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Add tsvd tests and fix a bug (#308)
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@norm4nn
norm4nn authored Nov 7, 2024
1 parent c024c5b commit 7089254
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Showing 3 changed files with 222 additions and 5 deletions.
4 changes: 2 additions & 2 deletions lib/scholar/decomposition/truncated_svd.ex
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Original file line number Diff line number Diff line change
Expand Up @@ -127,7 +127,7 @@ defmodule Scholar.Decomposition.TruncatedSVD do
>
iex> key = Nx.Random.key(0)
iex> x = Nx.tensor([[0, 0, 3], [1, 0, 3], [1, 1, 3], [3, 3, 3], [4, 4.5, 3]])
iex> tsvd = Scholar.Decomposition.TruncatedSVD.fit_transform(x, num_components: 2, key: key)
iex> Scholar.Decomposition.TruncatedSVD.fit_transform(x, num_components: 2, key: key)
#Nx.Tensor<
f32[5][2]
[
Expand Down Expand Up @@ -174,7 +174,7 @@ defmodule Scholar.Decomposition.TruncatedSVD do
end

{u, sigma, vt} = randomized_svd(x, key, opts)
{_u, vt} = Scholar.Decomposition.Utils.flip_svd(u, vt)
{_u, vt} = Scholar.Decomposition.Utils.flip_svd(u, vt, false)

x_transformed = Nx.dot(x, Nx.transpose(vt))
explained_variance = Nx.variance(x_transformed, axes: [0])
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13 changes: 10 additions & 3 deletions lib/scholar/decomposition/utils.ex
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Original file line number Diff line number Diff line change
Expand Up @@ -3,9 +3,16 @@ defmodule Scholar.Decomposition.Utils do
import Nx.Defn
require Nx

defn flip_svd(u, v) do
max_abs_cols_idx = u |> Nx.abs() |> Nx.argmax(axis: 0, keep_axis: true)
signs = u |> Nx.take_along_axis(max_abs_cols_idx, axis: 0) |> Nx.sign() |> Nx.squeeze()
defn flip_svd(u, v, u_based \\ true) do
base =
if u_based do
u
else
Nx.transpose(v)
end

max_abs_cols_idx = base |> Nx.abs() |> Nx.argmax(axis: 0, keep_axis: true)
signs = base |> Nx.take_along_axis(max_abs_cols_idx, axis: 0) |> Nx.sign() |> Nx.squeeze()
u = u * signs
v = v * Nx.new_axis(signs, -1)
{u, v}
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210 changes: 210 additions & 0 deletions test/scholar/decomposition/truncated_svd_test.exs
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Original file line number Diff line number Diff line change
@@ -0,0 +1,210 @@
defmodule Scholar.Decomposition.TruncatedSVDTest do
use Scholar.Case, async: true
alias Scholar.Decomposition.TruncatedSVD
doctest TruncatedSVD

defp key do
Nx.Random.key(1)
end

test "fit test - all default options" do
key = key()

{x, _new_key} =
Nx.Random.multivariate_normal(
key,
Nx.tensor([0.0, 0.0, 0.0, 0.0]),
Nx.tensor([
[3.0, 2.0, 1.0, 9.0],
[1.0, 2.0, 3.0, 8.2],
[1.3, 1.0, 2.2, 2.4],
[1.8, 1.0, 2.0, 2.9]
]),
shape: {50},
type: :f32
)

model = Scholar.Decomposition.TruncatedSVD.fit(x, key: key)

assert_all_close(
model.components,
Nx.tensor([
[0.49934840202331543, 0.44504958391189575, 0.5053765773773193, 0.5451390743255615],
[0.4780271351337433, 0.569697916507721, -0.5178372263908386, -0.42282143235206604]
]),
atol: 1.0e-3
)

assert_all_close(
model.explained_variance,
Nx.tensor([5.641434192657471, 1.3331592082977295]),
atol: 1.0e-3
)

assert_all_close(
model.explained_variance_ratio,
Nx.tensor([0.649896502494812, 0.15358072519302368]),
atol: 1.0e-3
)

assert_all_close(
model.explained_variance_ratio,
Nx.tensor([0.649896502494812, 0.15358072519302368]),
atol: 1.0e-3
)

assert_all_close(
model.singular_values,
Nx.tensor([16.81821060180664, 8.335840225219727]),
atol: 1.0e-3
)
end

test "fit_transform test - all default options" do
key = key()

