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# Copyright 2024-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Adapted from https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/decomposition/tests/test_incremental_pca.py
import pytest
import torch
from datasets import load_dataset
from torch.testing import assert_close
from peft.utils.incremental_pca import IncrementalPCA
torch.manual_seed(1999)
@pytest.fixture(scope="module")
def iris():
return load_dataset("scikit-learn/iris", split="train")
def test_incremental_pca(iris):
# Incremental PCA on dense arrays.
n_components = 2
X = torch.tensor([iris["SepalLengthCm"], iris["SepalWidthCm"], iris["PetalLengthCm"], iris["PetalWidthCm"]]).T
batch_size = X.shape[0] // 3
ipca = IncrementalPCA(n_components=n_components, batch_size=batch_size)
ipca.fit(X)
X_transformed = ipca.transform(X)
# PCA
U, S, Vh = torch.linalg.svd(X - torch.mean(X, dim=0))
max_abs_rows = torch.argmax(torch.abs(Vh), dim=1)
signs = torch.sign(Vh[range(Vh.shape[0]), max_abs_rows])
Vh *= signs.view(-1, 1)
explained_variance = S**2 / (X.size(0) - 1)
explained_variance_ratio = explained_variance / explained_variance.sum()
assert X_transformed.shape == (X.shape[0], 2)
assert_close(
ipca.explained_variance_ratio_.sum().item(),
explained_variance_ratio[:n_components].sum().item(),
rtol=1e-3,
atol=1e-3,
)
def test_incremental_pca_check_projection():
# Test that the projection of data is correct.
n, p = 100, 3
X = torch.randn(n, p, dtype=torch.float64) * 0.1
X[:10] += torch.tensor([3, 4, 5])
Xt = 0.1 * torch.randn(1, p, dtype=torch.float64) + torch.tensor([3, 4, 5])
# Get the reconstruction of the generated data X
# Note that Xt has the same "components" as X, just separated
# This is what we want to ensure is recreated correctly
Yt = IncrementalPCA(n_components=2).fit(X).transform(Xt)
# Normalize
Yt /= torch.sqrt((Yt**2).sum())
# Make sure that the first element of Yt is ~1, this means
# the reconstruction worked as expected
assert_close(torch.abs(Yt[0][0]).item(), 1.0, atol=1e-1, rtol=1e-1)
def test_incremental_pca_validation():
# Test that n_components is <= n_features.
X = torch.tensor([[0, 1, 0], [1, 0, 0]])
n_samples, n_features = X.shape
n_components = 4
with pytest.raises(
ValueError,
match=(
f"n_components={n_components} invalid"
f" for n_features={n_features}, need more rows than"
" columns for IncrementalPCA"
" processing"
),
):
IncrementalPCA(n_components, batch_size=10).fit(X)
# Tests that n_components is also <= n_samples.
n_components = 3
with pytest.raises(
ValueError,
match=(f"n_components={n_components} must be less or equal to the batch number of samples {n_samples}"),
):
IncrementalPCA(n_components=n_components).partial_fit(X)
def test_n_components_none():
# Ensures that n_components == None is handled correctly
for n_samples, n_features in [(50, 10), (10, 50)]:
X = torch.rand(n_samples, n_features)
ipca = IncrementalPCA(n_components=None)
# First partial_fit call, ipca.n_components_ is inferred from
# min(X.shape)
ipca.partial_fit(X)
assert ipca.n_components == min(X.shape)
def test_incremental_pca_num_features_change():
# Test that changing n_components will raise an error.
n_samples = 100
X = torch.randn(n_samples, 20)
X2 = torch.randn(n_samples, 50)
ipca = IncrementalPCA(n_components=None)
ipca.fit(X)
with pytest.raises(ValueError):
ipca.partial_fit(X2)
def test_incremental_pca_batch_signs():
# Test that components_ sign is stable over batch sizes.
n_samples = 100
n_features = 3
X = torch.randn(n_samples, n_features)
all_components = []
batch_sizes = torch.arange(10, 20)
for batch_size in batch_sizes:
ipca = IncrementalPCA(n_components=None, batch_size=batch_size).fit(X)
all_components.append(ipca.components_)
for i, j in zip(all_components[:-1], all_components[1:]):
assert_close(torch.sign(i), torch.sign(j), rtol=1e-6, atol=1e-6)
def test_incremental_pca_batch_values():
# Test that components_ values are stable over batch sizes.
n_samples = 100
n_features = 3
X = torch.randn(n_samples, n_features)
all_components = []
batch_sizes = torch.arange(20, 40, 3)
for batch_size in batch_sizes:
ipca = IncrementalPCA(n_components=None, batch_size=batch_size).fit(X)
all_components.append(ipca.components_)
for i, j in zip(all_components[:-1], all_components[1:]):
assert_close(i, j, rtol=1e-1, atol=1e-1)
def test_incremental_pca_partial_fit():
# Test that fit and partial_fit get equivalent results.
n, p = 50, 3
X = torch.randn(n, p) # spherical data
X[:, 1] *= 0.00001 # make middle component relatively small
X += torch.tensor([5, 4, 3]) # make a large mean
# same check that we can find the original data from the transformed
# signal (since the data is almost of rank n_components)
batch_size = 10
ipca = IncrementalPCA(n_components=2, batch_size=batch_size).fit(X)
pipca = IncrementalPCA(n_components=2, batch_size=batch_size)
# Add one to make sure endpoint is included
batch_itr = torch.arange(0, n + 1, batch_size)
for i, j in zip(batch_itr[:-1], batch_itr[1:]):
pipca.partial_fit(X[i:j, :])
assert_close(ipca.components_, pipca.components_, rtol=1e-3, atol=1e-3)
def test_incremental_pca_lowrank(iris):
# Test that lowrank mode is equivalent to non-lowrank mode.
n_components = 2
X = torch.tensor([iris["SepalLengthCm"], iris["SepalWidthCm"], iris["PetalLengthCm"], iris["PetalWidthCm"]]).T
batch_size = X.shape[0] // 3
ipca = IncrementalPCA(n_components=n_components, batch_size=batch_size)
ipca.fit(X)
ipcalr = IncrementalPCA(n_components=n_components, batch_size=batch_size, lowrank=True)
ipcalr.fit(X)
assert_close(ipca.components_, ipcalr.components_, rtol=1e-7, atol=1e-7)
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