# Source code for cleanlab.classification

```
# Copyright (C) 2017-2023 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/>.
"""
cleanlab can be used for learning with noisy labels for any dataset and model.
For regular (multi-class) classification tasks,
the :py:class:`CleanLearning <cleanlab.classification.CleanLearning>` class wraps an instance of an
sklearn classifier. The wrapped classifier must adhere to the `sklearn estimator API
<https://scikit-learn.org/stable/developers/develop.html#rolling-your-own-estimator>`_,
meaning it must define four functions:
* ``clf.fit(X, y, sample_weight=None)``
* ``clf.predict_proba(X)``
* ``clf.predict(X)``
* ``clf.score(X, y, sample_weight=None)``
where `X` contains data (i.e. features), `y` contains labels (with elements in 0, 1, ..., K-1,
where K is the number of classes). The first index of `X` and of `y` should correspond to the different examples in the dataset,
such that ``len(X) = len(y) = N`` (sample-size). Here `sample_weight` re-weights examples in
the loss function while training (supporting `sample_weight` in your classifier is recommended but optional).
Furthermore, your estimator should be correctly clonable via
`sklearn.base.clone <https://scikit-learn.org/stable/modules/generated/sklearn.base.clone.html>`_:
cleanlab internally creates multiple instances of the
estimator, and if you e.g. manually wrap a PyTorch model, you must ensure that
every call to the estimator's ``__init__()`` creates an independent instance of
the model (for sklearn compatibility, the weights of neural network models should typically be initialized inside of ``clf.fit()``).
Note
----
There are two new notions of confidence in this package:
1. Confident *examples* --- examples we are confident are labeled correctly.
We prune everything else. Mathematically, this means keeping the examples
with high probability of belong to their provided label class.
2. Confident *errors* --- examples we are confident are labeled erroneously.
We prune these. Mathematically, this means pruning the examples with
high probability of belong to a different class.
Examples
--------
>>> from cleanlab.classification import CleanLearning
>>> from sklearn.linear_model import LogisticRegression as LogReg
>>> cl = CleanLearning(clf=LogReg()) # Pass in any classifier.
>>> cl.fit(X_train, labels_maybe_with_errors)
>>> # Estimate the predictions as if you had trained without label issues.
>>> pred = cl.predict(X_test)
If the model is not sklearn-compatible by default, it might be the case that
standard packages can adapt the model. For example, you can adapt PyTorch
models using `skorch <https://skorch.readthedocs.io/>`_ and adapt Keras models
using `SciKeras <https://www.adriangb.com/scikeras/>`_.
If an open-source adapter doesn't already exist, you can manually wrap the
model to be sklearn-compatible. This is made easy by inheriting from
`sklearn.base.BaseEstimator
<https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html>`_:
.. code:: python
from sklearn.base import BaseEstimator
class YourModel(BaseEstimator):
def __init__(self, ):
pass
def fit(self, X, y, sample_weight=None):
pass
def predict(self, X):
pass
def predict_proba(self, X):
pass
def score(self, X, y, sample_weight=None):
pass
Note
----
* `labels` refers to the given labels in the original dataset, which may have errors
* labels must be integers in 0, 1, ..., K-1, where K is the total number of classes
Note
----
Confident learning is the state-of-the-art (`Northcutt et al., 2021 <https://jair.org/index.php/jair/article/view/12125>`_) for
weak supervision, finding label issues in datasets, learning with noisy
labels, uncertainty estimation, and more. It works with *any* classifier,
including deep neural networks. See the `clf` parameter.
Confident learning is a subfield of theory and algorithms of machine learning with noisy labels.
Cleanlab achieves state-of-the-art performance of any open-sourced implementation of confident
learning across a variety of tasks like multi-class classification, multi-label classification,
and PU learning.
Given any classifier having the `predict_proba` method, an input feature
matrix `X`, and a discrete vector of noisy labels `labels`, confident learning estimates the
classifications that would be obtained if the *true labels* had instead been provided
to the classifier during training. `labels` denotes the noisy labels instead of
the :math:`\\tilde{y}` used in confident learning paper.
"""
from sklearn.linear_model import LogisticRegression as LogReg
from sklearn.metrics import accuracy_score
from sklearn.base import BaseEstimator
import numpy as np
import pandas as pd
import inspect
import warnings
from typing import Optional, TYPE_CHECKING
if TYPE_CHECKING: # pragma: no cover
from typing_extensions import Self
from cleanlab.rank import get_label_quality_scores
from cleanlab import filter
from cleanlab.internal.util import (
value_counts,
compress_int_array,
subset_X_y,
get_num_classes,
)
from cleanlab.count import (
estimate_py_noise_matrices_and_cv_pred_proba,
estimate_py_and_noise_matrices_from_probabilities,
estimate_cv_predicted_probabilities,
estimate_latent,
compute_confident_joint,
)
from cleanlab.internal.latent_algebra import (
compute_py_inv_noise_matrix,
compute_noise_matrix_from_inverse,
)
from cleanlab.internal.validation import (
assert_valid_inputs,
labels_to_array,
)
[docs]class CleanLearning(BaseEstimator): # Inherits sklearn classifier
"""
CleanLearning = Machine Learning with cleaned data (even when training on messy, error-ridden data).
