Output

Write results into a csv file. This function takes as input the results from the learn_paths and build_paths functions, including the information on gold paths that is optionally returned as second output result.

Reformat results into a dataframe. This function takes as input the results from the learn_paths and build_paths functions, including the information on gold paths that is optionally returned as second output result.

Write comprehension evaluation into a CSV file, include target and predicted ids and indentifiers and their correlations.

write2csv(res, data, cue_obj_train, cue_obj_val, filename)

Write results into csv file for the results from learn_paths and build_paths.

Obligatory Arguments

• res::Array{Array{Result_Path_Info_Struct,1},1}: the results from learn_paths or build_paths
• data::DataFrame: the dataset
• cue_obj_train::Cue_Matrix_Struct: the cue object for training dataset
• cue_obj_val::Cue_Matrix_Struct: the cue object for validation dataset
• filename::String: the filename

Optional Arguments

• grams::Int64=3: the number n in n-gram cues
• tokenized::Bool=false: if true, the dataset target is tokenized
• sep_token::Union{Nothing, String, Char}=nothing: separator
• start_end_token::Union{String, Char}="#": start and end token in boundary cues
• output_sep_token::Union{String, Char}="": output separator
• path_sep_token::Union{String, Char}=":": path separator
• target_col::Union{String, Symbol}=:Words: the column name for target strings
• root_dir::String=".": dir path for project root dir
• output_dir::String=".": output dir inside root dir

Examples

# writing results for training data
JudiLing.write2csv(
    res_train,
    latin_train,
     cue_obj_train,
    cue_obj_train,
    "res_latin_train.csv",
    grams=3,
    tokenized=false,
    sep_token=nothing,
    start_end_token="#",
    output_sep_token="",
    path_sep_token=":",
    target_col=:Word,
    root_dir=".",
    output_dir="test_out")

# writing results for validation data
JudiLing.write2csv(
    res_val,
    latin_val,
    cue_obj_train,
    cue_obj_val,
    "res_latin_val.csv",
    grams=3,
    tokenized=false,
    sep_token=nothing,
    start_end_token="#",
    output_sep_token="",
    path_sep_token=":",
    target_col=:Word,
    root_dir=".",
    output_dir="test_out")

write2csv(gpi::Vector{Gold_Path_Info_Struct}, filename)

Write results into csv file for the gold paths' information optionally returned by learn_paths and build_paths.

Obligatory Arguments

• gpi::Vector{Gold_Path_Info_Struct}: the gold paths' information
• filename::String: the filename

Optional Arguments

• root_dir::String=".": dir path for project root dir
• output_dir::String=".": output dir inside root dir

Examples

# write gold standard paths to csv for training data
JudiLing.write2csv(
    gpi_train,
    "gpi_latin_train.csv",
    root_dir=".",
    output_dir="test_out"
    )

# write gold standard paths to csv for validation data
JudiLing.write2csv(
    gpi_val,
    "gpi_latin_val.csv",
    root_dir=".",
    output_dir="test_out"
    )

write2csv(ts::Threshold_Stat_Struct, filename)

Write results into csv file for threshold and tolerance proportion for each timestep.

Obligatory Arguments

• gpi::Vector{Gold_Path_Info_Struct}: the gold paths' information
• filename::String: the filename

Optional Arguments

• root_dir::String=".": dir path for project root dir
• output_dir::String=".": output dir inside root dir

Examples

JudiLing.write2csv(ts, "ts.csv", root_dir = @__DIR__, output_dir="out")

write2df(res, data, cue_obj_train, cue_obj_val)

Reformat results into a dataframe for the results form learn_paths and build_paths functions.

Obligatory Arguments

• res: output of learn_paths or build_paths
• data::DataFrame: the dataset
• cue_obj_train: cue object of the training data set
• cue_obj_val: cue object of the validation data set

Optional Arguments

• grams::Int64=3: the number n in n-gram cues
• tokenized::Bool=false: if true, the dataset target is tokenized
• sep_token::Union{Nothing, String, Char}=nothing: separator
• start_end_token::Union{String, Char}="#": start and end token in boundary cues
• output_sep_token::Union{String, Char}="": output separator
• path_sep_token::Union{String, Char}=":": path separator
• target_col::Union{String, Symbol}=:Words: the column name for target strings

