Merge branch 'dev'

Author: MegamindHenry

Project.toml

@@ -1,7 +1,7 @@
 name = "JudiLing"
 uuid = "b43a184b-0e9d-488b-813a-80fd5dbc9fd8"
 authors = ["Xuefeng Luo"]
-version = "0.4.7"
+version = "0.4.8"
 
 [deps]
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"

docs/src/index.md

@@ -795,7 +795,7 @@ Parameters for Cholesky mode are:
 - `shift::Float64 = 0.02`: shift value for :additive decomposition
 - `multiplier::Float64 = 1.01`: multiplier value for :multiplicative decomposition
 - `output_format::Symbol = :auto`: to force output format to dense(:dense) or sparse(:sparse), make it auto(:auto) to determined by the program
-- `sparse_ratio::Float64 = 0.2`: the ratio to decide whether a matrix is sparse
+- `sparse_ratio::Float64 = 0.05`: the ratio to decide whether a matrix is sparse
 
 ```julia
 JudiLing.test_combo(
@@ -812,7 +812,7 @@ JudiLing.test_combo(
     method = :additive,
     shift = 0.02,
     output_format = :auto,
-    sparse_ratio = 0.2,
+    sparse_ratio = 0.05,
     verbose = true
     )
 ```

src/cholesky.jl

@@ -75,7 +75,7 @@ function make_transform_matrix(
     X::Union{SparseMatrixCSC,Matrix},
     Y::Union{SparseMatrixCSC,Matrix};
     output_format = :auto,
-    sparse_ratio = 0.2,
+    sparse_ratio = 0.05,
     verbose = false,
 )
 
@@ -103,7 +103,7 @@ where X is a sparse matrix and Y is a dense matrix.
 - `shift::Float64 = 0.02`: shift value for :additive decomposition
 - `multiplier::Float64 = 1.01`: multiplier value for :multiplicative decomposition
 - `output_format::Symbol = :auto`: to force output format to dense(:dense) or sparse(:sparse), make it auto(:auto) to determined by the program
-- `sparse_ratio::Float64 = 0.2`: the ratio to decide whether a matrix is sparse
+- `sparse_ratio::Float64 = 0.05`: the ratio to decide whether a matrix is sparse
 - `verbose::Bool = false`: if true, more information will be printed out
 
 # Examples
@@ -128,7 +128,7 @@ JudiLing.make_transform_matrix(
 JudiLing.make_transform_matrix(
   ...
     output_format = :auto,
-    sparse_ratio = 0.2,
+    sparse_ratio = 0.05,
   ...)
 ```
 """
@@ -139,7 +139,7 @@ function make_transform_matrix(
     shift = 0.02,
     multiplier = 1.01,
     output_format = :auto,
-    sparse_ratio = 0.2,
+    sparse_ratio = 0.05,
     verbose = false,
 )
 
@@ -181,7 +181,7 @@ where X is a dense matrix and Y is either a dense matrix or a sparse matrix.
 - `shift::Float64 = 0.02`: shift value for :additive decomposition
 - `multiplier::Float64 = 1.01`: multiplier value for :multiplicative decomposition
 - `output_format::Symbol = :auto`: to force output format to dense(:dense) or sparse(:sparse), make it auto(:auto) to determined by the program
-- `sparse_ratio::Float64 = 0.2`: the ratio to decide whether a matrix is sparse
+- `sparse_ratio::Float64 = 0.05`: the ratio to decide whether a matrix is sparse
 - `verbose::Bool = false`: if true, more information will be printed out
 
 # Examples
@@ -206,7 +206,7 @@ JudiLing.make_transform_matrix(
 JudiLing.make_transform_matrix(
     ...
     output_format = :auto,
-    sparse_ratio = 0.2,
+    sparse_ratio = 0.05,
     ...)
 ```
 """
@@ -217,7 +217,7 @@ function make_transform_matrix(
     shift = 0.02,
     multiplier = 1.01,
     output_format = :auto,
-    sparse_ratio = 0.2,
+    sparse_ratio = 0.05,
     verbose = false,
 )
 
@@ -259,7 +259,7 @@ where X is a sparse matrix and Y is a sparse matrix.
 - `shift::Float64 = 0.02`: shift value for :additive decomposition
 - `multiplier::Float64 = 1.01`: multiplier value for :multiplicative decomposition
 - `output_format::Symbol = :auto`: to force output format to dense(:dense) or sparse(:sparse), make it auto(:auto) to determined by the program
-- `sparse_ratio::Float64 = 0.2`: the ratio to decide whether a matrix is sparse
+- `sparse_ratio::Float64 = 0.05`: the ratio to decide whether a matrix is sparse
 - `verbose::Bool = false`: if true, more information will be printed out
 
