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Refactor tests to test types and transform functionality separately (#84
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* Test on ColumnTable and DataFrames

* Delete NamedTuple tests

* Remove DataFrames tests

* Add vector tests

* Remove vector tests

* Add matrix tests

* Remove matrix tests

* Add KeyedArray and AxisArray tests

* Remove KeyedArray and AxisArray tests

* Add cube tests

* Add KeyedArray tests with dims

* Simplify tests by defining data at top

* Add broken apply! tests
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Glenn Moynihan authored Jun 28, 2021
2 parents 86efefa + 00376b7 commit 1b023b1
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300 changes: 14 additions & 286 deletions test/linear_combination.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -4,102 +4,23 @@
@test lc isa Transform
@test cardinality(lc) == ManyToOne()

@testset "Vector" begin

@testset "all inds" begin
x = [1, 2]
lc = LinearCombination([1, -1])
@test FeatureTransforms.apply(x, lc) == fill(-1)
@test lc(x) == fill(-1)
end

@testset "dims behaviour" begin
x = [1, 2]
lc = LinearCombination([1, -1])
@test FeatureTransforms.apply(x, lc; dims=1) == fill(-1)
@test_throws BoundsError FeatureTransforms.apply(x, lc; dims=2)
end

@testset "dimension mismatch" begin
x = [1, 2, 3]
lc = LinearCombination([1, -1])
@test_throws DimensionMismatch FeatureTransforms.apply(x, lc)
end

@testset "specified inds" begin
x = [1, 2, 3]
lc = LinearCombination([1, -1])
@test FeatureTransforms.apply(x, lc; inds=[2, 3]) == fill(-1)
@test lc(x; inds=[2, 3]) == fill(-1)
end

@testset "output is different type" begin
x = [1, 2]
lc = LinearCombination([.1, -.1])
@test FeatureTransforms.apply(x, lc) == fill(-.1)
@test lc(x) == fill(-.1)
end

@testset "apply_append" begin
x = [1, 2]
lc = LinearCombination([1, -1])
@test FeatureTransforms.apply_append(x, lc; append_dim=1) == [1, 2, -1]
end
@testset "default reduces over columns" begin
M = [1 1; 2 2; 3 5]
lc = LinearCombination([1, -1, 1])
@test FeatureTransforms.apply(M, lc) == [2, 4]
@test lc(M) == [2, 4]
end

@testset "Matrix" begin

@testset "default reduces over columns" begin
M = [1 1; 2 2; 3 5]
lc = LinearCombination([1, -1, 1])
@test FeatureTransforms.apply(M, lc) == [2, 4]
@test lc(M) == [2, 4]
end

@testset "dims" begin
@testset "dims = :" begin
M = [1 1; 2 2; 3 5]
lc = LinearCombination([1, -1, 1])
@test_deprecated FeatureTransforms.apply(M, lc; dims=:)
end

@testset "dims = 1" begin
M = [1 1; 2 2; 3 5]
lc = LinearCombination([1, -1, 1])
@test FeatureTransforms.apply(M, lc; dims=1) == [2, 4]
@test lc(M; dims=1) == [2, 4]
end

@testset "dims = 2" begin
M = [1 1; 2 2; 3 5]
lc = LinearCombination([1, -1])
@test FeatureTransforms.apply(M, lc; dims=2) == [0, 0, -2]
end
end

@testset "dimension mismatch" begin
M = [1 1 1; 2 2 2]
lc = LinearCombination([1, -1, 1]) # there are only 2 rows
@test_throws DimensionMismatch FeatureTransforms.apply(M, lc)
end

@testset "specified inds" begin
M = [1 1; 5 2; 2 4]
lc = LinearCombination([1, -1])
@test FeatureTransforms.apply(M, lc; inds=[2, 3]) == [3, -2]
@test lc(M; inds=[2, 3]) == [3, -2]
end

@testset "apply_append" begin
M = [1 1 1; 2 2 2; 3 3 3]
lc = LinearCombination([1, 1, 1])

expected1 = [1 1 1; 2 2 2; 3 3 3; 6 6 6]
@test FeatureTransforms.apply_append(M, lc; dims=1, append_dim=1) == expected1
@testset "dims = :" begin
M = [1 1; 2 2; 3 5]
lc = LinearCombination([1, -1, 1])
@test_deprecated FeatureTransforms.apply(M, lc; dims=:)
end

expected2 = [1 1 1 3; 2 2 2 6; 3 3 3 9]
@test FeatureTransforms.apply_append(M, lc; dims=2, append_dim=2) == expected2
end
@testset "dimension mismatch" begin
M = [1 1 1; 2 2 2]
lc = LinearCombination([1, -1, 1]) # there are only 2 rows
@test_throws DimensionMismatch FeatureTransforms.apply(M, lc)
end