Automated and robust learning with noisy labels using any dataset and any model. This class
trains a model `clf` with error-prone, noisy labels as if the model had been instead trained
on a dataset with perfect labels. It achieves this by cleaning out the error and providing
cleaned data while training. This class is currently intended for standard (multi-class) classification tasks.
Parameters
----------
clf : estimator instance, optional
A classifier implementing the `sklearn estimator API
<https://scikit-learn.org/stable/developers/develop.html#rolling-your-own-estimator>`_,
defining the following functions:
* ``clf.fit(X, y, sample_weight=None)``
* ``clf.predict_proba(X)``
* ``clf.predict(X)``
* ``clf.score(X, y, sample_weight=None)``
See :py:mod:`cleanlab.experimental` for examples of sklearn wrappers,
e.g. around PyTorch and FastText.
If the model is not sklearn-compatible by default, it might be the case that
standard packages can adapt the model. For example, you can adapt PyTorch
models using `skorch <https://skorch.readthedocs.io/>`_ and adapt Keras models
using `SciKeras <https://www.adriangb.com/scikeras/>`_.
Stores the classifier used in Confident Learning.
Default classifier used is `sklearn.linear_model.LogisticRegression
<https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html>`_.
seed : int, optional
Set the default state of the random number generator used to split
the cross-validated folds. By default, uses `np.random` current random state.
cv_n_folds : int, default=5
This class needs holdout predicted probabilities for every data example
and if not provided, uses cross-validation to compute them.
`cv_n_folds` sets the number of cross-validation folds used to compute
out-of-sample probabilities for each example in `X`.
converge_latent_estimates : bool, optional
If true, forces numerical consistency of latent estimates. Each is
estimated independently, but they are related mathematically with closed
form equivalences. This will iteratively enforce consistency.
pulearning : {None, 0, 1}, default=None
Only works for 2 class datasets. Set to the integer of the class that is
perfectly labeled (you are certain that there are no errors in that class).
find_label_issues_kwargs : dict, optional
Keyword arguments to pass into :py:func:`filter.find_label_issues
<cleanlab.filter.find_label_issues>`. Options that may especially impact
accuracy include: `filter_by`, `frac_noise`, `min_examples_per_class`.
label_quality_scores_kwargs : dict, optional
Keyword arguments to pass into :py:func:`rank.get_label_quality_scores
<cleanlab.rank.get_label_quality_scores>`. Options include: `method`, `adjust_pred_probs`.
verbose : bool, default=False
Controls how much output is printed. Set to ``False`` to suppress print
statements.
"""
def __init__(
self,
clf=None,
*,
seed=None,
# Hyper-parameters (used by .fit() function)
cv_n_folds=5,
converge_latent_estimates=False,
pulearning=None,
find_label_issues_kwargs={},
label_quality_scores_kwargs={},
verbose=False,
):
if clf is None:
# Use logistic regression if no classifier is provided.
clf = LogReg(multi_class="auto", solver="lbfgs")
# Make sure the given classifier has the appropriate methods defined.
if not hasattr(clf, "fit"):
raise ValueError("The classifier (clf) must define a .fit() method.")
if not hasattr(clf, "predict_proba"):
raise ValueError("The classifier (clf) must define a .predict_proba() method.")
if not hasattr(clf, "predict"):
raise ValueError("The classifier (clf) must define a .predict() method.")
if seed is not None:
np.random.seed(seed=seed)
self.clf = clf
self.seed = seed
self.cv_n_folds = cv_n_folds
self.converge_latent_estimates = converge_latent_estimates
self.pulearning = pulearning
self.find_label_issues_kwargs = find_label_issues_kwargs
self.label_quality_scores_kwargs = label_quality_scores_kwargs
self.verbose = verbose
self.label_issues_df = None
self.label_issues_mask = None
self.sample_weight = None
self.confident_joint = None
self.py = None
self.ps = None
self.num_classes = None
self.noise_matrix = None
self.inverse_noise_matrix = None
self.clf_kwargs = None
self.clf_final_kwargs = None
[docs] def fit(
self,
X,
labels=None,
*,
pred_probs=None,
thresholds=None,
noise_matrix=None,
inverse_noise_matrix=None,
label_issues=None,
sample_weight=None,
clf_kwargs={},
clf_final_kwargs={},
validation_func=None,
y=None,
) -> "Self":
"""
Train the model `clf` with error-prone, noisy labels as if
the model had been instead trained on a dataset with the correct labels.
`fit` achieves this by first training `clf` via cross-validation on the noisy data,
using the resulting predicted probabilities to identify label issues,
pruning the data with label issues, and finally training `clf` on the remaining clean data.
