##### Beginning of file

# This file was generated by PredictMD version 0.24.0
# For help, please visit https://predictmd.net

import PredictMD

### Begin project-specific settings

PredictMD.require_julia_version("v1.1.0")

PredictMD.require_predictmd_version("0.24.0")

# PredictMD.require_predictmd_version("0.24.0", "0.25.0-")

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



### End project-specific settings

### Begin model output 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)

PredictMD.predict_proba(
    logistic_classifier,
    smoted_training_features_df,
    )
PredictMD.predict_proba(
    random_forest_classifier,
    smoted_training_features_df,
    )
PredictMD.predict_proba(
    c_svc_svm_classifier,
    smoted_training_features_df,
    )
PredictMD.predict_proba(
    nu_svc_svm_classifier,
    smoted_training_features_df,
    )
PredictMD.predict_proba(
    knet_mlp_classifier,
    smoted_training_features_df,
    )

PredictMD.predict_proba(logistic_classifier,testing_features_df,)
PredictMD.predict_proba(random_forest_classifier,testing_features_df,)
PredictMD.predict_proba(c_svc_svm_classifier,testing_features_df,)
PredictMD.predict_proba(nu_svc_svm_classifier,testing_features_df,)
PredictMD.predict_proba(knet_mlp_classifier,testing_features_df,)

PredictMD.predict(logistic_classifier,smoted_training_features_df,)
PredictMD.predict(random_forest_classifier,smoted_training_features_df,)
PredictMD.predict(c_svc_svm_classifier,smoted_training_features_df,)
PredictMD.predict(nu_svc_svm_classifier,smoted_training_features_df,)
PredictMD.predict(knet_mlp_classifier,smoted_training_features_df,)

PredictMD.predict(logistic_classifier,testing_features_df,)
PredictMD.predict(random_forest_classifier,testing_features_df,)
PredictMD.predict(c_svc_svm_classifier,testing_features_df,)
PredictMD.predict(nu_svc_svm_classifier,testing_features_df,)
PredictMD.predict(knet_mlp_classifier,testing_features_df,)

### End model output code



##### End of file

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