@testset "N-dim Array" begin
Expand All @@ -108,197 +29,4 @@
@test FeatureTransforms.apply(A, lc) == [2 11 20; 5 14 23; 8 17 26]
@test lc(A) == [2 11 20; 5 14 23; 8 17 26]
end

@testset "AxisArray" begin
A = AxisArray([1 2; 4 5], foo=["a", "b"], bar=["x", "y"])
lc = LinearCombination([1, -1])

@testset "all inds" begin
@test FeatureTransforms.apply(A, lc) == [-3, -3]
@test lc(A) == [-3, -3]
end

@testset "dims" begin
@testset "dims = :" begin
@test_deprecated FeatureTransforms.apply(A, lc; dims=:)
end

@testset "dims = 1" begin
@test FeatureTransforms.apply(A, lc; dims=1) == [-3, -3]
@test lc(A; dims=1) == [-3, -3]
end

@testset "dims = 2" begin
@test FeatureTransforms.apply(A, lc; dims=2) == [-1, -1]
@test lc(A; dims=2) == [-1, -1]
end
end

@testset "dimension mismatch" begin
A = AxisArray([1 2 3; 4 5 5], foo=["a", "b"], bar=["x", "y", "z"])
@test_throws DimensionMismatch FeatureTransforms.apply(A, lc; dims=2)
end

@testset "specified inds" begin
A = AxisArray([1 2 3; 4 5 5], foo=["a", "b"], bar=["x", "y", "z"])
@test FeatureTransforms.apply(A, lc; inds=[1, 2]) == [-3, -3, -2]
@test lc(A; inds=[1, 2]) == [-3, -3, -2]
end

@testset "apply_append" begin
A = AxisArray([1 2; 4 5], foo=["a", "b"], bar=["x", "y"])

expected1 = [1 2; 4 5; -3 -3]
@test FeatureTransforms.apply_append(A, lc; dims=1, append_dim=1) == expected1

expected2 = [1 2 -1; 4 5 -1]
@test FeatureTransforms.apply_append(A, lc; dims=2, append_dim=2) == expected2
end
end

@testset "AxisKey" begin
A = KeyedArray([1 2; 4 5], foo=["a", "b"], bar=["x", "y"])
lc = LinearCombination([1, -1])

@testset "all inds" begin
@test FeatureTransforms.apply(A, lc) == [-3, -3]
@test lc(A) == [-3, -3]
end

@testset "dims" begin
@testset "dims = :" begin
@test_deprecated FeatureTransforms.apply(A, lc; dims=:)
end

@testset "dims = 1" begin
@test FeatureTransforms.apply(A, lc; dims=1) == [-3, -3]
@test FeatureTransforms.apply(A, lc; dims=:foo) == [-3, -3]
@test lc(A; dims=1) == [-3, -3]
end

@testset "dims = 2" begin
@test FeatureTransforms.apply(A, lc; dims=2) == [-1, -1]
@test FeatureTransforms.apply(A, lc; dims=:bar) == [-1, -1]
@test lc(A; dims=2) == [-1, -1]
end
end

@testset "dimension mismatch" begin
A = KeyedArray([1 2 3; 4 5 6], foo=["a", "b"], bar=["x", "y", "z"])
@test_throws DimensionMismatch FeatureTransforms.apply(A, lc; dims=2)
end

@testset "specified inds" begin
A = KeyedArray([1 2 3; 4 5 5], foo=["a", "b"], bar=["x", "y", "z"])
@test FeatureTransforms.apply(A, lc; inds=[1, 2]) == [-3, -3, -2]
@test lc(A; inds=[1, 2]) == [-3, -3, -2]
end

@testset "apply_append" begin
A = KeyedArray([1 2; 4 5], foo=["a", "b"], bar=["x", "y"])

expected1 = [1 2; 4 5; -3 -3]
@test FeatureTransforms.apply_append(A, lc; dims=:foo, append_dim=:foo) == expected1

expected2 = [1 2 -1; 4 5 -1]
@test FeatureTransforms.apply_append(A, lc; dims=:bar, append_dim=:bar) == expected2
end
end

