Skip to content

Commit

Permalink
Add doctests and other
Browse files Browse the repository at this point in the history
  • Loading branch information
@msluszniak
msluszniak committed Nov 3, 2023
1 parent 4d44f37 commit a3b2a82
Show file tree
Hide file tree
Showing 3 changed files with 92 additions and 82 deletions.
20 changes: 10 additions & 10 deletions lib/scholar/linear/isotonic_regression.ex
View file
Original file line number Diff line number Diff line change
Expand Up @@ -162,18 +162,18 @@ defmodule Scholar.Linear.IsotonicRegression do

{increasing, opts} = Keyword.pop(opts, :increasing)

# increasing =
# case increasing do
# :auto ->
# check_increasing(x, y)
increasing =
case increasing do
:auto ->
check_increasing(x, y)

# true ->
# Nx.u8(1)
true ->
Nx.u8(1)

# false ->
# Nx.u8(0)
# end
increasing = Nx.u8(1)
false ->
Nx.u8(0)
end
# increasing = Nx.u8(1)

fit_n(x, y, sample_weights, increasing, opts)
end
Expand Down
149 changes: 77 additions & 72 deletions lib/scholar/manifold/mds.ex
View file
Original file line number Diff line number Diff line change
Expand Up @@ -70,7 +70,6 @@ defmodule Scholar.Manifold.MDS do

# initialize x randomly or pass the init x earlier
defnp smacof(dissimilarities, x, max_iter, opts) do
# n = Nx.axis_size(dissimilarities, 0)
similarities_flat = Nx.flatten(dissimilarities)
similarities_flat_indices = lower_triangle_indices(dissimilarities)

Expand All @@ -86,7 +85,6 @@ defmodule Scholar.Manifold.MDS do
similarities_flat_w, old_stress = Nx.Constants.infinity(Nx.type(dissimilarities)),
metric, normalized_stress, eps, stop_value = 0}},
i < max_iter and not stop_value do
# i < 1 and not stop_value do
dis = Distance.pairwise_euclidean(x)
n = Nx.axis_size(dissimilarities, 0)

Expand All @@ -101,7 +99,6 @@ defmodule Scholar.Manifold.MDS do
n = Nx.axis_size(dis, 0)

dis_flat_w = Nx.take(dis_flat, dis_flat_indices)
# dis_flat_w = Nx.flatten(remove_main_diag(dis))

disparities_flat_model =
Scholar.Linear.IsotonicRegression.fit(similarities_flat_w, dis_flat_w)
Expand All @@ -111,20 +108,13 @@ defmodule Scholar.Manifold.MDS do
disparities_flat =
Scholar.Linear.IsotonicRegression.predict(model, similarities_flat_w)

# similarities_flat_indices

# disparities = Nx.select(similarities_flat != 0, disparities_flat, disparities_flat)
# {dis_flat, similarities_flat_indices, disparities_flat}

disparities =
Nx.indexed_put(
dis_flat,
Nx.new_axis(similarities_flat_indices, -1),
disparities_flat
)

# disparities = Nx.reshape(dis, {n, n})

disparities = Nx.reshape(disparities, {n, n})

disparities * Nx.sqrt(n * (n - 1) / 2 / Nx.sum(disparities ** 2))
Expand Down Expand Up @@ -159,32 +149,31 @@ defmodule Scholar.Manifold.MDS do
{x, stress, i}
end

defnp mds_main_loop(dissimilarities, x, key, opts) do
defnp mds_main_loop(dissimilarities, x, _key, opts) do
n_init = opts[:n_init]

type = Nx.Type.merge(to_float_type(x), to_float_type(dissimilarities))
dissimilarities = Nx.as_type(dissimilarities, type)
x = Nx.as_type(x, type)

dissimilarities = Distance.pairwise_euclidean(dissimilarities)

{{best, best_stress, best_iter}, _} =
while {{best = x, best_stress = Nx.Constants.infinity(type),
best_iter = 0}, {n_init, dissimilarities, x, i = 0}},
i < n_init do
# # i < 1 do
{temp, stress, iter} = smacof(dissimilarities, x, opts[:max_iter], opts)
# smacof(dissimilarities, x, opts[:max_iter], opts)

