##### Beginning of file
# This file was generated by PredictMD version 0.25.0
# For help, please visit https://predictmd.net
import PredictMD
### Begin project-specific settings
PredictMD.require_julia_version("v1.1.0")
PredictMD.require_predictmd_version("0.25.0")
# PredictMD.require_predictmd_version("0.25.0", "0.26.0-")
PROJECT_OUTPUT_DIRECTORY = PredictMD.project_directory(
homedir(),
"Desktop",
"breast_cancer_biopsy_example",
)
### End project-specific settings
### Begin model comparison code
import PredictMDFull
import Pkg
try Pkg.add("StatsBase") catch end
import StatsBase
import Statistics
Kernel = LIBSVM.Kernel
import LinearAlgebra
import Random
import Statistics
try Pkg.add("GLM") catch end
try Pkg.add("Distributions") catch end
try Pkg.add("StatsModels") catch end
import GLM
import Distributions
import StatsModels
Random.seed!(999)
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",
)
trainingandtuning_features_df = CSV.read(
trainingandtuning_features_df_filename,
DataFrames.DataFrame;
rows_for_type_detect = 100,
)
trainingandtuning_labels_df = CSV.read(
trainingandtuning_labels_df_filename,
DataFrames.DataFrame;
rows_for_type_detect = 100,
)
testing_features_df = CSV.read(
testing_features_df_filename,
DataFrames.DataFrame;
rows_for_type_detect = 100,
)
testing_labels_df = CSV.read(
testing_labels_df_filename,
DataFrames.DataFrame;
rows_for_type_detect = 100,
)
training_features_df = CSV.read(
training_features_df_filename,
DataFrames.DataFrame;
rows_for_type_detect = 100,
)
training_labels_df = CSV.read(
training_labels_df_filename,
DataFrames.DataFrame;
rows_for_type_detect = 100,
)
tuning_features_df = CSV.read(
tuning_features_df_filename,
DataFrames.DataFrame;
rows_for_type_detect = 100,
)
tuning_labels_df = CSV.read(
tuning_labels_df_filename,
DataFrames.DataFrame;
rows_for_type_detect = 100,
)
smoted_training_features_df_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"smoted_training_features_df.csv",
)
smoted_training_labels_df_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"smoted_training_labels_df.csv",
)
smoted_training_features_df = CSV.read(
smoted_training_features_df_filename,
DataFrames.DataFrame;
rows_for_type_detect = 100,
)
smoted_training_labels_df = CSV.read(
smoted_training_labels_df_filename,
DataFrames.DataFrame;
rows_for_type_detect = 100,
)
logistic_classifier_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"logistic_classifier.jld2",
)
random_forest_classifier_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"random_forest_classifier.jld2",
)
c_svc_svm_classifier_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"c_svc_svm_classifier.jld2",
)
nu_svc_svm_classifier_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"nu_svc_svm_classifier.jld2",
)
knet_mlp_classifier_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"knet_mlp_classifier.jld2",
)
logistic_classifier =
PredictMD.load_model(logistic_classifier_filename)
random_forest_classifier =
PredictMD.load_model(random_forest_classifier_filename)
c_svc_svm_classifier =
PredictMD.load_model(c_svc_svm_classifier_filename)
nu_svc_svm_classifier =
PredictMD.load_model(nu_svc_svm_classifier_filename)
knet_mlp_classifier =
PredictMD.load_model(knet_mlp_classifier_filename)
PredictMD.parse_functions!(knet_mlp_classifier)
all_models = PredictMD.Fittable[
logistic_classifier,
random_forest_classifier,
c_svc_svm_classifier,
nu_svc_svm_classifier,
knet_mlp_classifier,
]
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)
println(
string(
"Single label binary classification metrics, training set, ",
"fix sensitivity",
)
)
show(
PredictMD.singlelabelbinaryclassificationmetrics(
all_models,
training_features_df,
training_labels_df,
single_label_name,
positive_class;
sensitivity = 0.95,
),
allcols=true,
)
println(
string(
"Single label binary classification metrics, training set, ",
"fix specificity",
)
)
show(
PredictMD.singlelabelbinaryclassificationmetrics(
all_models,
training_features_df,
training_labels_df,
single_label_name,
positive_class;
specificity = 0.95,
),
allcols=true,
)
println(
string(
"Single label binary classification metrics, training set, ",
"maximize F1 score",
)
)
show(
PredictMD.singlelabelbinaryclassificationmetrics(
all_models,
training_features_df,
training_labels_df,
single_label_name,
positive_class;
maximize = :f1score,
),
allcols=true,
)
println(
string(
"Single label binary classification metrics, training set, ",
"maximize Cohen's kappa",
)
)
show(
PredictMD.singlelabelbinaryclassificationmetrics(
all_models,
training_features_df,
training_labels_df,
single_label_name,
positive_class;
maximize = :cohen_kappa,
),
allcols=true,
)
println(
string(
"Single label binary classification metrics, testing set, ",
"fix sensitivity",
)
)
show(
PredictMD.singlelabelbinaryclassificationmetrics(
all_models,
testing_features_df,
testing_labels_df,
single_label_name,
positive_class;
sensitivity = 0.95,
),
allcols=true,
)
println(
string(
"Single label binary classification metrics, testing set, ",
"fix specificity",
)
)
show(
PredictMD.singlelabelbinaryclassificationmetrics(
all_models,
testing_features_df,
testing_labels_df,
single_label_name,
positive_class;
specificity = 0.95,
),
allcols=true,
)
println(
string(
"Single label binary classification metrics, testing set, ",
"maximize F1 score",
)
)
show(
PredictMD.singlelabelbinaryclassificationmetrics(
all_models,
testing_features_df,
testing_labels_df,
single_label_name,
positive_class;
maximize = :f1score,
),
allcols=true,
)
println(
string(
"Single label binary classification metrics, testing set, ",
"maximize Cohen's kappa",
)
)
show(
PredictMD.singlelabelbinaryclassificationmetrics(
all_models,
testing_features_df,
testing_labels_df,
single_label_name,
positive_class;
maximize = :cohen_kappa,
),
allcols=true,
)
rocplottesting = PredictMD.plotroccurves(
all_models,
testing_features_df,
testing_labels_df,
single_label_name,
positive_class,
);
display(rocplottesting)
prplottesting = PredictMD.plotprcurves(
all_models,
testing_features_df,
testing_labels_df,
single_label_name,
positive_class,
);
display(prplottesting)
### End model comparison code
##### End of file
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