Graham McIntire
cb8445f329
- Add 17 missing @spec annotations (layouts, error_json, error_html, skewt_svg) - Move 12+ nested alias/import/require to module top level - Add phx-change/id attributes to 11 raw HTML <form> tags - Remove 4 unused LiveView assigns (:bounds, :data_provider) - Add 3 missing doctest references (HrrrNativeClient, BulkFetch, Accounts) - Break 2 long lines (path_compute.ex:382) - Strengthen weak test assertions (is_binary→byte_size, is_list→!=[]) - Replace Module.concat with Module.safe_concat (2 occurrences) - Replace length/1 > 0 with list != [] (9 occurrences) - Remove no-op assert true, fix no-assertion tests Remaining: 24 socket.assigns introspection warnings (deliberate test pattern for observable behavior testing), 1 formatter-resistant long line, 3 app-code usage warnings.
306 lines
9 KiB
Elixir
306 lines
9 KiB
Elixir
defmodule Microwaveprop.Propagation.ModelTest do
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use ExUnit.Case, async: true
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alias Microwaveprop.Propagation.Model
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describe "build/0" do
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test "returns an Axon model" do
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model = Model.build()
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assert %Axon{} = model
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end
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end
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describe "init/0" do
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test "returns initialized model state with parameters" do
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params = Model.init()
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assert %Axon.ModelState{} = params
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assert Map.has_key?(params.data, "hidden_1")
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assert Map.has_key?(params.data, "hidden_2")
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assert Map.has_key?(params.data, "hidden_3")
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assert Map.has_key?(params.data, "output")
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end
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end
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describe "save/2 and load/1" do
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@tag :tmp_dir
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test "round-trips parameters to disk", %{tmp_dir: tmp_dir} do
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path = Path.join(tmp_dir, "test_model.nx")
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params = Model.init()
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assert :ok = Model.save(params, path)
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assert File.exists?(path)
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assert {:ok, loaded} = Model.load(path)
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# Predict with both and compare
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input = Nx.broadcast(0.5, {1, 20})
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original = Model.predict(params, input)
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reloaded = Model.predict(loaded, input)
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assert Nx.to_flat_list(original) == Nx.to_flat_list(reloaded)
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end
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test "load returns :error for missing file" do
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assert :error = Model.load("/tmp/nonexistent_model.nx")
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end
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end
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describe "load_or_init/1" do
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@tag :tmp_dir
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test "loads from file when it exists", %{tmp_dir: tmp_dir} do
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path = Path.join(tmp_dir, "test_model.nx")
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params = Model.init()
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Model.save(params, path)
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loaded = Model.load_or_init(path)
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input = Nx.broadcast(0.5, {1, 20})
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assert Nx.to_flat_list(Model.predict(params, input)) == Nx.to_flat_list(Model.predict(loaded, input))
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end
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test "initializes when file missing" do
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params = Model.load_or_init("/tmp/nonexistent_model.nx")
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assert %Axon.ModelState{} = params
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end
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end
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describe "predict/2" do
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test "produces output in [0, 1] range" do
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params = Model.init()
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# Single sample, 20 features
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input = Nx.broadcast(0.5, {1, 20})
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output = Model.predict(params, input)
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assert Nx.shape(output) == {1, 1}
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value = output |> Nx.to_flat_list() |> hd()
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assert value >= 0.0 and value <= 1.0
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end
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test "handles batch of multiple samples" do
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params = Model.init()
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input = Nx.broadcast(0.5, {4, 20})
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output = Model.predict(params, input)
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assert Nx.shape(output) == {4, 1}
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end
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end
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describe "encode_features/1" do
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test "returns 20-element list" do
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features =
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Model.encode_features(%{
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surface_temp_c: 25.0,
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surface_dewpoint_c: 15.0,
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surface_pressure_mb: 1015.0,
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min_refractivity_gradient: -80.0,
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hpbl_m: 600.0,
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pwat_mm: 25.0,
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utc_hour: 18,
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month: 7,
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freq_mhz: 10_000
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})
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assert length(features) == 20
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assert Enum.all?(features, &is_float/1)
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end
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test "uses defaults for missing values" do
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features = Model.encode_features(%{})
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assert length(features) == 20
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assert Enum.all?(features, &is_float/1)
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end
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test "cyclical encoding: hour 0 and hour 24 produce same sin/cos" do
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f0 = Model.encode_features(%{utc_hour: 0})
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f24 = Model.encode_features(%{utc_hour: 24})
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# sin/cos at indices 8, 9
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assert_in_delta Enum.at(f0, 8), Enum.at(f24, 8), 0.001
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assert_in_delta Enum.at(f0, 9), Enum.at(f24, 9), 0.001
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end
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test "log_freq_mhz is last feature" do
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features = Model.encode_features(%{freq_mhz: 10_000})
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assert_in_delta List.last(features), :math.log(10_000) / 13.0, 0.001
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end
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end
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describe "train/3" do
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test "trains on synthetic data and returns state with metrics" do
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# Generate small synthetic dataset: 64 samples, 13 features
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key = Nx.Random.key(42)
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{features, key} = Nx.Random.uniform(key, shape: {64, 20}, type: :f32)
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{targets, _key} = Nx.Random.uniform(key, shape: {64, 1}, type: :f32)
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{trained_state, metrics} = Model.train(features, targets, epochs: 2, batch_size: 32)
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assert %Axon.ModelState{} = trained_state
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assert is_float(metrics.final_loss)
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assert metrics.final_loss >= 0.0
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end
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end
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describe "evaluate/3" do
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test "returns rmse and r_squared" do
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params = Model.init()
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key = Nx.Random.key(42)
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{features, key} = Nx.Random.uniform(key, shape: {32, 20}, type: :f32)