{best, best_stress, best_iter} =
if stress < best_stress, do: {temp, stress, iter}, else: {best, best_stress, best_iter}

{best, best_stress, best_iter, {n_init, dissimilarities, x, i + 1}}
{{best, best_stress, best_iter}, {n_init, dissimilarities, x, i + 1}}
end

{best, best_stress, best_iter}
end

defnp mds_main_loop(dissimilarities, key, opts) do
# key = opts[:key]
n_init = opts[:n_init]
max_iter = opts[:max_iter]
num_samples = Nx.axis_size(dissimilarities, 0)
Expand All @@ -204,14 +193,12 @@ defmodule Scholar.Manifold.MDS do
while {{best = dummy, best_stress = Nx.Constants.infinity(type), best_iter = 0},
{n_init, new_key, max_iter, dissimilarities, i = 0}},
i < n_init do
# i < 1 do
num_samples = Nx.axis_size(dissimilarities, 0)

{x, new_key} =
Nx.Random.uniform(new_key, shape: {num_samples, opts[:num_components]}, type: type)

{temp, stress, iter} = smacof(dissimilarities, x, max_iter, opts)
# smacof(dissimilarities, x, max_iter, opts)

{best, best_stress, best_iter} =
if stress < best_stress, do: {temp, stress, iter}, else: {best, best_stress, best_iter}
Expand Down Expand Up @@ -257,16 +244,24 @@ defmodule Scholar.Manifold.MDS do
## Examples
iex> x = Nx.iota({4,5})
iex> Scholar.Manifold.MDS.fit(x)
#Nx.Tensor<
f32[4][2]
[
[-2197.154296875, 0.0],
[-1055.148681640625, 0.0],
[1055.148681640625, 0.0],
[2197.154296875, 0.0]
]
>
iex> key = Nx.Random.key(42)
iex> Scholar.Manifold.MDS.fit(x, key: key)
%Scholar.Manifold.MDS{
embedding: Nx.tensor(
[
[0.040477119386196136, -0.4997042417526245],
[-0.35801631212234497, -0.09504470974206924],
[-0.08517580479383469, 0.35293734073638916],
[0.42080432176589966, 0.23617777228355408]
]
),
stress: Nx.tensor(
0.0016479993937537074
),
n_iter: Nx.tensor(
19
)
}
"""
deftransform fit(x) do
opts = NimbleOptions.validate!([], @opts_schema)
Expand All @@ -288,16 +283,24 @@ defmodule Scholar.Manifold.MDS do
## Examples
iex> x = Nx.iota({4,5})
iex> Scholar.Manifold.MDS.fit(x, num_components: 2)
#Nx.Tensor<
f32[4][2]
[
[-2197.154296875, 0.0],
[-1055.148681640625, 0.0],
[1055.148681640625, 0.0],
[2197.154296875, 0.0]
]
>
iex> key = Nx.Random.key(42)
iex> Scholar.Manifold.MDS.fit(x, num_components: 2, key: key)
%Scholar.Manifold.MDS{
embedding: Nx.tensor(
[
[0.040477119386196136, -0.4997042417526245],
[-0.35801631212234497, -0.09504470974206924],
[-0.08517580479383469, 0.35293734073638916],
[0.42080432176589966, 0.23617777228355408]
]
),
stress: Nx.tensor(
0.0016479993937537074
),
n_iter: Nx.tensor(
19
)
}
"""
deftransform fit(x, opts) when is_list(opts) do
opts = NimbleOptions.validate!(opts, @opts_schema)
Expand All @@ -306,7 +309,6 @@ defmodule Scholar.Manifold.MDS do
end

