##### 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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