{x, _new_key} =
Nx.Random.multivariate_normal(
key,
Nx.tensor([0.0, 0.0, 0.0, 0.0]),
Nx.tensor([
[3.0, 2.0, 1.0, 9.0],
[1.0, 2.0, 3.0, 8.2],
[1.3, 1.0, 2.2, 2.4],
[1.8, 1.0, 2.0, 2.9]
]),
shape: {10},
type: :f32
)

x_reduced = Scholar.Decomposition.TruncatedSVD.fit_transform(x, key: key)

assert_all_close(
x_reduced,
Nx.tensor([
[4.441530227661133, -1.5630521774291992],
[-2.187946081161499, -1.2309558391571045],
[-0.9562748074531555, -1.4839725494384766],
[2.2274107933044434, 0.1483912318944931],
[2.879176378250122, -0.12200745940208435],
[2.8487348556518555, 0.8317009806632996],
[1.9470200538635254, 0.96690434217453],
[2.140472173690796, -1.0529983043670654],
[-1.265346884727478, -0.7587057948112488],
[-0.8837906122207642, 0.07025688886642456]
]),
atol: 1.0e-3
)
end

test "fit_transform test - :num_components" do
key = key()

{x, _new_key} =
Nx.Random.multivariate_normal(
key,
Nx.tensor([0.0, 0.0, 0.0, 0.0]),
Nx.tensor([
[3.0, 2.0, 1.0, 9.0],
[1.0, 2.0, 3.0, 8.2],
[1.3, 1.0, 2.2, 2.4],
[1.8, 1.0, 2.0, 2.9]
]),
shape: {10},
type: :f32
)

x_reduced = Scholar.Decomposition.TruncatedSVD.fit_transform(x, key: key, num_components: 3)

assert_all_close(
x_reduced,
Nx.tensor([
[4.441530704498291, -1.5630513429641724, 0.08955635130405426],
[-2.1879451274871826, -1.2309576272964478, 1.2222723960876465],
[-0.9562751054763794, -1.4839714765548706, -0.562005341053009],
[2.2274117469787598, 0.1483895182609558, 0.8012741804122925],
[2.879176378250122, -0.12200674414634705, -0.7124714255332947],
[2.8487346172332764, 0.8317020535469055, -0.1308409571647644],
[1.9470199346542358, 0.9669057130813599, 0.6275887489318848],
[2.140472412109375, -1.0529969930648804, 0.32528647780418396],
[-1.2653470039367676, -0.7587059140205383, -0.5229729413986206],
[-0.8837906122207642, 0.0702567845582962, 0.2195502668619156]
]),
atol: 1.0e-3
)
end

test "fit_transform test - :num_oversamples" do
key = key()

{x, _new_key} =
Nx.Random.multivariate_normal(
key,
Nx.tensor([0.0, 0.0, 0.0, 0.0]),
Nx.tensor([
[3.0, 2.0, 1.0, 9.0],
[1.0, 2.0, 3.0, 8.2],
[1.3, 1.0, 2.2, 2.4],
[1.8, 1.0, 2.0, 2.9]
]),
shape: {10},
type: :f32
)

x_reduced = Scholar.Decomposition.TruncatedSVD.fit_transform(x, key: key, num_oversamples: 20)

assert_all_close(
x_reduced,
Nx.tensor([
[4.441530227661133, -1.5630521774291992],
[-2.187946081161499, -1.2309565544128418],
[-0.9562748670578003, -1.4839720726013184],
[2.2274110317230225, 0.14839033782482147],
[2.879176616668701, -0.12200725078582764],
[2.8487348556518555, 0.8317012190818787],
[1.9470199346542358, 0.9669046401977539],
[2.140472173690796, -1.0529980659484863],
[-1.265346884727478, -0.7587056756019592],
[-0.8837906122207642, 0.07025686651468277]
]),
atol: 1.0e-3
)
end

test "fit_transform test - :num_iters" do
key = key()

{x, _new_key} =
Nx.Random.multivariate_normal(
key,
Nx.tensor([0.0, 0.0, 0.0, 0.0]),
Nx.tensor([
[3.0, 2.0, 1.0, 9.0],
[1.0, 2.0, 3.0, 8.2],
[1.3, 1.0, 2.2, 2.4],
[1.8, 1.0, 2.0, 2.9]
]),
shape: {10},
type: :f32
)

x_reduced = Scholar.Decomposition.TruncatedSVD.fit_transform(x, key: key, num_iter: 20)

assert_all_close(
x_reduced,
Nx.tensor([
[4.441530227661133, -1.5630522966384888],
[-2.18794584274292, -1.2309566736221313],
[-0.9562749862670898, -1.4839718341827393],
[2.2274110317230225, 0.1483900398015976],
[2.879176378250122, -0.1220073327422142],
[2.8487348556518555, 0.8317012190818787],
[1.9470200538635254, 0.9669046998023987],
[2.140472173690796, -1.0529979467391968],
[-1.265346884727478, -0.7587056159973145],
[-0.8837906122207642, 0.07025690376758575]
]),
atol: 1.0e-3
)
end
end

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