5. C-SVC support vector machine classifier
# This file was generated by PredictMD version 0.32.0, code name Cephalosporin
# For help, please visit https://predictmd.net

import PredictMDExtra
PredictMDExtra.import_all()

import 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 C-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,
    )

[email protected]_throws ErrorException PredictMD.single_labelmulticlassdataframesvmclassifier(
    feature_names,
    single_label_name,
    single_label_levels;
    package = :packagethatdoesnotexist,
    svmtype = LIBSVM.SVC,
    name = "SVM (C-SVC)",
    verbose = false,
    feature_contrasts = feature_contrasts,
    )

c_svc_svm_classifier =
    PredictMD.single_labelmulticlassdataframesvmclassifier(
        feature_names,
        single_label_name,
        single_label_levels;
        package = :LIBSVM,
        svmtype = LIBSVM.SVC,
        name = "SVM (C-SVC)",
        verbose = false,
        feature_contrasts = feature_contrasts,
        )

PredictMD.fit!(
    c_svc_svm_classifier,
    smoted_training_features_df,
    smoted_training_labels_df,
    )

c_svc_svm_classifier_hist_training =
    PredictMD.plotsinglelabelbinaryclassifierhistogram(
        c_svc_svm_classifier,
        smoted_training_features_df,
        smoted_training_labels_df,
        single_label_name,
        single_label_levels,
        );



display(c_svc_svm_classifier_hist_training)
PredictMD.save_plot(
    joinpath(
        PROJECT_OUTPUT_DIRECTORY,
        "plots",
        "c_svc_svm_classifier_hist_training.pdf",
        ),
    c_svc_svm_classifier_hist_training,
    )

c_svc_svm_classifier_hist_testing =
    PredictMD.plotsinglelabelbinaryclassifierhistogram(
        c_svc_svm_classifier,
        testing_features_df,
        testing_labels_df,
        single_label_name,
        single_label_levels,
        );



display(c_svc_svm_classifier_hist_testing)
PredictMD.save_plot(
    joinpath(
        PROJECT_OUTPUT_DIRECTORY,
        "plots",
        "c_svc_svm_classifier_hist_testing.pdf",
        ),
    c_svc_svm_classifier_hist_testing,
    )

show(
    PredictMD.singlelabelbinaryclassificationmetrics(
        c_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(
        c_svc_svm_classifier,
        testing_features_df,
        testing_labels_df,
        single_label_name,
        positive_class;
        sensitivity = 0.95,
        );
    allrows = true,
    allcols = true,
    splitcols = false,
    )

c_svc_svm_classifier_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "models",
    "c_svc_svm_classifier.jld2",
    )

PredictMD.save_model(c_svc_svm_classifier_filename, c_svc_svm_classifier)

### End C-SVC code



# This file was generated by PredictMD version 0.32.0, code name Cephalosporin
# For help, please visit https://predictmd.net

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