defnp fit_n(x, key, opts) do
# mds_main_loop(x, key, opts)
{best, best_stress, best_iter} = mds_main_loop(x, key, opts)
%__MODULE__{embedding: best, stress: best_stress, n_iter: best_iter}
end
Expand All @@ -325,17 +327,25 @@ defmodule Scholar.Manifold.MDS do
## Examples
iex> x = Nx.iota({4,5})
iex> init = Nx.reverse(Nx.iota({4,5}))
iex> key = Nx.Random.key(42)
iex> init = Nx.reverse(Nx.iota({4,2}))
iex> Scholar.Manifold.MDS.fit(x, init)
#Nx.Tensor<
f32[4][2]
[
[-2197.154296875, 0.0],
[-1055.148681640625, 0.0],
[1055.148681640625, 0.0],
[2197.154296875, 0.0]
]
>
%Scholar.Manifold.MDS{
embedding: Nx.tensor(
[
[0.41079193353652954, 0.41079193353652954],
[0.1369306445121765, 0.1369306445121765],
[-0.1369306445121765, -0.1369306445121765],
[-0.41079193353652954, -0.41079193353652954]
]
),
stress: Nx.tensor(
0.0
),
n_iter: Nx.tensor(
3
)
}
"""
deftransform fit(x, init) do
opts = NimbleOptions.validate!([], @opts_schema)
Expand All @@ -357,17 +367,25 @@ defmodule Scholar.Manifold.MDS do
## Examples
iex> x = Nx.iota({4,5})
iex> init = Nx.reverse(Nx.iota({4,5}))
iex> Scholar.Manifold.MDS.fit(x, init, num_clusters: 3)
#Nx.Tensor<
f32[4][3]
[
[-2197.154296875, 0.0, 0.0],
[-1055.148681640625, 0.0, 0.0],
[1055.148681640625, 0.0, 0.0],
[2197.154296875, 0.0, 0.0]
]
>
iex> key = Nx.Random.key(42)
iex> init = Nx.reverse(Nx.iota({4,3}))
iex> Scholar.Manifold.MDS.fit(x, init, num_components: 3, key: key)
%Scholar.Manifold.MDS{
embedding: Nx.tensor(
[
[0.3354101777076721, 0.3354101777076721, 0.3354101777076721],
[0.11180339753627777, 0.11180339753627777, 0.11180339753627777],
[-0.11180339753627777, -0.11180340498685837, -0.11180339753627777],
[-0.3354102075099945, -0.3354102075099945, -0.3354102075099945]
]
),
stress: Nx.tensor(
2.6645352591003757e-15
),
n_iter: Nx.tensor(
3
)
}
"""
deftransform fit(x, init, opts) when is_list(opts) do
opts = NimbleOptions.validate!(opts, @opts_schema)
Expand All @@ -379,17 +397,4 @@ defmodule Scholar.Manifold.MDS do
{best, best_stress, best_iter} = mds_main_loop(x, init, key, opts)
%__MODULE__{embedding: best, stress: best_stress, n_iter: best_iter}
end

# defn remove_main_diag_indices(tensor) do
# n = Nx.axis_size(tensor, 0)

# temp =
# Nx.broadcast(Nx.s64(0), {n})
# |> Nx.indexed_put(Nx.new_axis(0, -1), Nx.s64(1))
# |> Nx.tile([n - 1])

# Nx.iota({n * (n - 1)}) + Nx.cumulative_sum(temp)
# # indices = Nx.iota({n * (n - 1)}) + Nx.cumulative_sum(temp)
# # Nx.take(Nx.flatten(tensor), indices) |> Nx.reshape({n, n - 1})
# end
end
5 changes: 5 additions & 0 deletions test/scholar/manifold/mds_test.exs
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,5 @@
defmodule Scholar.Manifold.MDSTest do
use Scholar.Case, async: true
alias Scholar.Manifold.MDS
doctest MDS

Check failure on line 4 in test/scholar/manifold/mds_test.exs

View workflow job for this annotation

GitHub Actions / main (1.14.5, 25.3) 

doctest Scholar.Manifold.MDS.fit/2 (2) (Scholar.Manifold.MDSTest)

Check failure on line 4 in test/scholar/manifold/mds_test.exs

View workflow job for this annotation

GitHub Actions / main (1.14.5, 25.3) 

doctest Scholar.Manifold.MDS.fit/3 (3) (Scholar.Manifold.MDSTest)

Check failure on line 4 in test/scholar/manifold/mds_test.exs

View workflow job for this annotation

GitHub Actions / main (1.14.5, 25.3) 

doctest Scholar.Manifold.MDS.fit/1 (1) (Scholar.Manifold.MDSTest)
end

0 comments on commit a3b2a82

Please sign in to comment.