Parameters
----------
X : np.ndarray or DatasetLike
Data features (i.e. training inputs for ML), typically an array of shape ``(N, ...)``,
where N is the number of examples.
Supported `DatasetLike` types beyond ``np.ndarray`` include:
``pd.DataFrame``, ``scipy.sparse.csr_matrix``, ``torch.utils.data.Dataset``, ``tensorflow.data.Dataset``,
or any dataset object ``X`` that supports list-based indexing:
``X[index_list]`` to select a subset of training examples.
Your classifier that this instance was initialized with,
``clf``, must be able to ``fit()`` and ``predict()`` data of this format.
Note
----
If providing `X` as a ``tensorflow.data.Dataset``,
make sure ``shuffle()`` has been called before ``batch()`` (if shuffling)
and no other order-destroying operation (eg. ``repeat()``) has been applied.
labels : array_like
An array of shape ``(N,)`` of noisy classification labels, where some labels may be erroneous.
Elements must be integers in the set 0, 1, ..., K-1, where K is the number of classes.
Supported `array_like` types include: ``np.ndarray``, ``pd.Series``, or ``list``.
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 be :ref:`out-of-sample, eg. computed via cross-validation <pred_probs_cross_val>`.
If provided, `pred_probs` will be used to find label issues rather than the ``clf`` classifier.
Note
----
If you are not sure, leave ``pred_probs=None`` (the default) and it
will be computed for you using cross-validation with the provided model.
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.
noise_matrix : np.ndarray, optional
An array of shape ``(K, K)`` representing the conditional probability
matrix ``P(label=k_s | true label=k_y)``, 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, optional
An array of shape ``(K, K)`` representing the conditional probability
matrix ``P(true label=k_y | label=k_s)``,
the estimated fraction observed examples in each class ``k_s``
that are mislabeled examples from every other class ``k_y``,
Assumes columns of `inverse_noise_matrix` sum to 1.
label_issues : pd.DataFrame or np.ndarray, optional
Specifies the label issues for each example in dataset.
If ``pd.DataFrame``, must be formatted as the one returned by:
:py:meth:`CleanLearning.find_label_issues
<cleanlab.classification.CleanLearning.find_label_issues>` or
:py:meth:`CleanLearning.get_label_issues
<cleanlab.classification.CleanLearning.get_label_issues>`.
If ``np.ndarray``, must contain either boolean `label_issues_mask` as output by:
default :py:func:`filter.find_label_issues <cleanlab.filter.find_label_issues>`,
or integer indices as output by
:py:func:`filter.find_label_issues <cleanlab.filter.find_label_issues>`
with its `return_indices_ranked_by` argument specified.
Providing this argument significantly reduces the time this method takes to run by
skipping the slow cross-validation step necessary to find label issues.
Examples identified to have label issues will be
pruned from the data before training the final `clf` model.
Caution: If you provide `label_issues` without having previously called
:py:meth:`self.find_label_issues<cleanlab.classification.CleanLearning.find_label_issues>`,
e.g. as a ``np.ndarray``, then some functionality like training with sample weights may be disabled.
sample_weight : array_like, optional
Array of weights with shape ``(N,)`` that are assigned to individual samples,
assuming total number of examples in dataset is `N`.
If not provided, samples may still be weighted by the estimated noise in the class they are labeled as.
clf_kwargs : dict, optional
Optional keyword arguments to pass into `clf`'s ``fit()`` method.
clf_final_kwargs : dict, optional
Optional extra keyword arguments to pass into the final `clf` ``fit()`` on the cleaned data
but not the `clf` ``fit()`` in each fold of cross-validation on the noisy data.
The final ``fit()`` will also receive `clf_kwargs`,
but these may be overwritten by values in `clf_final_kwargs`.
This can be useful for training differently in the final ``fit()``
than during cross-validation.
validation_func : callable, optional
Optional callable function that takes two arguments, `X_val`, `y_val`, and returns a dict
of keyword arguments passed into to ``clf.fit()`` which may be functions of the validation
data in each cross-validation fold. Specifies how to map the validation data split in each
cross-validation fold into the appropriate format to pass into `clf`'s ``fit()`` method, assuming
``clf.fit()`` can utilize validation data if it is appropriately passed in (eg. for early-stopping).
Eg. if your model's ``fit()`` method is called using ``clf.fit(X, y, X_validation, y_validation)``,
then you could set ``validation_func = f`` where
``def f(X_val, y_val): return {"X_validation": X_val, "y_validation": y_val}``
Note that `validation_func` will be ignored in the final call to `clf.fit()` on the
cleaned subset of the data. This argument is only for allowing `clf` to access the
validation data in each cross-validation fold (eg. for early-stopping or hyperparameter-selection
purposes). If you want to pass in validation data even in the final training call to ``clf.fit()``
on the cleaned data subset, you should explicitly pass in that data yourself
(eg. via `clf_final_kwargs` or `clf_kwargs`).
y: array_like, optional
Alternative argument that can be specified instead of `labels`.
