# This file was generated by PredictMD version 0.34.21
# For help, please visit https://predictmd.net

using PredictMDExtra
PredictMDExtra.import_all()

using PredictMD
PredictMD.import_all()



### Begin project-specific settings

DIRECTORY_CONTAINING_THIS_FILE = @__DIR__
PROJECT_DIRECTORY = dirname(
    joinpath(splitpath(DIRECTORY_CONTAINING_THIS_FILE)...)
    )
PROJECT_OUTPUT_DIRECTORY = joinpath(
    PROJECT_DIRECTORY,
    "output",
    )
mkpath(PROJECT_OUTPUT_DIRECTORY)
mkpath(joinpath(PROJECT_OUTPUT_DIRECTORY, "data"))
mkpath(joinpath(PROJECT_OUTPUT_DIRECTORY, "models"))
mkpath(joinpath(PROJECT_OUTPUT_DIRECTORY, "plots"))



### End project-specific settings

### Begin data preprocessing code

Random.seed!(999)

df = RDatasets.dataset("MASS", "Boston")

# PredictMD requires that you provide your data in a DataFrame.

# If your data are in a CSV file (e.g. "data.csv"), load them into
# a DataFrame named `df` with:
# df = DataFrames.DataFrame(CSVFiles.load("data.csv"; type_detect_rows = 10_000))

# If your data are in a gzipped CSV file (e.g. "data.csv.gz"), load them into
# a DataFrame named `df` with:
# df = DataFrames.DataFrame(CSVFiles.load(CSVFiles.File(CSVFiles.format"CSV", "data.csv.gz"); type_detect_rows = 10_000))

# If your data are in some other format, use the appropriate Julia package to
# load your data into a DataFrame named `df`.



categorical_feature_names = Symbol[]
continuous_feature_names = Symbol[
    :Crim,
    :Zn,
    :Indus,
    :Chas,
    :NOx,
    :Rm,
    :Age,
    :Dis,
    :Rad,
    :Tax,
    :PTRatio,
    :Black,
    :LStat,
    ]
categorical_feature_names_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "data",
    "categorical_feature_names.jld2",
    )
continuous_feature_names_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "data",
    "continuous_feature_names.jld2",
    )



FileIO.save(
    categorical_feature_names_filename,
    "categorical_feature_names",
    categorical_feature_names,
    )
FileIO.save(
    continuous_feature_names_filename,
    "continuous_feature_names",
    continuous_feature_names,
    )
feature_names = vcat(categorical_feature_names, continuous_feature_names)

single_label_name = :MedV

continuous_label_names = Symbol[single_label_name]
categorical_label_names = Symbol[]
label_names = vcat(categorical_label_names, continuous_label_names)

df = df[:, vcat(feature_names, label_names)]
DataFrames.dropmissing!(df; disallowmissing=true,)
PredictMD.shuffle_rows!(df)

PredictMD.fix_column_types!(
    df;
    categorical_feature_names = categorical_feature_names,
    continuous_feature_names = continuous_feature_names,
    categorical_label_names = categorical_label_names,
    continuous_label_names = continuous_label_names,
    )
PredictMD.check_column_types(
    df;
    categorical_feature_names = categorical_feature_names,
    continuous_feature_names = continuous_feature_names,
    categorical_label_names = categorical_label_names,
    continuous_label_names = continuous_label_names,
    )



features_df = df[feature_names]
labels_df = df[label_names]

DataFrames.describe(labels_df[single_label_name])

(trainingandtuning_features_df,
    trainingandtuning_labels_df,
    testing_features_df,
    testing_labels_df,) = PredictMD.split_data(
        features_df,
        labels_df,
        0.75,
        )
(training_features_df,
    training_labels_df,
    tuning_features_df,
    tuning_labels_df,) = PredictMD.split_data(
        trainingandtuning_features_df,
        trainingandtuning_labels_df,
        2/3,
        )

trainingandtuning_features_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "data",
    "trainingandtuning_features_df.csv",
    )
trainingandtuning_labels_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "data",
    "trainingandtuning_labels_df.csv",
    )
testing_features_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "data",
    "testing_features_df.csv",
    )
testing_labels_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "data",
    "testing_labels_df.csv",
    )
training_features_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "data",
    "training_features_df.csv",
    )
training_labels_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "data",
    "training_labels_df.csv",
    )
tuning_features_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "data",
    "tuning_features_df.csv",
    )
tuning_labels_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "data",
    "tuning_labels_df.csv",
    )
FileIO.save(trainingandtuning_features_df_filename, trainingandtuning_features_df)
FileIO.save(trainingandtuning_labels_df_filename, trainingandtuning_labels_df)
FileIO.save(testing_features_df_filename, testing_features_df)
FileIO.save(testing_labels_df_filename, testing_labels_df)
FileIO.save(training_features_df_filename, training_features_df)
FileIO.save(training_labels_df_filename, training_labels_df)
FileIO.save(tuning_features_df_filename, tuning_features_df)
FileIO.save(tuning_labels_df_filename, tuning_labels_df)



### End data preprocessing code



# This file was generated by PredictMD version 0.34.21
# For help, please visit https://predictmd.net

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