Examples

# writing results for training data
JudiLing.write2df(
    res_train,
    latin_train,
    cue_obj_train,
    cue_obj_train,
    grams=3,
    tokenized=false,
    sep_token=nothing,
    start_end_token="#",
    output_sep_token="",
    path_sep_token=":",
    target_col=:Word)

# writing results for validation data
JudiLing.write2df(
    res_val,
    latin_val,
    cue_obj_train,
    cue_obj_val,
    grams=3,
    tokenized=false,
    sep_token=nothing,
    start_end_token="#",
    output_sep_token="",
    path_sep_token=":",
    target_col=:Word)

write2df(gpi::Vector{Gold_Path_Info_Struct})

Write results into a dataframe for the gold paths' information optionally returned by learn_paths and build_paths.

Obligatory Arguments

• gpi::Vector{Gold_Path_Info_Struct}: the gold paths' information

Examples

# write gold standard paths to df for training data
JudiLing.write2csv(gpi_train)

# write gold standard paths to df for validation data
JudiLing.write2csv(gpi_val)

write2df(ts::Threshold_Stat_Struct)

Write results into a dataframe for threshold and tolerance proportion for each timestep.

Obligatory Arguments

• ts::Threshold_Stat_Struct: the threshold and tolerance proportion

Examples

JudiLing.write2df(ts)

write_comprehension_eval(SChat, SC, data, target_col, filename)

Write comprehension evaluation into a CSV file, include target and predicted ids and indentifiers and their correlations.

Obligatory Arguments

• SChat::Matrix: the Shat/Chat matrix
• SC::Matrix: the S/C matrix
• data::DataFrame: the data
• target_col::Symbol: the name of target column
• filename::String: the filename/filepath

Optional Arguments

• k: top k candidates
• root_dir::String=".": dir path for project root dir
• output_dir::String=".": output dir inside root dir

Examples

JudiLing.write_comprehension_eval(Chat, cue_obj.C, latin, :Word, "output.csv",
    k=10, root_dir=@__DIR__, output_dir="out")

write_comprehension_eval(SChat, SC, SC_rest, data, data_rest, target_col, filename)

Write comprehension evaluation into a CSV file for both training and validation datasets, include target and predicted ids and indentifiers and their correlations.

Obligatory Arguments

• SChat::Matrix: the Shat/Chat matrix
• SC::Matrix: the S/C matrix
• SC_rest::Matrix: the rest S/C matrix
• data::DataFrame: the data
• data_rest::DataFrame: the rest data
• target_col::Symbol: the name of target column
• filename::String: the filename/filepath

Optional Arguments

• k: top k candidates
• root_dir::String=".": dir path for project root dir
• output_dir::String=".": output dir inside root dir

Examples

JudiLing.write_comprehension_eval(Shat_val, S_val, S_train, latin_val, latin_train,
    :Word, "all_output.csv", k=10, root_dir=@__DIR__, output_dir="out")

save_L_matrix(L, filename)

Save lexome matrix into csv file.

Obligatory Arguments

• L::L_Matrix_Struct: the lexome matrix struct
• filename::String: the filename/filepath

Examples

JudiLing.save_L_matrix(L, joinpath(@__DIR__, "L.csv"))

load_L_matrix(filename)

Load lexome matrix from csv file.

Obligatory Arguments

• filename::String: the filename/filepath

Optional Arguments

• header::Bool=false: header in csv

Examples

L_load = JudiLing.load_L_matrix(joinpath(@__DIR__, "L.csv"))

save_S_matrix(S, filename, data, target_col)

Save S matrix into a csv file.

Obligatory Arguments

• S::Matrix: the S matrix
• filename::String: the filename/filepath
• data::DataFrame: the data
• target_col::Symbol: the name of target column

Optional Arguments

• sep::Bool=" ": separator in CSV file

Examples

JudiLing.save_S_matrix(S, joinpath(@__DIR__, "S.csv"), latin, :Word)

load_S_matrix(filename)

Load S matrix from a csv file.

Obligatory Arguments

• filename::String: the filename/filepath

Optional Arguments

• header::Bool=false: header in csv
• sep::Bool=" ": separator in CSV file

Examples

JudiLing.load_S_matrix(joinpath(@__DIR__, "S.csv"))