Specifying `y` has the same effect as specifying `labels`,
and is offered as an alternative for compatibility with sklearn.
Returns
-------
self : CleanLearning
Fitted estimator that has all the same methods as any sklearn estimator.
After calling ``self.fit()``, this estimator also stores extra attributes such as:
* *self.label_issues_df*: a ``pd.DataFrame`` accessible via
:py:meth:`get_label_issues
<cleanlab.classification.CleanLearning.get_label_issues>`
of similar format as the one returned by: :py:meth:`CleanLearning.find_label_issues<cleanlab.classification.CleanLearning.find_label_issues>`.
See documentation of :py:meth:`CleanLearning.find_label_issues<cleanlab.classification.CleanLearning.find_label_issues>`
for column descriptions.
After calling ``self.fit()``, `self.label_issues_df` may also contain an extra column:
* *sample_weight*: Numeric values that were used to weight examples during
the final training of `clf` in ``CleanLearning.fit()``.
`sample_weight` column will only be present if automatic sample weights were actually used.
These automatic weights are assigned to each example based on the class it belongs to,
i.e. there are only num_classes unique sample_weight values.
The sample weight for an example belonging to class k is computed as ``1 / p(given_label = k | true_label = k)``.
This sample_weight normalizes the loss to effectively trick `clf` into learning with the distribution
of the true labels by accounting for the noisy data pruned out prior to training on cleaned data.
In other words, examples with label issues were removed, so this weights the data proportionally
so that the classifier trains as if it had all the true labels,
not just the subset of cleaned data left after pruning out the label issues.
Note
----
If ``CleanLearning.fit()`` does not work for your data/model, you can run the same procedure yourself:
* Utilize :ref:`cross-validation <pred_probs_cross_val>` to get out-of-sample `pred_probs` for each example.
* Call :py:func:`filter.find_label_issues <cleanlab.filter.find_label_issues>` with `pred_probs`.
* Filter the examples with detected issues and train your model on the remaining data.
"""
if labels is not None and y is not None:
raise ValueError("You must specify either `labels` or `y`, but not both.")
if y is not None:
labels = y
if labels is None:
raise ValueError("You must specify `labels`.")
self.clf_final_kwargs = {**clf_kwargs, **clf_final_kwargs}
if "sample_weight" in clf_kwargs:
raise ValueError(
"sample_weight should be provided directly in fit() or in clf_final_kwargs rather than in clf_kwargs"
)
if sample_weight is not None:
if "sample_weight" not in inspect.getfullargspec(self.clf.fit).args:
raise ValueError(
"sample_weight must be a supported fit() argument for your model in order to be specified here"
)
if label_issues is None:
if self.label_issues_df is not None and self.verbose:
print(
"If you already ran self.find_label_issues() and don't want to recompute, you "
"should pass the label_issues in as a parameter to this function next time."
)
label_issues = self.find_label_issues(
X,
labels,
pred_probs=pred_probs,
thresholds=thresholds,
noise_matrix=noise_matrix,
inverse_noise_matrix=inverse_noise_matrix,
clf_kwargs=clf_kwargs,
validation_func=validation_func,
)
else: # set args that may not have been set if `self.find_label_issues()` wasn't called yet
assert_valid_inputs(X, labels, pred_probs)
if self.num_classes is None:
if noise_matrix is not None:
label_matrix = noise_matrix
else:
label_matrix = inverse_noise_matrix
self.num_classes = get_num_classes(labels, pred_probs, label_matrix)
if self.verbose:
print("Using provided label_issues instead of finding label issues.")
if self.label_issues_df is not None:
print(
"These will overwrite self.label_issues_df and will be returned by "
"`self.get_label_issues()`. "
)
# label_issues always overwrites self.label_issues_df. Ensure it is properly formatted:
self.label_issues_df = self._process_label_issues_arg(label_issues, labels)
if "label_quality" not in self.label_issues_df.columns and pred_probs is not None:
if self.verbose:
print("Computing label quality scores based on given pred_probs ...")
self.label_issues_df["label_quality"] = get_label_quality_scores(
labels, pred_probs, **self.label_quality_scores_kwargs
)
self.label_issues_mask = self.label_issues_df["is_label_issue"].to_numpy()
x_mask = np.invert(self.label_issues_mask)
x_cleaned, labels_cleaned = subset_X_y(X, labels, x_mask)
if self.verbose:
print(f"Pruning {np.sum(self.label_issues_mask)} examples with label issues ...")
print(f"Remaining clean data has {len(labels_cleaned)} examples.")
if sample_weight is None:
# Check if sample_weight in args of clf.fit()
if (
"sample_weight" in inspect.getfullargspec(self.clf.fit).args
and "sample_weight" not in self.clf_final_kwargs
and self.noise_matrix is not None
):
# Re-weight examples in the loss function for the final fitting
# such that the "apparent" original number of examples in each class
# is preserved, even though the pruned sets may differ.
if self.verbose:
print(
"Assigning sample weights for final training based on estimated label quality."
