model-predicted probabilities,
``P(label=k|x)``. Each row of this matrix corresponds
to an example `x` and contains the model-predicted probabilities that
`x` belongs to each possible class, for each of the K classes. The
columns must be ordered such that these probabilities correspond to
class 0, 1, ..., K-1. `pred_probs` should have been computed using 3 (or
higher) fold cross-validation.
thresholds : array_like, optional
An array of shape ``(K, 1)`` or ``(K,)`` of per-class threshold
probabilities, used to determine the cutoff probability necessary to
consider an example as a given class label (see `Northcutt et al.,
2021 <https://jair.org/index.php/jair/article/view/12125>`_, Section
3.1, Equation 2).
This is for advanced users only. If not specified, these are computed
for you automatically. If an example has a predicted probability
greater than this threshold, it is counted as having true_label =
k. This is not used for pruning/filtering, only for estimating the
noise rates using confident counts.
calibrate : bool, default=True
Calibrates confident joint estimate ``P(label=i, true_label=j)`` such that
``np.sum(cj) == len(labels)`` and ``np.sum(cj, axis = 1) == np.bincount(labels)``.
When ``calibrate=True``, this method returns an estimate of
the latent true joint counts of noisy and true labels.
multi_label : bool, optional
If ``True``, labels should be an iterable (e.g. list) of iterables, containing a
list of labels for each example, instead of just a single label.
The multi-label setting supports classification tasks where an example has 1 or more labels.
Example of a multi-labeled `labels` input: ``[[0,1], [1], [0,2], [0,1,2], [0], [1], ...]``.
The major difference in how this is calibrated versus single-label is that
the total number of errors considered is based on the number of labels,
not the number of examples. So, the calibrated `confident_joint` will sum
to the number of total labels.
return_indices_of_off_diagonals : bool, optional
If ``True``, returns indices of examples that were counted in off-diagonals
of confident joint as a baseline proxy for the label issues. This
sometimes works as well as ``filter.find_label_issues(confident_joint)``.
Returns
-------
confident_joint_counts : np.ndarray
An array of shape ``(K, K)`` representing counts of examples
for which we are confident about their given and true label.
If `return_indices_of_off_diagonals` is ``True``, `confident_joint_counts` is the first element of returned tuple
and second element is another array of indices counted in off-diagonals of confident joint.
Note
----
We provide a for-loop based simplification of the confident joint
below. This implementation is not efficient, not used in practice, and
not complete, but covers the gist of how the confident joint is computed:
.. code:: python
# Confident examples are those that we are confident have true_label = k
# Estimate (K, K) matrix of confident examples with label = k_s and true_label = k_y
cj_ish = np.zeros((K, K))
for k_s in range(K): # k_s is the class value k of noisy labels `s`
for k_y in range(K): # k_y is the (guessed) class k of true_label k_y
cj_ish[k_s][k_y] = sum((pred_probs[:,k_y] >= (thresholds[k_y] - 1e-8)) & (labels == k_s))
The following is a vectorized (but non-parallelized) implementation of the
confident joint, again slow, using for-loops/simplified for understanding.
This implementation is 100% accurate, it's just not optimized for speed.
.. code:: python
confident_joint = np.zeros((K, K), dtype = int)
for i, row in enumerate(pred_probs):
s_label = labels[i]
confident_bins = row >= thresholds - 1e-6
num_confident_bins = sum(confident_bins)
if num_confident_bins == 1:
confident_joint[s_label][np.argmax(confident_bins)] += 1
elif num_confident_bins > 1:
confident_joint[s_label][np.argmax(row)] += 1
"""
if multi_label:
return _compute_confident_joint_multi_label(
labels=labels,
pred_probs=pred_probs,
thresholds=thresholds,
calibrate=calibrate,
return_indices_of_off_diagonals=return_indices_of_off_diagonals,
)
# labels needs to be a numpy array
labels = np.asarray(labels)
# Estimate the probability thresholds for confident counting
if thresholds is None:
# P(we predict the given noisy label is k | given noisy label is k)
thresholds = get_confident_thresholds(labels, pred_probs, multi_label=multi_label)
thresholds = np.asarray(thresholds)
