# 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 nu-SVC code
Kernel = LIBSVM.Kernel
Random.seed!(999)
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",
)
trainingandtuning_features_df = DataFrames.DataFrame(
FileIO.load(
trainingandtuning_features_df_filename;
type_detect_rows = 100,
)
)
trainingandtuning_labels_df = DataFrames.DataFrame(
FileIO.load(
trainingandtuning_labels_df_filename;
type_detect_rows = 100,
)
)
testing_features_df = DataFrames.DataFrame(
FileIO.load(
testing_features_df_filename;
type_detect_rows = 100,
)
)
testing_labels_df = DataFrames.DataFrame(
FileIO.load(
testing_labels_df_filename;
type_detect_rows = 100,
)
)
training_features_df = DataFrames.DataFrame(
FileIO.load(
training_features_df_filename;
type_detect_rows = 100,
)
)
training_labels_df = DataFrames.DataFrame(
FileIO.load(
training_labels_df_filename;
type_detect_rows = 100,
)
)
tuning_features_df = DataFrames.DataFrame(
FileIO.load(
tuning_features_df_filename;
type_detect_rows = 100,
)
)
tuning_labels_df = DataFrames.DataFrame(
FileIO.load(
tuning_labels_df_filename;
type_detect_rows = 100,
)
)
smoted_training_features_df_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"data",
"smoted_training_features_df.csv",
)
smoted_training_labels_df_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"data",
"smoted_training_labels_df.csv",
)
smoted_training_features_df = DataFrames.DataFrame(
FileIO.load(
smoted_training_features_df_filename;
type_detect_rows = 100,
)
)
smoted_training_labels_df = DataFrames.DataFrame(
FileIO.load(
smoted_training_labels_df_filename;
type_detect_rows = 100,
)
)
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",
)
categorical_feature_names = FileIO.load(
categorical_feature_names_filename,
"categorical_feature_names",
)
continuous_feature_names = FileIO.load(
continuous_feature_names_filename,
"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)
feature_contrasts = PredictMD.generate_feature_contrasts(
smoted_training_features_df,
feature_names,
)
nu_svc_svm_classifier =
PredictMD.single_labelmulticlassdataframesvmclassifier(
feature_names,
single_label_name,
single_label_levels;
package = :LIBSVM,
svmtype = LIBSVM.NuSVC,
name = "SVM (nu-SVC)",
verbose = false,
feature_contrasts = feature_contrasts,
)
PredictMD.fit!(
nu_svc_svm_classifier,
smoted_training_features_df,
smoted_training_labels_df,
)
nu_svc_svm_classifier_hist_training =
PredictMD.plotsinglelabelbinaryclassifierhistogram(
nu_svc_svm_classifier,
smoted_training_features_df,
smoted_training_labels_df,
single_label_name,
single_label_levels,
);
display(nu_svc_svm_classifier_hist_training)
PredictMD.save_plot(
joinpath(
PROJECT_OUTPUT_DIRECTORY,
"plots",
"nu_svc_svm_classifier_hist_training.pdf",
),
nu_svc_svm_classifier_hist_training,
)
nu_svc_svm_classifier_hist_testing =
PredictMD.plotsinglelabelbinaryclassifierhistogram(
nu_svc_svm_classifier,
testing_features_df,
testing_labels_df,
single_label_name,
single_label_levels,
);
display(nu_svc_svm_classifier_hist_testing)
PredictMD.save_plot(
joinpath(
PROJECT_OUTPUT_DIRECTORY,
"plots",
"nu_svc_svm_classifier_hist_testing.pdf",
),
nu_svc_svm_classifier_hist_testing,
)
show(
PredictMD.singlelabelbinaryclassificationmetrics(
nu_svc_svm_classifier,
smoted_training_features_df,
smoted_training_labels_df,
single_label_name,
positive_class;
sensitivity = 0.95,
);
allrows = true,
allcols = true,
splitcols = false,
)
show(
PredictMD.singlelabelbinaryclassificationmetrics(
nu_svc_svm_classifier,
testing_features_df,
testing_labels_df,
single_label_name,
positive_class;
sensitivity = 0.95,
);
allrows = true,
allcols = true,
splitcols = false,
)
nu_svc_svm_classifier_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"models",
"nu_svc_svm_classifier.jld2",
)
PredictMD.save_model(
nu_svc_svm_classifier_filename,
nu_svc_svm_classifier,
)
### End nu-SVC code
# This file was generated by PredictMD version 0.34.21
# For help, please visit https://predictmd.netThis page was generated using Literate.jl.