)
sample_weight_auto = np.ones(np.shape(labels_cleaned))
for k in range(self.num_classes):
sample_weight_k = 1.0 / max(
self.noise_matrix[k][k], 1e-3
) # clip sample weights
sample_weight_auto[labels_cleaned == k] = sample_weight_k
sample_weight_expanded = np.zeros(
len(labels)
) # pad pruned examples with zeros, length of original dataset
sample_weight_expanded[x_mask] = sample_weight_auto
# Store the sample weight for every example in the original, unfiltered dataset
self.label_issues_df["sample_weight"] = sample_weight_expanded
self.sample_weight = self.label_issues_df[
"sample_weight"
] # pointer to here to avoid duplication
self.clf_final_kwargs["sample_weight"] = sample_weight_auto
if self.verbose:
print("Fitting final model on the clean data ...")
else:
if self.verbose:
if "sample_weight" in self.clf_final_kwargs:
print("Fitting final model on the clean data with custom sample_weight ...")
else:
if (
"sample_weight" in inspect.getfullargspec(self.clf.fit).args
and self.noise_matrix is None
):
print(
"Cannot utilize sample weights for final training! "
"Why this matters: during final training, sample weights help account for the amount of removed data in each class. "
"This helps ensure the correct class prior for the learned model. "
"To use sample weights, you need to either provide the noise_matrix or have previously called self.find_label_issues() instead of filter.find_label_issues() which computes them for you."
)
print("Fitting final model on the clean data ...")
elif sample_weight is not None and "sample_weight" not in self.clf_final_kwargs:
self.clf_final_kwargs["sample_weight"] = sample_weight[x_mask]
if self.verbose:
print("Fitting final model on the clean data with custom sample_weight ...")
else: # pragma: no cover
if self.verbose:
if "sample_weight" in self.clf_final_kwargs:
print("Fitting final model on the clean data with custom sample_weight ...")
else:
print("Fitting final model on the clean data ...")
self.clf.fit(x_cleaned, labels_cleaned, **self.clf_final_kwargs)
if self.verbose:
print(
"Label issues stored in label_issues_df DataFrame accessible via: self.get_label_issues(). "
"Call self.save_space() to delete this potentially large DataFrame attribute."
)
return self
[docs] def predict(self, *args, **kwargs) -> np.ndarray:
"""Predict class labels using your wrapped classifier `clf`.
Works just like ``clf.predict()``.
Parameters
----------
X : np.ndarray or DatasetLike
Test data in the same format expected by your wrapped classifier.
Returns
-------
class_predictions : np.ndarray
Vector of class predictions for the test examples.
"""
return self.clf.predict(*args, **kwargs)
[docs] def predict_proba(self, *args, **kwargs) -> np.ndarray:
"""Predict class probabilities ``P(true label=k)`` using your wrapped classifier `clf`.
Works just like ``clf.predict_proba()``.
Parameters
----------
X : np.ndarray or DatasetLike
Test data in the same format expected by your wrapped classifier.
Returns
-------
pred_probs : np.ndarray
``(N x K)`` array of predicted class probabilities, one row for each test example.
"""
return self.clf.predict_proba(*args, **kwargs)
[docs] def score(self, X, y, sample_weight=None) -> float:
"""Evaluates your wrapped classifier `clf`'s score on a test set `X` with labels `y`.
Uses your model's default scoring function, or simply accuracy if your model as no ``"score"`` attribute.
Parameters
----------
X : np.ndarray or DatasetLike
Test data in the same format expected by your wrapped classifier.
y : array_like
Test labels in the same format as labels previously used in ``fit()``.
sample_weight : np.ndarray, optional
An array of shape ``(N,)`` or ``(N, 1)`` used to weight each test example when computing the score.
Returns
-------
score: float
Number quantifying the performance of this classifier on the test data.
"""
if hasattr(self.clf, "score"):
# Check if sample_weight in clf.score()
if "sample_weight" in inspect.getfullargspec(self.clf.score).args:
return self.clf.score(X, y, sample_weight=sample_weight)
else:
return self.clf.score(X, y)
else:
return accuracy_score(
y,
self.clf.predict(X),
sample_weight=sample_weight,
)
[docs] def find_label_issues(
self,
X=None,
labels=None,
*,
pred_probs=None,
thresholds=None,
noise_matrix=None,
inverse_noise_matrix=None,
save_space=False,
clf_kwargs={},
validation_func=None,
) -> pd.DataFrame:
"""
Identifies potential label issues in the dataset using confident learning.
Runs cross-validation to get out-of-sample pred_probs from `clf`
and then calls :py:func:`filter.find_label_issues
<cleanlab.filter.find_label_issues>` to find label issues.