# Compute confident joint (vectorized for speed).
# pred_probs_bool is a bool matrix where each row represents a training example as a boolean vector of
# size num_classes, with True if the example confidently belongs to that class and False if not.
pred_probs_bool = pred_probs >= thresholds - 1e-6
num_confident_bins = pred_probs_bool.sum(axis=1)
at_least_one_confident = num_confident_bins > 0
more_than_one_confident = num_confident_bins > 1
pred_probs_argmax = pred_probs.argmax(axis=1)
# Note that confident_argmax is meaningless for rows of all False
confident_argmax = pred_probs_bool.argmax(axis=1)
# For each example, choose the confident class (greater than threshold)
# When there is 2+ confident classes, choose the class with largest prob.
true_label_guess = np.where(
more_than_one_confident,
pred_probs_argmax,
confident_argmax,
)
# true_labels_confident omits meaningless all-False rows
true_labels_confident = true_label_guess[at_least_one_confident]
labels_confident = labels[at_least_one_confident]
confident_joint = confusion_matrix(true_labels_confident, labels_confident).T
# Guarantee at least one correctly labeled example is represented in every class
np.fill_diagonal(confident_joint, confident_joint.diagonal().clip(min=1))
if calibrate:
confident_joint = calibrate_confident_joint(confident_joint, labels)
if return_indices_of_off_diagonals:
true_labels_neq_given_labels = true_labels_confident != labels_confident
indices = np.arange(len(labels))[at_least_one_confident][true_labels_neq_given_labels]
return confident_joint, indices
return confident_joint
[docs]def estimate_latent(
confident_joint,
labels,
*,
py_method="cnt",
converge_latent_estimates=False,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Computes the latent prior ``p(y)``, the noise matrix ``P(labels|y)`` and the
inverse noise matrix ``P(y|labels)`` from the `confident_joint` ``count(labels, y)``. The
`confident_joint` can be estimated by `compute_confident_joint <cleanlab.count.compute_confident_joint>`
by counting confident examples.
Parameters
----------
confident_joint : np.ndarray
An array of shape ``(K, K)`` representing the confident joint, the matrix used for identifying label issues, which
estimates a confident subset of the joint distribution of the noisy and true labels, ``P_{noisy label, true label}``.
Entry ``(j, k)`` in the matrix is the number of examples confidently counted into the pair of ``(noisy label=j, true label=k)`` classes.
The `confident_joint` can be computed using :py:func:`count.compute_confident_joint <cleanlab.count.compute_confident_joint>`.
If not provided, it is computed from the given (noisy) `labels` and `pred_probs`.
labels : np.ndarray
An array of shape ``(N,)`` of noisy labels, i.e. some labels may be erroneous.
Elements must be in the set 0, 1, ..., K-1, where K is the number of classes.
py_method : {"cnt", "eqn", "marginal", "marginal_ps"}, default="cnt"
`py` is shorthand for the "class proportions (a.k.a prior) of the true labels".
This method defines how to compute the latent prior ``p(true_label=k)``. Default is ``"cnt"``,
which works well even when the noise matrices are estimated poorly by using
the matrix diagonals instead of all the probabilities.
converge_latent_estimates : bool, optional
If ``True``, forces numerical consistency of estimates. Each is estimated
independently, but they are related mathematically with closed form
equivalences. This will iteratively make them mathematically consistent.
Returns
------
tuple
A tuple containing (py, noise_matrix, inv_noise_matrix)."""
# 'ps' is p(labels=k)
ps = value_counts(labels) / float(len(labels))
# Number of training examples confidently counted from each noisy class
labels_class_counts = confident_joint.sum(axis=1).astype(float)
# Number of training examples confidently counted into each true class
true_labels_class_counts = confident_joint.sum(axis=0).astype(float)
# p(label=k_s|true_label=k_y) ~ |label=k_s and true_label=k_y| / |true_label=k_y|
noise_matrix = confident_joint / true_labels_class_counts
# p(true_label=k_y|label=k_s) ~ |true_label=k_y and label=k_s| / |label=k_s|
inv_noise_matrix = confident_joint.T / labels_class_counts
# Compute the prior p(y), the latent (uncorrupted) class distribution.
py = compute_py(
ps,
noise_matrix,
inv_noise_matrix,
py_method=py_method,
true_labels_class_counts=true_labels_class_counts,
)
# Clip noise rates to be valid probabilities.
noise_matrix = clip_noise_rates(noise_matrix)
inv_noise_matrix = clip_noise_rates(inv_noise_matrix)