@testset "NamedTuple" begin
@testset "all cols" begin
nt = (a = [1, 2, 3], b = [4, 5, 6])
lc = LinearCombination([1, -1])
expected = (Column1 = [-3, -3, -3],)
@test FeatureTransforms.apply(nt, lc) == expected
@test lc(nt) == expected
end

@testset "custom header" begin
nt = (a = [1, 2, 3], b = [4, 5, 6])
lc = LinearCombination([1, -1])
expected = (x = [-3, -3, -3],)
@test FeatureTransforms.apply(nt, lc; header=[:x]) == expected
@test lc(nt; header=[:x]) == expected
end

@testset "dimension mismatch" begin
nt = (a = [1, 2, 3], b = [4, 5, 6], c = [1, 1, 1])
lc = LinearCombination([1, -1])
@test_throws DimensionMismatch FeatureTransforms.apply(nt, lc)
end

@testset "specified cols" begin
nt = (a = [1, 2, 3], b = [4, 5, 6], c = [1, 1, 1])
lc = LinearCombination([1, -1])
expected = (Column1 = [-3, -3, -3],)
@test FeatureTransforms.apply(nt, lc; cols=[:a, :b]) == expected
@test lc(nt; cols=[:a, :b]) == expected
end

@testset "single col" begin
nt = (a = [1, 2, 3], b = [4, 5, 6])
lc_single = LinearCombination([-1])
expected = (Column1 = [-1, -2, -3],)
@test FeatureTransforms.apply(nt, lc_single; cols=:a) == expected
@test FeatureTransforms.apply(nt, lc_single; cols=[:a]) == expected
@test lc_single(nt; cols=:a) == expected
end

@testset "apply_append" begin
nt = (a = [1, 2, 3], b = [4, 5, 6])
lc = LinearCombination([1, -1])
expected = (a = [1, 2, 3], b = [4, 5, 6], Column1 = [-3, -3, -3])
@test FeatureTransforms.apply_append(nt, lc) == expected
end
end

@testset "DataFrame" begin

@testset "all cols" begin
df = DataFrame(:a => [1, 2, 3], :b => [4, 5, 6])
lc = LinearCombination([1, -1])
@test FeatureTransforms.apply(df, lc) == DataFrame(:Column1 => [-3, -3, -3])
@test lc(df) == DataFrame(:Column1 => [-3, -3, -3])
end

@testset "custom header" begin
df = DataFrame(:a => [1, 2, 3], :b => [4, 5, 6])
lc = LinearCombination([1, -1])
expected = DataFrame(:x => [-3, -3, -3])
@test FeatureTransforms.apply(df, lc; header=[:x]) == expected
@test lc(df; header=[:x]) == expected
end

@testset "dimension mismatch" begin
df = DataFrame(:a => [1, 2, 3], :b => [4, 5, 6], :c => [1, 1, 1])
lc = LinearCombination([1, -1])
@test_throws DimensionMismatch FeatureTransforms.apply(df, lc)
end

@testset "specified cols" begin
df = DataFrame(:a => [1, 2, 3], :b => [4, 5, 6], :c => [1, 1, 1])
lc = LinearCombination([1, -1])
expected = DataFrame(:Column1 => [3, 4, 5])

@test FeatureTransforms.apply(df, lc; cols=[:b, :c]) == expected
@test lc(df; cols=[:b, :c]) == expected
end

@testset "single col" begin
df = DataFrame(:a => [1, 2, 3], :b => [4, 5, 6])
lc_single = LinearCombination([-1])
expected = DataFrame(:Column1 => [-1, -2, -3])
@test FeatureTransforms.apply(df, lc_single; cols=:a) == expected
@test FeatureTransforms.apply(df, lc_single; cols=[:a]) == expected
@test lc_single(df; cols=:a) == expected
end

@testset "apply_append" begin
df = DataFrame(:a => [1, 2, 3], :b => [4, 5, 6])
lc = LinearCombination([1, -1])
expected = DataFrame(:a => [1, 2, 3], :b => [4, 5, 6], :Column1 => [-3, -3, -3])
@test FeatureTransforms.apply_append(df, lc) == expected
end
end
end
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