These label issues are cached internally and returned in a pandas DataFrame.
Kwargs for :py:func:`filter.find_label_issues
<cleanlab.filter.find_label_issues>` must have already been specified
in the initialization of this class, not here.
Unlike :py:func:`filter.find_label_issues
<cleanlab.filter.find_label_issues>`, which requires `pred_probs`,
this method only requires a classifier and it can do the cross-validation for you.
Both methods return the same boolean mask that identifies which examples have label issues.
This is the preferred method to use if you plan to subsequently invoke:
:py:meth:`CleanLearning.fit()
<cleanlab.classification.CleanLearning.fit>`.
Note: this method computes the label issues from scratch. To access
previously-computed label issues from this :py:class:`CleanLearning
<cleanlab.classification.CleanLearning>` instance, use the
:py:meth:`get_label_issues
<cleanlab.classification.CleanLearning.get_label_issues>` method.
This is the method called to find label issues inside
:py:meth:`CleanLearning.fit()
<cleanlab.classification.CleanLearning.fit>`
and they share mostly the same parameters.
Parameters
----------
save_space : bool, optional
If True, then returned `label_issues_df` will not be stored as attribute.
This means some other methods like `self.get_label_issues()` will no longer work.
For info about the **other parameters**, see the docstring of :py:meth:`CleanLearning.fit()
<cleanlab.classification.CleanLearning.fit>`.
Returns
-------
label_issues_df : pd.DataFrame
DataFrame with info about label issues for each example.
Unless `save_space` argument is specified, same DataFrame is also stored as
`self.label_issues_df` attribute accessible via
:py:meth:`get_label_issues<cleanlab.classification.CleanLearning.get_label_issues>`.
Each row represents an example from our dataset and
the DataFrame may contain the following columns:
* *is_label_issue*: boolean mask for the entire dataset where ``True`` represents a label issue and ``False`` represents an example that is accurately labeled with high confidence. This column is equivalent to `label_issues_mask` output from :py:func:`filter.find_label_issues<cleanlab.filter.find_label_issues>`.
* *label_quality*: Numeric score that measures the quality of each label (how likely it is to be correct, with lower scores indicating potentially erroneous labels).
* *given_label*: Integer indices corresponding to the class label originally given for this example (same as `labels` input). Included here for ease of comparison against `clf` predictions, only present if "predicted_label" column is present.
* *predicted_label*: Integer indices corresponding to the class predicted by trained `clf` model. Only present if ``pred_probs`` were provided as input or computed during label-issue-finding.
* *sample_weight*: Numeric values used to weight examples during the final training of `clf` in :py:meth:`CleanLearning.fit()<cleanlab.classification.CleanLearning.fit>`. This column may not be present after `self.find_label_issues()` but may be added after call to :py:meth:`CleanLearning.fit()<cleanlab.classification.CleanLearning.fit>`. For more precise definition of sample weights, see documentation of :py:meth:`CleanLearning.fit()<cleanlab.classification.CleanLearning.fit>`
"""
# Check inputs
assert_valid_inputs(X, labels, pred_probs)
if noise_matrix is not None and np.trace(noise_matrix) <= 1:
t = np.round(np.trace(noise_matrix), 2)
raise ValueError("Trace(noise_matrix) is {}, but must exceed 1.".format(t))
if inverse_noise_matrix is not None and (np.trace(inverse_noise_matrix) <= 1):
t = np.round(np.trace(inverse_noise_matrix), 2)
raise ValueError("Trace(inverse_noise_matrix) is {}. Must exceed 1.".format(t))
if noise_matrix is not None:
label_matrix = noise_matrix
else:
label_matrix = inverse_noise_matrix
self.num_classes = get_num_classes(labels, pred_probs, label_matrix)
if (pred_probs is None) and (len(labels) / self.num_classes < self.cv_n_folds):
raise ValueError(
"Need more data from each class for cross-validation. "
"Try decreasing cv_n_folds (eg. to 2 or 3) in CleanLearning()"
)
# 'ps' is p(labels=k)
self.ps = value_counts(labels) / float(len(labels))
self.clf_kwargs = clf_kwargs
self._process_label_issues_kwargs(self.find_label_issues_kwargs)
# self._process_label_issues_kwargs might set self.confident_joint. If so, we should use it.
if self.confident_joint is not None:
self.py, noise_matrix, inv_noise_matrix = estimate_latent(
confident_joint=self.confident_joint,
labels=labels,
)
# If needed, compute noise rates (probability of class-conditional mislabeling).
if noise_matrix is not None:
self.noise_matrix = noise_matrix
if inverse_noise_matrix is None:
if self.verbose:
print("Computing label noise estimates from provided noise matrix ...")
self.py, self.inverse_noise_matrix = compute_py_inv_noise_matrix(
ps=self.ps,
noise_matrix=self.noise_matrix,
)
if inverse_noise_matrix is not None:
self.inverse_noise_matrix = inverse_noise_matrix
if noise_matrix is None:
if self.verbose:
print("Computing label noise estimates from provided inverse noise matrix ...")
self.noise_matrix = compute_noise_matrix_from_inverse(
ps=self.ps,
inverse_noise_matrix=self.inverse_noise_matrix,
)
if noise_matrix is None and inverse_noise_matrix is None:
if pred_probs is None:
if self.verbose:
print(
"Computing out of sample predicted probabilities via "
f"{self.cv_n_folds}-fold cross validation. May take a while ..."