# Make latent estimates mathematically agree in their algebraic relations.
if converge_latent_estimates:
py, noise_matrix, inv_noise_matrix = _converge_estimates(
ps, py, noise_matrix, inv_noise_matrix
)
# Again clip py and noise rates into proper range [0,1)
py = clip_values(py, low=1e-5, high=1.0, new_sum=1.0)
noise_matrix = clip_noise_rates(noise_matrix)
inv_noise_matrix = clip_noise_rates(inv_noise_matrix)
return py, noise_matrix, inv_noise_matrix
[docs]def estimate_py_and_noise_matrices_from_probabilities(
labels,
pred_probs,
*,
thresholds=None,
converge_latent_estimates=True,
py_method="cnt",
calibrate=True,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Computes the confident counts
estimate of latent variables `py` and the noise rates
using observed labels and predicted probabilities, `pred_probs`.
Important: this function assumes that `pred_probs` are out-of-sample
holdout probabilities. This can be :ref:`done with cross validation <pred_probs_cross_val>`. If
the probabilities are not computed out-of-sample, overfitting may occur.
This function estimates the `noise_matrix` of shape ``(K, K)``. This is the
fraction of examples in every class, labeled as every other class. The
`noise_matrix` is a conditional probability matrix for ``P(label=k_s|true_label=k_y)``.
Under certain conditions, estimates are exact, and in most
conditions, estimates are within one percent of the actual noise rates.
Parameters
----------
labels : np.ndarray
An array of shape ``(N,)`` of noisy labels, i.e. some labels may be erroneous.
Elements must be in the set 0, 1, ..., K-1, where K is the number of classes.
pred_probs : np.ndarray
An array of shape ``(N, K)`` of model-predicted probabilities,
``P(label=k|x)``. Each row of this matrix corresponds
to an example `x` and contains the model-predicted probabilities that
`x` belongs to each possible class, for each of the K classes. The
columns must be ordered such that these probabilities correspond to
class 0, 1, ..., K-1. `pred_probs` should have been computed using 3 (or
higher) fold cross-validation.
thresholds : array_like, optional
An array of shape ``(K, 1)`` or ``(K,)`` of per-class threshold
probabilities, used to determine the cutoff probability necessary to
consider an example as a given class label (see `Northcutt et al.,
2021 <https://jair.org/index.php/jair/article/view/12125>`_, Section
3.1, Equation 2).
This is for advanced users only. If not specified, these are computed
for you automatically. If an example has a predicted probability
greater than this threshold, it is counted as having true_label =
k. This is not used for pruning/filtering, only for estimating the
noise rates using confident counts.
converge_latent_estimates : bool, optional
If ``True``, forces numerical consistency of estimates. Each is estimated
independently, but they are related mathematically with closed form
equivalences. This will iteratively make them mathematically consistent.
py_method : {"cnt", "eqn", "marginal", "marginal_ps"}, default="cnt"
How to compute the latent prior ``p(true_label=k)``. Default is ``"cnt"`` as it often
works well even when the noise matrices are estimated poorly by using
the matrix diagonals instead of all the probabilities.
calibrate : bool, default=True
Calibrates confident joint estimate ``P(label=i, true_label=j)`` such that
``np.sum(cj) == len(labels)`` and ``np.sum(cj, axis = 1) == np.bincount(labels)``.
Returns
------
estimates : tuple
A tuple of arrays: (`py`, `noise_matrix`, `inverse_noise_matrix`, `confident_joint`)."""
confident_joint = compute_confident_joint(
labels=labels,
pred_probs=pred_probs,
thresholds=thresholds,
calibrate=calibrate,
)
py, noise_matrix, inv_noise_matrix = estimate_latent(
confident_joint=confident_joint,
labels=labels,
py_method=py_method,
converge_latent_estimates=converge_latent_estimates,
)
assert isinstance(confident_joint, np.ndarray)
return py, noise_matrix, inv_noise_matrix, confident_joint
[docs]def estimate_confident_joint_and_cv_pred_proba(
X,
labels,
clf=LogReg(multi_class="auto", solver="lbfgs"),
*,
cv_n_folds=5,
thresholds=None,
seed=None,
calibrate=True,
clf_kwargs={},
validation_func=None,
) -> Tuple[np.ndarray, np.ndarray]:
"""Estimates ``P(labels, y)``, the confident counts of the latent
joint distribution of true and noisy labels
using observed `labels` and predicted probabilities `pred_probs`.
