pyDVL 0.9.2

Description:

pyDVL 0.9.2

A library for data valuation.










pyDVL collects algorithms for Data Valuation and Influence Function
computation. Here is the list of all methods implemented.
Data Valuation for machine learning is the task of assigning a scalar
to each element of a training set which reflects its contribution to the final
performance or outcome of some model trained on it. Some concepts of
value depend on a specific model of interest, while others are model-agnostic.
pyDVL focuses on model-dependent methods.



Comparison of different data valuation methods
on best sample removal.


The Influence Function is an infinitesimal measure of the effect that single
training points have over the parameters of a model, or any function thereof.
In particular, in machine learning they are also used to compute the effect
of training samples over individual test points.



Influences of input points with corrupted data.
Highlighted points have flipped labels.


Installation
To install the latest release use:
$ pip install pyDVL

You can also install the latest development version from
TestPyPI:
pip install pyDVL --index-url https://test.pypi.org/simple/

pyDVL has also extra dependencies for certain functionalities,
e.g. for using influence functions run
$ pip install pyDVL[influence]

For more instructions and information refer to Installing pyDVL
in the documentation.
Usage
Please read Getting
Started in the
documentation for more instructions. We provide several examples for data
valuation and for influence functions in our Example
Gallery.
Influence Functions

Import the necessary packages (the exact ones depend on your specific use case).
Create PyTorch data loaders for your train and test splits.
Instantiate your neural network model and define your loss function.
Instantiate an InfluenceFunctionModel and fit it to the training data
For small input data, you can call the influences() method on the fitted
instance. The result is a tensor of shape (training samples, test samples)
that contains at index (i, j) the influence of training sample i on
test sample j.
For larger datasets, wrap the model into a "calculator" and call methods on
it. This splits the computation into smaller chunks and allows for lazy
evaluation and out-of-core computation.

The higher the absolute value of the influence of a training sample
on a test sample, the more influential it is for the chosen test sample, model
and data loaders. The sign of the influence determines whether it is
useful (positive) or harmful (negative).

Note pyDVL currently only support PyTorch for Influence Functions. We plan
to add support for Jax next.

import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from pydvl.influence import SequentialInfluenceCalculator
from pydvl.influence.torch import DirectInfluence
from pydvl.influence.torch.util import (
NestedTorchCatAggregator,
TorchNumpyConverter,
)

input_dim = (5, 5, 5)
output_dim = 3
train_x, train_y = torch.rand((10, *input_dim)), torch.rand((10, output_dim))
test_x, test_y = torch.rand((5, *input_dim)), torch.rand((5, output_dim))
train_data_loader = DataLoader(TensorDataset(train_x, train_y), batch_size=2)
test_data_loader = DataLoader(TensorDataset(test_x, test_y), batch_size=1)
model = nn.Sequential(
nn.Conv2d(in_channels=5, out_channels=3, kernel_size=3),
nn.Flatten(),
nn.Linear(27, 3),
)
loss = nn.MSELoss()

infl_model = DirectInfluence(model, loss, hessian_regularization=0.01)
infl_model = infl_model.fit(train_data_loader)

# For small datasets, instantiate the full influence matrix:
influences = infl_model.influences(test_x, test_y, train_x, train_y)

# For larger datasets, use the Influence calculators:
infl_calc = SequentialInfluenceCalculator(infl_model)

# Lazy object providing arrays batch-wise in a sequential manner
lazy_influences = infl_calc.influences(test_data_loader, train_data_loader)

# Trigger computation and pull results to memory
influences = lazy_influences.compute(aggregator=NestedTorchCatAggregator())

# Trigger computation and write results batch-wise to disk
lazy_influences.to_zarr("influences_result", TorchNumpyConverter())

Data Valuation
The steps required to compute data values for your samples are:

Import the necessary packages (the exact ones will depend on your specific
use case).
Create a Dataset object with your train and test splits.
Create an instance of a SupervisedModel (basically any sklearn compatible
predictor), and wrap it in a Utility object together with the data and a
scoring function.
Use one of the methods defined in the library to compute the values. In the
example below, we will use Permutation Montecarlo Shapley, an approximate
method for computing Data Shapley values. The result is a variable of type
ValuationResult that contains the indices and their values as well as other
attributes.
Convert the valuation result to a dataframe, and analyze and visualize the
values.

The higher the value for an index, the more important it is for the chosen
model, dataset and scorer. Reciprocally, low-value points could be mislabelled,
or out-of-distribution, and dropping them can improve the model's performance.
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression

from pydvl.utils import Dataset, Scorer, Utility
from pydvl.value import (MaxUpdates, RelativeTruncation,
permutation_montecarlo_shapley)

data = Dataset.from_sklearn(
load_breast_cancer(),
train_size=10,
stratify_by_target=True,
random_state=16,
)
model = LogisticRegression()
u = Utility(
model,
data,
Scorer("accuracy", default=0.0)
)
values = permutation_montecarlo_shapley(
u,
truncation=RelativeTruncation(u, 0.05),
done=MaxUpdates(1000),
seed=16,
progress=True
)
df = values.to_dataframe(column="data_value")

Contributing
Please open new issues for bugs, feature requests and extensions. You can read
about the structure of the project, the toolchain and workflow in the guide for
contributions.
License
pyDVL is distributed under
LGPL-3.0. A complete version can
be found in two files: here and here.
All contributions will be distributed under this license.