)
(
self.py,
self.noise_matrix,
self.inverse_noise_matrix,
self.confident_joint,
pred_probs,
) = estimate_py_noise_matrices_and_cv_pred_proba(
X=X,
labels=labels,
clf=self.clf,
cv_n_folds=self.cv_n_folds,
thresholds=thresholds,
converge_latent_estimates=self.converge_latent_estimates,
seed=self.seed,
clf_kwargs=self.clf_kwargs,
validation_func=validation_func,
)
else: # pred_probs is provided by user (assumed holdout probabilities)
if self.verbose:
print("Computing label noise estimates from provided pred_probs ...")
(
self.py,
self.noise_matrix,
self.inverse_noise_matrix,
self.confident_joint,
) = estimate_py_and_noise_matrices_from_probabilities(
labels=labels,
pred_probs=pred_probs,
thresholds=thresholds,
converge_latent_estimates=self.converge_latent_estimates,
)
# If needed, compute P(label=k|x), denoted pred_probs (the predicted probabilities)
if pred_probs is None:
if self.verbose:
print(
"Computing out of sample predicted probabilities via "
f"{self.cv_n_folds}-fold cross validation. May take a while ..."
)
pred_probs = estimate_cv_predicted_probabilities(
X=X,
labels=labels,
clf=self.clf,
cv_n_folds=self.cv_n_folds,
seed=self.seed,
clf_kwargs=self.clf_kwargs,
validation_func=validation_func,
)
# If needed, compute the confident_joint (e.g. occurs if noise_matrix was given)
if self.confident_joint is None:
self.confident_joint = compute_confident_joint(
labels=labels,
pred_probs=pred_probs,
thresholds=thresholds,
)
# if pulearning == the integer specifying the class without noise.
if self.num_classes == 2 and self.pulearning is not None: # pragma: no cover
# pulearning = 1 (no error in 1 class) implies p(label=1|true_label=0) = 0
self.noise_matrix[self.pulearning][1 - self.pulearning] = 0
self.noise_matrix[1 - self.pulearning][1 - self.pulearning] = 1
# pulearning = 1 (no error in 1 class) implies p(true_label=0|label=1) = 0
self.inverse_noise_matrix[1 - self.pulearning][self.pulearning] = 0
self.inverse_noise_matrix[self.pulearning][self.pulearning] = 1
# pulearning = 1 (no error in 1 class) implies p(label=1,true_label=0) = 0
self.confident_joint[self.pulearning][1 - self.pulearning] = 0
self.confident_joint[1 - self.pulearning][1 - self.pulearning] = 1
# Add confident joint to find label issue args if it is not previously specified
if "confident_joint" not in self.find_label_issues_kwargs.keys():
# however does not add if users specify filter_by="confident_learning", as it will throw a warning
if not self.find_label_issues_kwargs.get("filter_by") == "confident_learning":
self.find_label_issues_kwargs["confident_joint"] = self.confident_joint
labels = labels_to_array(labels)
if self.verbose:
print("Using predicted probabilities to identify label issues ...")
label_issues_mask = filter.find_label_issues(
labels,
pred_probs,
**self.find_label_issues_kwargs,
)
label_quality_scores = get_label_quality_scores(
labels, pred_probs, **self.label_quality_scores_kwargs
)
label_issues_df = pd.DataFrame(
{"is_label_issue": label_issues_mask, "label_quality": label_quality_scores}
)
if self.verbose:
print(f"Identified {np.sum(label_issues_mask)} examples with label issues.")
predicted_labels = pred_probs.argmax(axis=1)
label_issues_df["given_label"] = compress_int_array(labels, self.num_classes)
label_issues_df["predicted_label"] = compress_int_array(predicted_labels, self.num_classes)
if not save_space:
if self.label_issues_df is not None and self.verbose:
print(
"Overwriting previously identified label issues stored at self.label_issues_df. "
"self.get_label_issues() will now return the newly identified label issues. "
)
self.label_issues_df = label_issues_df
self.label_issues_mask = label_issues_df[
"is_label_issue"
] # pointer to here to avoid duplication
elif self.verbose:
print( # pragma: no cover
"Not storing label_issues as attributes since save_space was specified."
)
return label_issues_df
[docs] def get_label_issues(self) -> Optional[pd.DataFrame]:
"""
Accessor. Returns `label_issues_df` attribute if previously already computed.