The output of this function is an array of shape ``(K, K)``.
Under certain conditions, estimates are exact, and in many
conditions, estimates are within one percent of actual.
Notes: There are two ways to compute the confident joint with pros/cons.
(1) For each holdout set, we compute the confident joint, then sum them up.
(2) Compute pred_proba for each fold, combine, compute the confident joint.
(1) is more accurate because it correctly computes thresholds for each fold
(2) is more accurate when you have only a little data because it computes
the confident joint using all the probabilities. For example if you had 100
examples, with 5-fold cross validation + uniform p(y) you would only have 20
examples to compute each confident joint for (1). Such small amounts of data
is bound to result in estimation errors. For this reason, we implement (2),
but we implement (1) as a commented out function at the end of this file.
Parameters
----------
X : np.ndarray or pd.DataFrame
Input feature matrix of shape ``(N, ...)``, where N is the number of
examples. The classifier that this instance was initialized with,
``clf``, must be able to fit() and predict() data with this format.
labels : np.ndarray or pd.Series
An array of shape ``(N,)`` of noisy labels, i.e. some labels may be erroneous.
Elements must be in (0, 1, ..., K-1) where K is the number of classes,
and all classes must be present at least once.
clf : estimator instance, optional
A classifier implementing the `sklearn estimator API
<https://scikit-learn.org/stable/developers/develop.html#rolling-your-own-estimator>`_.
cv_n_folds : int, default=5
The number of cross-validation folds used to compute
out-of-sample probabilities for each example in `X`.
thresholds : array_like, optional
An array of shape ``(K, 1)`` or ``(K,)`` of per-class threshold
probabilities, used to determine the cutoff probability necessary to
consider an example as a given class label (see `Northcutt et al.,
2021 <https://jair.org/index.php/jair/article/view/12125>`_, Section
3.1, Equation 2).
This is for advanced users only. If not specified, these are computed
for you automatically. If an example has a predicted probability
greater than this threshold, it is counted as having true_label =
k. This is not used for pruning/filtering, only for estimating the
noise rates using confident counts.
seed : int, optional
Set the default state of the random number generator used to split
the cross-validated folds. If None, uses np.random current random state.
calibrate : bool, default=True
Calibrates confident joint estimate ``P(label=i, true_label=j)`` such that
``np.sum(cj) == len(labels)`` and ``np.sum(cj, axis = 1) == np.bincount(labels)``.
clf_kwargs : dict, optional
Optional keyword arguments to pass into `clf`'s ``fit()`` method.
validation_func : callable, optional
Specifies how to map the validation data split in cross-validation as input for ``clf.fit()``.
For details, see the documentation of :py:meth:`CleanLearning.fit<cleanlab.classification.CleanLearning.fit>`
Returns
------
estimates : tuple
Tuple of two numpy arrays in the form:
(joint counts matrix, predicted probability matrix)"""
assert_valid_inputs(X, labels)
labels = labels_to_array(labels)
num_classes = get_num_classes(
labels=labels
) # This method definitely only works if all classes are present.
# Create cross-validation object for out-of-sample predicted probabilities.
# CV folds preserve the fraction of noisy positive and
# noisy negative examples in each class.
kf = StratifiedKFold(n_splits=cv_n_folds, shuffle=True, random_state=seed)
# Initialize pred_probs array
pred_probs = np.zeros(shape=(len(labels), num_classes))
# Split X and labels into "cv_n_folds" stratified folds.
# CV indices only require labels: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html
# Only split based on labels because X may have various formats:
for k, (cv_train_idx, cv_holdout_idx) in enumerate(kf.split(X=labels, y=labels)):
try:
clf_copy = sklearn.base.clone(clf) # fresh untrained copy of the model
except Exception:
raise ValueError(
"`clf` must be clonable via: sklearn.base.clone(clf). "
"You can either implement instance method `clf.get_params()` to produce a fresh untrained copy of this model, "
"or you can implement the cross-validation outside of cleanlab "
"and pass in the obtained `pred_probs` to skip cleanlab's internal cross-validation"
)
