Merge remote-tracking branch 'refs/remotes/origin/master'

Author: MariaHei

src/eval.jl

@@ -540,6 +540,79 @@ function eval_SC_loose(SChat, SC, k, data, target_col; digits=4)
     round(correct / total, digits=digits)
 end
 
+
+"""
+    eval_SC_loose(SChat, SC, SC_rest, k; digits=4)
+
+Assess model accuracy on the basis of the correlations of row vectors of Chat and
+C or Shat and S. Count it as correct if one of the top k candidates is correct.
+Does not consider homophones.
+Takes into account gold-standard vectors in both the actual targets (SC)
+as well as in a second matrix (e.g. the training or validation data; SC_rest).
+
+# Obligatory Arguments
+- `SChat::Union{SparseMatrixCSC, Matrix}`: the Chat or Shat matrix
+- `SC::Union{SparseMatrixCSC, Matrix}`: the C or S matrix of the data under consideration
+- `SC_rest::Union{SparseMatrixCSC, Matrix}`: the C or S matrix of rest data
+- `k`: top k candidates
+
+# Optional Arguments
+- `digits=4`: the specified number of digits after the decimal place (or before if negative)
+
+```julia
+eval_SC_loose(Chat_val, cue_obj_val.C, cue_obj_train.C, k)
+eval_SC_loose(Shat_val, S_val, S_train, k)
+```
+"""
+function eval_SC_loose(SChat, SC, SC_rest, k; digits=4)
+ SC_combined = vcat(SC, SC_rest)
+ eval_SC_loose(SChat, SC_combined, k, digits=digits)
+end
+
+"""
+    eval_SC_loose(SChat, SC, SC_rest, k, data, data_rest, target_col; digits=4)
+
+Assess model accuracy on the basis of the correlations of row vectors of Chat and
+C or Shat and S. Count it as correct if one of the top k candidates is correct.
+Considers homophones.
+Takes into account gold-standard vectors in both the actual targets (SC)
+as well as in a second matrix (e.g. the training or validation data; SC_rest).
+
+# Obligatory Arguments
+- `SChat::Union{SparseMatrixCSC, Matrix}`: the Chat or Shat matrix
+- `SC::Union{SparseMatrixCSC, Matrix}`: the C or S matrix of the data under consideration
+- `SC_rest::Union{SparseMatrixCSC, Matrix}`: the C or S matrix of rest data
+- `k`: top k candidates
+- `data`: dataset under consideration
+- `data_rest`: remaining dataset
+- `target_col`: target column name
+
+# Optional Arguments
+- `digits=4`: the specified number of digits after the decimal place (or before if negative)
+
+```julia
+eval_SC_loose(Chat_val, cue_obj_val.C, cue_obj_train.C, k, latin_val, latin_train, :Word)
+eval_SC_loose(Shat_val, S_val, S_train, k, latin_val, latin_train, :Word)
+```
+"""
+function eval_SC_loose(SChat, SC, SC_rest, k, data, data_rest, target_col; digits=4)
+    SC_combined = vcat(SC, SC_rest)
+
+    n_data = size(data, 1)
+    n_data_rest = size(data_rest, 1)
+
+    if n_data > n_data_rest
+        data_combined = similar(data, 0)
+    else
+        data_combined = similar(data_rest, 0)
+    end
+
+    append!(data_combined, data)
+    append!(data_combined, data_rest)
+
+    eval_SC_loose(SChat, SC_combined, k, data_combined, target_col, digits=digits)
+end
+
 """
     eval_manual(res, data, i2f)
 

test/eval_tests.jl

@@ -121,6 +121,53 @@ end
         @test JudiLing.eval_SC_loose(Chat, cue_obj.C, k, latin, :Word) == 1
         @test JudiLing.eval_SC_loose(Shat, S, k, latin, :Word) == 1
     end
+
+    latin_train = DataFrame(
+        Word = ["ABC", "BCD", "CDE", "BCD"],
+        Lexeme = ["A", "B", "C", "B"],
+        Person = ["B", "C", "D", "D"],
+    )
+
+    latin_val = DataFrame(
+        Word = ["ABC", "BCD"],
+        Lexeme = ["A", "B"],
+        Person = ["B", "C"],
+    )
+
+    cue_obj_train, cue_obj_val = JudiLing.make_combined_cue_matrix(
+        latin_train,
+        latin_val,
+        grams = 3,
+        target_col = :Word,
+        tokenized = false,
+        keep_sep = false,
+    )
+
+    n_features = size(cue_obj_train.C, 2)
+    S_train, S_val = JudiLing.make_combined_S_matrix(
+        latin_train,
+        latin_val,
+        [:Lexeme],
+        [:Person],
+        ncol = n_features,
+        add_noise = false
+    )
+
+    G = JudiLing.make_transform_matrix(S_train, cue_obj_train.C)
+    Chat_val = S_val * G
+    Chat_train = S_train * G
+    F = JudiLing.make_transform_matrix(cue_obj_train.C, S_train)
+    Shat_val = cue_obj_val.C * F
+    Shat_train = cue_obj_train.C * F
+
+    @test JudiLing.eval_SC_loose(Chat_val, cue_obj_val.C, cue_obj_train.C, 1) >= 0.5
+    @test JudiLing.eval_SC_loose(Chat_val, cue_obj_val.C, cue_obj_train.C, 2) == 1
+    @test JudiLing.eval_SC_loose(Shat_val, S_val, S_train, 1) >= 0.5
+    @test JudiLing.eval_SC_loose(Shat_val, S_val, S_train, 2) == 1
+    @test JudiLing.eval_SC_loose(Chat_val, cue_obj_val.C, cue_obj_train.C, 1, latin_val, latin_train, :Word) == 1
+    @test JudiLing.eval_SC_loose(Chat_val, cue_obj_val.C, cue_obj_train.C, 2, latin_val, latin_train, :Word) == 1
+    @test JudiLing.eval_SC_loose(Shat_val, S_val, S_train, 1, latin_val, latin_train, :Word) == 1
+    @test JudiLing.eval_SC_loose(Shat_val, S_val, S_train, 2, latin_val, latin_train, :Word) == 1
 end
 
 @testset "accuracy_comprehension" begin