# 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/>.
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Callable,
ClassVar,
Dict,
List,
Optional,
Union,
)
import numpy as np
import pandas as pd
from scipy.sparse import csr_matrix
from cleanlab.data_valuation import data_shapley_knn
from cleanlab.datalab.internal.issue_manager import IssueManager
from cleanlab.datalab.internal.issue_manager.knn_graph_helpers import (
num_neighbors_in_knn_graph,
set_knn_graph,
)
if TYPE_CHECKING: # pragma: no cover
import numpy.typing as npt
import pandas as pd
from cleanlab.datalab.datalab import Datalab
[docs]class DataValuationIssueManager(IssueManager):
"""
Detect which examples in a dataset are least valuable via an approximate Data Shapely value.
Examples
--------
.. code-block:: python
>>> from cleanlab import Datalab
>>> import numpy as np
>>> from sklearn.neighbors import NearestNeighbors
>>>
>>> # Generate two distinct clusters
>>> X = np.vstack([
... np.random.normal(-1, 1, (25, 2)),
... np.random.normal(1, 1, (25, 2)),
... ])
>>> y = np.array([0]*25 + [1]*25)
>>>
>>> # Initialize Datalab with data
>>> lab = Datalab(data={"y": y}, label_name="y")
>>>
>>> # Creating a knn_graph for data valuation
>>> knn = NearestNeighbors(n_neighbors=10).fit(X)
>>> knn_graph = knn.kneighbors_graph(mode='distance')
>>>
>>> # Specifying issue types for data valuation
>>> issue_types = {"data_valuation": {}}
>>> lab.find_issues(knn_graph=knn_graph, issue_types=issue_types)
"""
description: ClassVar[
str
] = """
Examples that contribute minimally to a model's training
receive lower valuation scores.
Since the original knn-shapley value is in [-1, 1], we transform it to [0, 1] by:
.. math::
0.5 \times (\text{shapley} + 1)
here shapley is the original knn-shapley value.
"""
issue_name: ClassVar[str] = "data_valuation"
issue_score_key: ClassVar[str]
verbosity_levels: ClassVar[Dict[int, List[str]]] = {
0: [],
1: [],
2: [],
3: ["average_data_valuation"],
}
DEFAULT_THRESHOLD = 0.5
def __init__(
self,
datalab: Datalab,
metric: Optional[Union[str, Callable]] = None,
threshold: Optional[float] = None,
k: int = 10,
**kwargs,
):
super().__init__(datalab)
self.metric = metric
self.k = k
self.threshold = threshold if threshold is not None else self.DEFAULT_THRESHOLD
[docs] def find_issues(
self,
features: Optional[npt.NDArray] = None,
**kwargs,
) -> None:
"""Calculate the data valuation score with a provided or existing knn graph.
Based on KNN-Shapley value described in https://arxiv.org/abs/1911.07128
The larger the score, the more valuable the data point is, the more contribution it will make to the model's training.
Parameters
----------
knn_graph : csr_matrix
A sparse matrix representing the knn graph.
"""
labels = self.datalab.labels
if not isinstance(labels, np.ndarray):
error_msg = (
f"Expected labels to be a numpy array of shape (n_samples,) to use with DataValuationIssueManager, "
f"but got {type(labels)} instead."
)
raise TypeError(error_msg)
knn_graph, self.metric, _ = set_knn_graph(
features=features,
find_issues_kwargs=kwargs,
metric=self.metric,
k=self.k,
statistics=self.datalab.get_info("statistics"),
)
# TODO: Check self.k against user-provided knn-graphs across all issue managers
num_neighbors = num_neighbors_in_knn_graph(knn_graph)
if self.k > num_neighbors:
raise ValueError(
f"The provided knn graph has {num_neighbors} neighbors, which is less than the required {self.k} neighbors. "
"Please ensure that the knn graph you provide has at least as many neighbors as the required value of k."
)
scores = data_shapley_knn(labels, knn_graph=knn_graph, k=self.k)
self.issues = pd.DataFrame(
{
f"is_{self.issue_name}_issue": scores < self.threshold,
self.issue_score_key: scores,
},
)
self.summary = self.make_summary(score=scores.mean())
self.info = self.collect_info(issues=self.issues, knn_graph=knn_graph)
[docs] def collect_info(self, issues: pd.DataFrame, knn_graph: csr_matrix) -> dict:
issues_info = {
"num_low_valuation_issues": sum(issues[f"is_{self.issue_name}_issue"]),
"average_data_valuation": issues[self.issue_score_key].mean(),
}
params_dict = {
"metric": self.metric,
"k": self.k,
"threshold": self.threshold,
}
statistics_dict = self._build_statistics_dictionary(knn_graph=knn_graph)
info_dict = {
**issues_info,
**params_dict,
**statistics_dict,
}
return info_dict
def _build_statistics_dictionary(self, knn_graph: csr_matrix) -> Dict[str, Dict[str, Any]]:
statistics_dict: Dict[str, Dict[str, Any]] = {"statistics": {}}
# Add the knn graph as a statistic if necessary
graph_key = "weighted_knn_graph"
old_knn_graph = self.datalab.get_info("statistics").get(graph_key, None)
old_graph_exists = old_knn_graph is not None
prefer_new_graph = (
not old_graph_exists
or knn_graph.nnz > old_knn_graph.nnz
or self.metric != self.datalab.get_info("statistics").get("knn_metric", None)
)
if prefer_new_graph:
statistics_dict["statistics"][graph_key] = knn_graph
if self.metric is not None:
statistics_dict["statistics"]["knn_metric"] = self.metric
return statistics_dict