# Select the training and holdout cross-validated sets.
X_train_cv, X_holdout_cv, s_train_cv, s_holdout_cv = train_val_split(
X, labels, cv_train_idx, cv_holdout_idx
)
# dict with keys: which classes missing, values: index of holdout data from this class that is duplicated:
missing_class_inds = {}
is_tf_or_torch_dataset = is_torch_dataset(X) or is_tensorflow_dataset(X)
if not is_tf_or_torch_dataset:
# Ensure no missing classes in training set.
train_cv_classes = set(s_train_cv)
all_classes = set(range(num_classes))
if len(train_cv_classes) != len(all_classes):
missing_classes = all_classes.difference(train_cv_classes)
warnings.warn(
"Duplicated some data across multiple folds to ensure training does not fail "
f"because these classes do not have enough data for proper cross-validation: {missing_classes}."
)
for missing_class in missing_classes:
# Duplicate one instance of missing_class from holdout data to the training data:
holdout_inds = np.where(s_holdout_cv == missing_class)[0]
dup_idx = holdout_inds[0]
s_train_cv = np.append(s_train_cv, s_holdout_cv[dup_idx])
# labels are always np.ndarray so don't have to consider .iloc above
X_train_cv = append_extra_datapoint(
to_data=X_train_cv, from_data=X_holdout_cv, index=dup_idx
)
missing_class_inds[missing_class] = dup_idx
# Map validation data into appropriate format to pass into classifier clf
if validation_func is None:
validation_kwargs = {}
elif callable(validation_func):
validation_kwargs = validation_func(X_holdout_cv, s_holdout_cv)
else:
raise TypeError("validation_func must be callable function with args: X_val, y_val")
# Fit classifier clf to training set, predict on holdout set, and update pred_probs.
clf_copy.fit(X_train_cv, s_train_cv, **clf_kwargs, **validation_kwargs)
pred_probs_cv = clf_copy.predict_proba(X_holdout_cv) # P(labels = k|x) # [:,1]
# Replace predictions for duplicated indices with dummy predictions:
for missing_class in missing_class_inds:
dummy_pred = np.zeros(pred_probs_cv[0].shape)
dummy_pred[missing_class] = 1.0 # predict given label with full confidence
dup_idx = missing_class_inds[missing_class]
pred_probs_cv[dup_idx] = dummy_pred
pred_probs[cv_holdout_idx] = pred_probs_cv
# Compute the confident counts, a num_classes x num_classes matrix for all pairs of labels.
confident_joint = compute_confident_joint(
labels=labels,
pred_probs=pred_probs, # P(labels = k|x)
thresholds=thresholds,
calibrate=calibrate,
)
assert isinstance(confident_joint, np.ndarray)
assert isinstance(pred_probs, np.ndarray)
return confident_joint, pred_probs
[docs]def estimate_py_noise_matrices_and_cv_pred_proba(
X,
labels,
clf=LogReg(multi_class="auto", solver="lbfgs"),
*,
cv_n_folds=5,
thresholds=None,
converge_latent_estimates=False,
py_method="cnt",
seed=None,
clf_kwargs={},
validation_func=None,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""This function computes the out-of-sample predicted
probability ``P(label=k|x)`` for every example x in `X` using cross
validation while also computing the confident counts noise
rates within each cross-validated subset and returning
the average noise rate across all examples.
This function estimates the `noise_matrix` of shape ``(K, K)``. This is the
fraction of examples in every class, labeled as every other class. The
`noise_matrix` is a conditional probability matrix for ``P(label=k_s|true_label=k_y)``.
Under certain conditions, estimates are exact, and in most
conditions, estimates are within one percent of the actual noise rates.
Parameters
----------
X : np.ndarray
Input feature matrix of shape ``(N, ...)``, where N is the number of
examples. The classifier that this instance was initialized with,
`clf`, must be able to handle data with this shape.
labels : np.ndarray
An array of shape ``(N,)`` of noisy labels, i.e. some labels may be erroneous.
Elements must be in the set 0, 1, ..., K-1, where K is the number of classes.
clf : estimator instance, optional
A classifier implementing the `sklearn estimator API
<https://scikit-learn.org/stable/developers/develop.html#rolling-your-own-estimator>`_.
cv_n_folds : int, default=5
The number of cross-validation folds used to compute
out-of-sample probabilities for each example in `X`.
thresholds : array_like, optional
An array of shape ``(K, 1)`` or ``(K,)`` of per-class threshold
probabilities, used to determine the cutoff probability necessary to
consider an example as a given class label (see `Northcutt et al.,
2021 <https://jair.org/index.php/jair/article/view/12125>`_, Section
3.1, Equation 2).
