Merge branch '0.4.2'

Author: MegamindHenry

Manifest.toml

@@ -16,10 +16,10 @@ uuid = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 version = "0.5.0"
 
 [[CSV]]
-deps = ["CategoricalArrays", "DataFrames", "Dates", "Mmap", "Parsers", "PooledArrays", "SentinelArrays", "Tables", "Unicode"]
-git-tree-sha1 = "b1f60ea404dc40a76e7f539672ce651079a5a190"
+deps = ["Dates", "Mmap", "Parsers", "PooledArrays", "SentinelArrays", "Tables", "Unicode"]
+git-tree-sha1 = "1f79803452adf73e2d3fc84785adb7aaca14db36"
 uuid = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
-version = "0.7.10"
+version = "0.8.3"
 
 [[CategoricalArrays]]
 deps = ["DataAPI", "Future", "JSON", "Missings", "Printf", "Statistics", "StructTypes", "Unicode"]
@@ -116,9 +116,9 @@ version = "0.2.0"
 
 [[FillArrays]]
 deps = ["LinearAlgebra", "Random", "SparseArrays"]
-git-tree-sha1 = "bed538ad14d132aa8240bb2e8ab82fcd2fd2f548"
+git-tree-sha1 = "4705cc4e212c3c978c60b1b18118ec49b4d731fd"
 uuid = "1a297f60-69ca-5386-bcde-b61e274b549b"
-version = "0.11.3"
+version = "0.11.5"
 
 [[Formatting]]
 deps = ["Printf"]

Project.toml

@@ -1,7 +1,7 @@
 name = "JudiLing"
 uuid = "b43a184b-0e9d-488b-813a-80fd5dbc9fd8"
 authors = ["Xuefeng Luo"]
-version = "0.4.1"
+version = "0.4.2"
 
 [deps]
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
@@ -22,7 +22,7 @@ SuiteSparse = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"
 
 [compat]
 BenchmarkTools = "0.5"
-CSV = "0.7"
+CSV = "0.8"
 DataFrames = "0.21, 0.22"
 DataStructures = "0.18"
 Distributions = "0.23, 0.24"

docs/src/index.md

@@ -51,7 +51,7 @@ We first download and read the csv file into Julia:
 ```julia
 download("https://osf.io/2ejfu/download", "latin.csv")
 
-latin = CSV.DataFrame!(CSV.File(joinpath(@__DIR__, "latin.csv")));
+latin = DataFrame(CSV.File(joinpath(@__DIR__, "latin.csv")));
 ```
 
 and we can inspect the latin dataframe:
@@ -347,8 +347,8 @@ The model also provides functionality for cross-validation. Here, you can downlo
 download("https://osf.io/2ejfu/download", "latin_train.csv")
 download("https://osf.io/bm7y6/download", "latin_val.csv")
 
-latin_train = CSV.DataFrame!(CSV.File(joinpath(@__DIR__, "latin_train.csv")))
-latin_val = CSV.DataFrame!(CSV.File(joinpath(@__DIR__, "latin_val.csv")))
+latin_train = DataFrame(CSV.File(joinpath(@__DIR__, "latin_train.csv")))
+latin_val = DataFrame(CSV.File(joinpath(@__DIR__, "latin_val.csv")))
 ```
 
 Then, we make the C and S matrices passing both training and validation datasets to the `make_cue_matrix` function.

docs/src/man/make_adjacency_matrix.md

@@ -5,7 +5,7 @@ CurrentModule = JudiLing
 # Make Adjacency Matrix
 
 ```@docs
-    make_adjacency_matrix
-    make_adjacency_matrix(i2f)
+    make_full_adjacency_matrix
+    make_full_adjacency_matrix(i2f)
     make_combined_adjacency_matrix(data_train, data_val)
 ```

docs/src/man/preprocess.md

@@ -5,9 +5,10 @@ CurrentModule = JudiLing
 # Preprocess
 
 ```@docs
-    train_val_split
     lpo_cv_split
     loo_cv_split
     lpo_cv_split(p, data_path)
     loo_cv_split(data_path)
+    train_val_random_split(data_path, output_dir_path, data_prefix)
+    train_val_carefully_split(data_path, output_dir_path, data_prefix, n_features_columns)
 ```

docs/src/man/test_combo.md

@@ -5,5 +5,5 @@ CurrentModule = JudiLing
 # Test Combo
 
 ```@docs
-    test_combo
+    test_combo(test_mode;kwargs...)
 ```

examples/estonian.jl

@@ -3,9 +3,9 @@ using CSV # read csv files into dataframes
 
 # load estonian file
 estonian_train =
-    CSV.DataFrame!(CSV.File(joinpath(@__DIR__, "data", "estonian_train.csv")))
+    DataFrame(CSV.File(joinpath(@__DIR__, "data", "estonian_train.csv")))
 estonian_val =
-    CSV.DataFrame!(CSV.File(joinpath(@__DIR__, "data", "estonian_val.csv")))
+    DataFrame(CSV.File(joinpath(@__DIR__, "data", "estonian_val.csv")))
 
 # create C matrixes for training and validation datasets
 cue_obj_train = JudiLing.make_cue_matrix(

examples/latin.jl

@@ -5,7 +5,7 @@ mkpath(joinpath(@__DIR__, "data"))
 download("https://osf.io/2ejfu/download", "latin.csv")
 mv("latin.csv", joinpath(@__DIR__, "data", "latin.csv"), force = true)
 # load latin file
-latin = CSV.DataFrame!(CSV.File(joinpath(@__DIR__, "data", "latin.csv")))
+latin = DataFrame(CSV.File(joinpath(@__DIR__, "data", "latin.csv")))
 display(latin)
 
 # create C matrixes for training datasets
@@ -171,9 +171,9 @@ mv(
 mv("latin_val.csv", joinpath(@__DIR__, "data", "latin_val.csv"), force = true)
 # cross-validation
 latin_train =
-    CSV.DataFrame!(CSV.File(joinpath(@__DIR__, "data", "latin_train.csv")))
+    DataFrame(CSV.File(joinpath(@__DIR__, "data", "latin_train.csv")))
 latin_val =
-    CSV.DataFrame!(CSV.File(joinpath(@__DIR__, "data", "latin_val.csv")))
+    DataFrame(CSV.File(joinpath(@__DIR__, "data", "latin_val.csv")))
 
 # create C matrices for both training and validation datasets
 cue_obj_train, cue_obj_val = JudiLing.make_cue_matrix(

src/make_adjacency_matrix.jl

@@ -1,7 +1,7 @@
 """
 Make full adjacency matrix.
 """
-function make_adjacency_matrix end
+function make_full_adjacency_matrix end
 
 """
     make_adjacency_matrix(i2f)
@@ -34,7 +34,7 @@ JudiLing.make_adjacency_matrix(
 ```
 ...
 """
-function make_adjacency_matrix(
+function make_full_adjacency_matrix(
     i2f;
     tokenized = false,
     sep_token = nothing,

src/make_yt_matrix.jl

@@ -22,7 +22,7 @@ for the corresponding n-gram predicted for timestep t for each of the observatio
 
 # Examples
 ```julia
-latin = CSV.DataFrame!(CSV.File(joinpath("data", "latin_mini.csv")))
+latin = DataFrame(CSV.File(joinpath("data", "latin_mini.csv")))
 JudiLing.make_Yt_matrix(2, latin)
 ```
 ...