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{targets, _key} = Nx.Random.uniform(key, shape: {32, 1}, type: :f32)
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metrics = Model.evaluate(params, features, targets)
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assert is_float(metrics.rmse)
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assert metrics.rmse >= 0.0
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assert is_float(metrics.r_squared)
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end
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end
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describe "predict_score/3" do
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test "returns integer between 0 and 100" do
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params = Model.init()
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predict_fn = Model.compile_predict()
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score =
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Model.predict_score(predict_fn, params, %{
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surface_temp_c: 25.0,
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surface_dewpoint_c: 15.0,
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surface_pressure_mb: 1015.0,
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min_refractivity_gradient: -80.0,
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hpbl_m: 600.0,
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pwat_mm: 25.0,
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utc_hour: 18,
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month: 7,
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freq_mhz: 10_000
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})
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assert is_integer(score)
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assert score >= 0
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assert score <= 100
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end
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test "returns different scores for different conditions" do
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params = Model.init()
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predict_fn = Model.compile_predict()
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score_hot =
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Model.predict_score(predict_fn, params, %{
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surface_temp_c: 35.0,
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surface_dewpoint_c: 25.0,
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surface_pressure_mb: 1010.0,
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min_refractivity_gradient: -300.0,
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hpbl_m: 200.0,
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pwat_mm: 40.0,
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utc_hour: 3,
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month: 7,
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freq_mhz: 10_000
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})
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score_cold =
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Model.predict_score(predict_fn, params, %{
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surface_temp_c: -10.0,
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surface_dewpoint_c: -20.0,
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surface_pressure_mb: 1030.0,
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min_refractivity_gradient: -40.0,
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hpbl_m: 1500.0,
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pwat_mm: 5.0,
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utc_hour: 14,
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month: 1,
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freq_mhz: 10_000
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})
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# With random init, scores likely differ (not guaranteed but very probable)
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assert is_integer(score_hot)
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assert is_integer(score_cold)
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end
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end
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describe "feature_names/0" do
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test "returns 20 feature names" do
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assert length(Model.feature_names()) == 20
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end
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end
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describe "explain_prediction/3" do
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test "returns score and one contribution entry per feature" do
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params = Model.init()
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predict_fn = Model.compile_predict()
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result =
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Model.explain_prediction(predict_fn, params, %{
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surface_temp_c: 25.0,
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surface_dewpoint_c: 18.0,
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surface_pressure_mb: 1013.0,
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min_refractivity_gradient: -250.0,
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hpbl_m: 800.0,
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pwat_mm: 30.0,
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surface_refractivity: 340.0,
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latitude: 32.5,
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longitude: -97.0,
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sfi: 130.0,
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kp_max: 2.0,
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ducting_detected: true,
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k_index: 25.0,
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lifted_index: -2.0,
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utc_hour: 23,
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month: 7,
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freq_mhz: 10_000
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})
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assert is_integer(result.score)
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assert result.score >= 0 and result.score <= 100
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assert length(result.contributions) == 20
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Enum.each(result.contributions, fn c ->
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assert byte_size(to_string(c.feature)) > 0
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assert c.normalized >= -10_000 and c.normalized <= 10_000
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assert is_float(c.sensitivity)
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assert is_float(c.contribution)
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end)
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end
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test "contributions are sorted by absolute contribution descending" do
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params = Model.init()
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predict_fn = Model.compile_predict()
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result =
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Model.explain_prediction(predict_fn, params, %{
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surface_temp_c: 15.0,
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surface_dewpoint_c: 5.0,
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freq_mhz: 24_000
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})
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magnitudes = Enum.map(result.contributions, &abs(&1.contribution))
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assert magnitudes == Enum.sort(magnitudes, :desc)
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end
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test "every emitted feature name matches feature_names/0" do
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params = Model.init()
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predict_fn = Model.compile_predict()
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result = Model.explain_prediction(predict_fn, params, %{freq_mhz: 47_000})
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emitted = result.contributions |> Enum.map(& &1.feature) |> Enum.sort()
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expected = Enum.sort(Model.feature_names())
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assert emitted == expected
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end
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test "perturbing a feature changes the predicted score" do
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# Verify the sensitivity output is non-trivial: the model must
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# actually move when at least one feature moves. With random init
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# this is essentially guaranteed unless every weight collapses.
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params = Model.init()
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predict_fn = Model.compile_predict()
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result =
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Model.explain_prediction(predict_fn, params, %{
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surface_temp_c: 20.0,
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surface_dewpoint_c: 10.0,
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surface_pressure_mb: 1013.0,
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freq_mhz: 10_000
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})
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max_sensitivity = result.contributions |> Enum.map(&abs(&1.sensitivity)) |> Enum.max()
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assert max_sensitivity > 0.0
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end
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end
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end
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