This is for advanced users only. If not specified, these are computed
for you automatically. If an example has a predicted probability
greater than this threshold, it is counted as having true_label =
k. This is not used for pruning/filtering, only for estimating the
noise rates using confident counts.
converge_latent_estimates : bool, optional
If ``True``, forces numerical consistency of estimates. Each is estimated
independently, but they are related mathematically with closed form
equivalences. This will iteratively make them mathematically consistent.
py_method : {"cnt", "eqn", "marginal", "marginal_ps"}, default="cnt"
How to compute the latent prior ``p(true_label=k)``. Default is ``"cnt"`` as it often
works well even when the noise matrices are estimated poorly by using
the matrix diagonals instead of all the probabilities.
seed : int, optional
Set the default state of the random number generator used to split
the cross-validated folds. If ``None``, uses ``np.random`` current random state.
clf_kwargs : dict, optional
Optional keyword arguments to pass into `clf`'s ``fit()`` method.
validation_func : callable, optional
Specifies how to map the validation data split in cross-validation as input for ``clf.fit()``.
For details, see the documentation of :py:meth:`CleanLearning.fit<cleanlab.classification.CleanLearning.fit>`
Returns
------
estimates: tuple
A tuple of five arrays (py, noise matrix, inverse noise matrix, confident joint, predicted probability matrix).
"""
confident_joint, pred_probs = estimate_confident_joint_and_cv_pred_proba(
X=X,
labels=labels,
clf=clf,
cv_n_folds=cv_n_folds,
thresholds=thresholds,
seed=seed,
clf_kwargs=clf_kwargs,
validation_func=validation_func,
)
py, noise_matrix, inv_noise_matrix = estimate_latent(
confident_joint=confident_joint,
labels=labels,
py_method=py_method,
converge_latent_estimates=converge_latent_estimates,
)
return py, noise_matrix, inv_noise_matrix, confident_joint, pred_probs
[docs]def estimate_cv_predicted_probabilities(
X,
labels,
clf=LogReg(multi_class="auto", solver="lbfgs"),
*,
cv_n_folds=5,
seed=None,
clf_kwargs={},
validation_func=None,
) -> np.ndarray:
"""This function computes the out-of-sample predicted
probability [P(label=k|x)] for every example in X using cross
validation. Output is a np.ndarray of shape (N, K) where N is
the number of training examples and K is the number of classes.
Parameters
----------
X : np.ndarray
Input feature matrix of shape ``(N, ...)``, where N is the number of
examples. The classifier that this instance was initialized with,
`clf`, must be able to handle data with this shape.
labels : np.ndarray
An array of shape ``(N,)`` of noisy labels, i.e. some labels may be erroneous.
Elements must be in the set 0, 1, ..., K-1, where K is the number of classes.
clf : estimator instance, optional
A classifier implementing the `sklearn estimator API
<https://scikit-learn.org/stable/developers/develop.html#rolling-your-own-estimator>`_.
cv_n_folds : int, default=5
The number of cross-validation folds used to compute
out-of-sample probabilities for each example in `X`.
seed : int, optional
Set the default state of the random number generator used to split
the cross-validated folds. If ``None``, uses ``np.random`` current random state.
clf_kwargs : dict, optional
Optional keyword arguments to pass into `clf`'s ``fit()`` method.
validation_func : callable, optional
Specifies how to map the validation data split in cross-validation as input for ``clf.fit()``.
For details, see the documentation of :py:meth:`CleanLearning.fit<cleanlab.classification.CleanLearning.fit>`
Returns
--------
pred_probs : np.ndarray
An array of shape ``(N, K)`` representing ``P(label=k|x)``, the model-predicted probabilities.
Each row of this matrix corresponds to an example `x` and contains the model-predicted
probabilities that `x` belongs to each possible class.
