##### Beginning of file

# This file was generated by PredictMD version 0.27.0
# For help, please visit https://predictmd.net

import PredictMD

### Begin project-specific settings

PredictMD.require_julia_version("v1.1.0")

PredictMD.require_predictmd_version("0.27.0")

# PredictMD.require_predictmd_version("0.27.0", "0.28.0-")

PROJECT_OUTPUT_DIRECTORY = PredictMD.project_directory(
    homedir(),
    "Desktop",
    "breast_cancer_biopsy_example",
    )



### End project-specific settings

### Begin data preprocessing code

import PredictMDFull

Random.seed!(999)

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

categorical_feature_names = Symbol[]
continuous_feature_names = Symbol[
    :V1,
    :V2,
    :V3,
    :V4,
    :V5,
    :V6,
    :V7,
    :V8,
    :V9,
    ]
categorical_feature_names_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "categorical_feature_names.jld2",
    )
continuous_feature_names_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "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 = :Class
negative_class = "benign"
positive_class = "malignant"
single_label_levels = [negative_class, positive_class]

categorical_label_names = Symbol[single_label_name]
continuous_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,
    )
PredictMD.check_no_constant_columns(df)

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

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

### End data preprocessing code



##### End of file

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