This ``pd.DataFrame`` describes the label issues identified for each example
(each row corresponds to an example).
For column definitions, see the documentation of
:py:meth:`CleanLearning.find_label_issues<cleanlab.classification.CleanLearning.find_label_issues>`.
Returns
-------
label_issues_df : pd.DataFrame
DataFrame with (precomputed) info about label issues for each example.
"""
if self.label_issues_df is None:
warnings.warn(
"Label issues have not yet been computed. Run `self.find_label_issues()` or `self.fit()` first."
)
return self.label_issues_df
[docs] def save_space(self):
"""
Clears non-sklearn attributes of this estimator to save space (in-place).
This includes the DataFrame attribute that stored label issues which may be large for big datasets.
You may want to call this method before deploying this model (i.e. if you just care about producing predictions).
After calling this method, certain non-prediction-related attributes/functionality will no longer be available
(e.g. you cannot call ``self.fit()`` anymore).
"""
if self.label_issues_df is None and self.verbose:
print("self.label_issues_df is already empty") # pragma: no cover
self.label_issues_df = None
self.sample_weight = None
self.label_issues_mask = None
self.find_label_issues_kwargs = None
self.label_quality_scores_kwargs = None
self.label_issues_df = None
self.label_issues_mask = None
self.sample_weight = None
self.confident_joint = None
self.py = None
self.ps = None
self.num_classes = None
self.noise_matrix = None
self.inverse_noise_matrix = None
self.clf_kwargs = None
self.clf_final_kwargs = None
if self.verbose:
print("Deleted non-sklearn attributes such as label_issues_df to save space.")
def _process_label_issues_kwargs(self, find_label_issues_kwargs):
"""
Private helper function that is used to modify the arguments to passed to
filter.find_label_issues via the CleanLearning.find_label_issues class. Because
this is a classification task, some default parameters change and some errors should
be throne if certain unsupported (for classification) arguments are passed in. This method
handles those parameters inside of find_label_issues_kwargs and throws an error if you pass
in a kwargs argument to filter.find_label_issues that is not supported by the
CleanLearning.find_label_issues() function.
"""
# Defaults for CleanLearning.find_label_issues() vs filter.find_label_issues()
DEFAULT_FIND_LABEL_ISSUES_KWARGS = {"min_examples_per_class": 10}
find_label_issues_kwargs = {**DEFAULT_FIND_LABEL_ISSUES_KWARGS, **find_label_issues_kwargs}
# Todo: support multi_label classification in the future and remove multi_label from list
unsupported_kwargs = ["return_indices_ranked_by", "multi_label"]
for unsupported_kwarg in unsupported_kwargs:
if unsupported_kwarg in find_label_issues_kwargs:
raise ValueError(
"These kwargs of `find_label_issues()` are not supported "
f"for `CleanLearning`: {unsupported_kwargs}"
)
# CleanLearning will use this to compute the noise_matrix and inverse_noise_matrix
if "confident_joint" in find_label_issues_kwargs:
self.confident_joint = find_label_issues_kwargs["confident_joint"]
self.find_label_issues_kwargs = find_label_issues_kwargs
def _process_label_issues_arg(self, label_issues, labels) -> pd.DataFrame:
"""
Helper method to get the label_issues input arg into a formatted DataFrame.
"""
labels = labels_to_array(labels)
if isinstance(label_issues, pd.DataFrame):
if "is_label_issue" not in label_issues.columns:
raise ValueError(
"DataFrame label_issues must contain column: 'is_label_issue'. "
"See CleanLearning.fit() documentation for label_issues column descriptions."
)
if len(label_issues) != len(labels):
raise ValueError("label_issues and labels must have same length")
if "given_label" in label_issues.columns and np.any(
label_issues["given_label"].to_numpy() != labels
):
raise ValueError("labels must match label_issues['given_label']")
return label_issues
elif isinstance(label_issues, np.ndarray):
if not label_issues.dtype in [np.dtype("bool"), np.dtype("int")]:
raise ValueError("If label_issues is numpy.array, dtype must be 'bool' or 'int'.")
if label_issues.dtype is np.dtype("bool") and label_issues.shape != labels.shape:
raise ValueError(
"If label_issues is boolean numpy.array, must have same shape as labels"
)
if label_issues.dtype is np.dtype("int"): # convert to boolean mask
if len(np.unique(label_issues)) != len(label_issues):
raise ValueError(
"If label_issues.dtype is 'int', must contain unique integer indices "
"corresponding to examples with label issues such as output by: "
"filter.find_label_issues(..., return_indices_ranked_by=...)"
)
issue_indices = label_issues
label_issues = np.full(len(labels), False, dtype=bool)
if len(issue_indices) > 0:
label_issues[issue_indices] = True
return pd.DataFrame({"is_label_issue": label_issues})
else:
raise ValueError("label_issues must be either pandas.DataFrame or numpy.array")
```