"""
return estimate_py_noise_matrices_and_cv_pred_proba(
X=X,
labels=labels,
clf=clf,
cv_n_folds=cv_n_folds,
seed=seed,
clf_kwargs=clf_kwargs,
validation_func=validation_func,
)[-1]
[docs]def estimate_noise_matrices(
X,
labels,
clf=LogReg(multi_class="auto", solver="lbfgs"),
*,
cv_n_folds=5,
thresholds=None,
converge_latent_estimates=True,
seed=None,
clf_kwargs={},
validation_func=None,
) -> Tuple[np.ndarray, np.ndarray]:
"""Estimates the `noise_matrix` of shape ``(K, K)``. This is the
fraction of examples in every class, labeled as every other class. The
`noise_matrix` is a conditional probability matrix for ``P(label=k_s|true_label=k_y)``.
Under certain conditions, estimates are exact, and in most
conditions, estimates are within one percent of the actual noise rates.
Parameters
----------
X : np.ndarray
Input feature matrix of shape ``(N, ...)``, where N is the number of
examples. The classifier that this instance was initialized with,
`clf`, must be able to handle data with this shape.
labels : np.ndarray
An array of shape ``(N,)`` of noisy labels, i.e. some labels may be erroneous.
Elements must be in the set 0, 1, ..., K-1, where K is the number of classes.
clf : estimator instance, optional
A classifier implementing the `sklearn estimator API
<https://scikit-learn.org/stable/developers/develop.html#rolling-your-own-estimator>`_.
cv_n_folds : int, default=5
The number of cross-validation folds used to compute
out-of-sample probabilities for each example in `X`.
thresholds : array_like, optional
An array of shape ``(K, 1)`` or ``(K,)`` of per-class threshold
probabilities, used to determine the cutoff probability necessary to
consider an example as a given class label (see `Northcutt et al.,
2021 <https://jair.org/index.php/jair/article/view/12125>`_, Section
3.1, Equation 2).
This is for advanced users only. If not specified, these are computed
for you automatically. If an example has a predicted probability
greater than this threshold, it is counted as having true_label =
k. This is not used for pruning/filtering, only for estimating the
noise rates using confident counts.
converge_latent_estimates : bool, optional
If ``True``, forces numerical consistency of estimates. Each is estimated
independently, but they are related mathematically with closed form
equivalences. This will iteratively make them mathematically consistent.
seed : int, optional
Set the default state of the random number generator used to split
the cross-validated folds. If None, uses np.random current random state.
clf_kwargs : dict, optional
Optional keyword arguments to pass into `clf`'s ``fit()`` method.
validation_func : callable, optional
Specifies how to map the validation data split in cross-validation as input for ``clf.fit()``.
For details, see the documentation of :py:meth:`CleanLearning.fit<cleanlab.classification.CleanLearning.fit>`
Returns
------
estimates : tuple
A tuple containing arrays (`noise_matrix`, `inv_noise_matrix`)."""
return estimate_py_noise_matrices_and_cv_pred_proba(
X=X,
labels=labels,
clf=clf,
cv_n_folds=cv_n_folds,
thresholds=thresholds,
converge_latent_estimates=converge_latent_estimates,
seed=seed,
clf_kwargs=clf_kwargs,
validation_func=validation_func,
)[1:-2]
def _converge_estimates(
ps,
py,
noise_matrix,
inverse_noise_matrix,
*,
inv_noise_matrix_iterations=5,
noise_matrix_iterations=3,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Updates py := P(true_label=k) and both `noise_matrix` and `inverse_noise_matrix`
to be numerically consistent with each other, by iteratively updating their estimates based on
the mathematical relationships between them.
Forces numerical consistency of estimates. Each is estimated
independently, but they are related mathematically with closed form
equivalences. This will iteratively make them mathematically consistent.
py := P(true_label=k) and the inverse noise matrix P(true_label=k_y|label=k_s) specify one
another, meaning one can be computed from the other and vice versa.
When numerical discrepancy exists due to poor estimation, they can be made
to agree by repeatedly computing one from the other,
for some a certain number of iterations (3-10 works fine.)
Do not set iterations too high or performance will decrease as small
deviations will get perturbed over and over and potentially magnified.
Note that we have to first converge the inverse_noise_matrix and py,
then we can update the noise_matrix, then repeat. This is because the
inverse noise matrix depends on py (which is unknown/latent), but the
noise matrix depends on ps (which is known), so there will be no change in
the noise matrix if we recompute it when py and inverse_noise_matrix change.