 # Examples
@@ -284,7 +284,7 @@ JudiLing.make_transform_matrix(
 JudiLing.make_transform_matrix(
     ...
     output_format = :auto,
-    sparse_ratio = 0.2,
+    sparse_ratio = 0.05,
     ...)
 ```
 """
@@ -295,7 +295,7 @@ function make_transform_matrix(
     shift = 0.02,
     multiplier = 1.01,
     output_format = :auto,
-    sparse_ratio = 0.2,
+    sparse_ratio = 0.05,
     verbose = false,
 )
 
@@ -332,7 +332,7 @@ Convert output matrix format to either a dense matrix or a sparse matrix.
 function format_matrix(
     M::Union{SparseMatrixCSC,Matrix},
     output_format = :auto;
-    sparse_ratio = 0.2,
+    sparse_ratio = 0.05,
     verbose = false,
 )
 

src/find_path.jl

@@ -67,7 +67,7 @@ word, which n-grams are best supported for a given position in the sequence of n
 - `target_col::Union{String, :Symbol}=:Words`: the column name for target strings
 - `start_end_token::Union{String, Char}="#"`: start and end token in boundary cues
 - `issparse::Symbol=:auto`: control of whether output of Mt matrix is a dense matrix or a sparse matrix
-- `sparse_ratio::Float64=0.2`: the ratio to decide whether a matrix is sparse
+- `sparse_ratio::Float64=0.05`: the ratio to decide whether a matrix is sparse
 - `if_pca::Bool=false`: turn on to enable pca mode
 - `pca_eval_M::Matrix=nothing`: pass original F for pca mode
 - `verbose::Bool=false`: if true, more information is printed
@@ -160,7 +160,7 @@ check_gold_path=true,
 res_val = JudiLing.learn_paths(
 ...
 issparse=:auto,
-sparse_ratio=0.2,
+sparse_ratio=0.05,
 ...)
 
 # pca mode
@@ -217,7 +217,7 @@ function learn_paths(
     target_col = "Words",
     start_end_token = "#",
     issparse = :auto,
-    sparse_ratio = 0.2,
+    sparse_ratio = 0.05,
     if_pca = false,
     pca_eval_M = nothing,
     verbose = false,

src/test_combo.jl

@@ -237,10 +237,6 @@ function test_combo(test_mode; kwargs...)
         start_end_token,
         if_combined,
         verbose)
-    for i in 1:10
-        display(cue_obj_train.i2f[i])
-        display(cue_obj_val.i2f[i])
-    end
 
     verbose && println("="^20)
     verbose && println("Making S matrix...")
@@ -502,8 +498,13 @@ function test_combo(test_mode; kwargs...)
     acc_Shat_train_homo = eval_SC(Shat_train, S_train, data_train, n_grams_target_col)
 
     acc_Chat_val = eval_SC(Chat_val, cue_obj_val.C)
+    acc_Chat_val_tv = eval_SC(Chat_val, cue_obj_val.C, cue_obj_train.C)
+
     acc_Shat_val = eval_SC(Shat_val, S_val)
+    acc_Shat_val_tv = eval_SC(Shat_val, S_val, S_train)
+
     acc_Shat_val_homo = eval_SC(Shat_val, S_val, data_val, n_grams_target_col)
+    acc_Shat_val_homo_tv = eval_SC(Shat_val, S_val, S_train, data_val, data_train, n_grams_target_col)
 
     acc_learn_train = JudiLing.eval_acc(
         res_learn_train,
@@ -541,8 +542,11 @@ function test_combo(test_mode; kwargs...)
     println(accio, "Acc for Shat train: $acc_Shat_train")
     println(accio, "Acc for Shat train homophones: $acc_Shat_train_homo")
     println(accio, "Acc for Chat val: $acc_Chat_val")
+    println(accio, "Acc for Chat val for both train and val: $acc_Chat_val_tv")
     println(accio, "Acc for Shat val: $acc_Shat_val")
+    println(accio, "Acc for Acc for Shat val for both train and val: $acc_Shat_val_tv")
     println(accio, "Acc for Shat val homophones: $acc_Shat_val_homo")
+    println(accio, "Acc for Shat val homophones for both train and val: $acc_Shat_val_homo_tv")
     println(accio, "Acc for learn_path train: $acc_learn_train")
     println(accio, "Acc for learn_path val: $acc_learn_val")
     println(accio, "Acc for build_path train: $acc_build_train")