Parameters
----------
ps : np.ndarray (shape (K, ) or (1, K))
The fraction (prior probability) of each observed, NOISY class P(labels = k).
py : np.ndarray (shape (K, ) or (1, K))
The estimated fraction (prior probability) of each TRUE class P(true_label = k).
noise_matrix : np.ndarray of shape (K, K), K = number of classes
A conditional probability matrix of the form P(label=k_s|true_label=k_y) containing
the fraction of examples in every class, labeled as every other class.
Assumes columns of noise_matrix sum to 1.
inverse_noise_matrix : np.ndarray of shape (K, K), K = number of classes
A conditional probability matrix of the form P(true_label=k_y|labels=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 pred_probs and labels.
Assumes columns of inverse_noise_matrix sum to 1.
inv_noise_matrix_iterations : int, default = 5
Number of times to converge inverse noise matrix with py and noise mat.
noise_matrix_iterations : int, default = 3
Number of times to converge noise matrix with py and inverse noise mat.
Returns
------
estimates: tuple
Three arrays of the form (`py`, `noise_matrix`, `inverse_noise_matrix`) all
having numerical agreement in terms of their mathematical relations."""
for j in range(noise_matrix_iterations):
for i in range(inv_noise_matrix_iterations):
inverse_noise_matrix = compute_inv_noise_matrix(py=py, noise_matrix=noise_matrix, ps=ps)
py = compute_py(ps, noise_matrix, inverse_noise_matrix)
noise_matrix = compute_noise_matrix_from_inverse(
ps=ps, inverse_noise_matrix=inverse_noise_matrix, py=py
)
return py, noise_matrix, inverse_noise_matrix
[docs]def get_confident_thresholds(
labels: np.ndarray,
pred_probs: np.ndarray,
multi_label: bool = False,
) -> np.ndarray:
"""Returns expected (average) "self-confidence" for each class.
The confident class threshold for a class j is the expected (average) "self-confidence" for class j.
Parameters
----------
labels : np.ndarray
An array of shape ``(N,)`` of noisy labels, i.e. some labels may be erroneous.
Elements must be in the set 0, 1, ..., K-1, where K is the number of classes.
All the classes (0, 1, ..., and K-1) MUST be present in ``labels``, such that:
``len(set(labels)) == pred_probs.shape[1]`` for standard multi-class classification with single-labeled data (e.g. ``labels = [1,0,2,1,1,0...]``).
For multi-label classification where each example can belong to multiple classes(e.g. ``labels = [[1,2],[1],[0],..]``),
your labels should instead satisfy: ``len(set(k for l in labels for k in l)) == pred_probs.shape[1])``.
pred_probs : np.ndarray
An array of shape ``(N, K)`` of model-predicted probabilities,
``P(label=k|x)``. Each row of this matrix corresponds
to an example `x` and contains the model-predicted probabilities that
`x` belongs to each possible class, for each of the K classes. The
columns must be ordered such that these probabilities correspond to
class 0, 1, ..., K-1. `pred_probs` should have been computed using 3 (or
higher) fold cross-validation.
multi_label : bool, optional
If ``True``, labels should be an iterable (e.g. list) of iterables, containing a
list of labels for each example, instead of just a single label.
Assumes all classes in pred_probs.shape[1] are represented in labels.
The multi-label setting supports classification tasks where an example has 1 or more labels.
Example of a multi-labeled `labels` input: ``[[0,1], [1], [0,2], [0,1,2], [0], [1], ...]``.
The major difference in how this is calibrated versus single-label is that
the total number of errors considered is based on the number of labels,
not the number of examples. So, the calibrated `confident_joint` will sum
to the number of total labels.
Returns
-------
confident_thresholds : np.ndarray
An array of shape ``(K, )`` where K is the number of classes."""
# Assumes all classes are represented in labels: [0, 1, 2, ... num_classes - 1]
unique_classes = range(
get_num_classes(labels=labels, pred_probs=pred_probs, multi_label=multi_label)
)
if multi_label:
# Compute thresholds = p(label=k | k in set of given labels)
k_in_l = np.array([[k in lst for lst in labels] for k in unique_classes])
# The avg probability of class given that the label is represented.
confident_thresholds = np.array(
[np.mean(pred_probs[:, k][k_in_l[k]]) for k in unique_classes]
)
else:
confident_thresholds = np.array(
[np.mean(pred_probs[:, k][labels == k]) for k in unique_classes]
)
return confident_thresholds
```