##### Beginning of file

# This file was generated by PredictMD version 0.20.0
# For help, please visit https://www.predictmd.net

import PredictMD

### Begin project-specific settings

PredictMD.require_julia_version("v0.7.0")

PredictMD.require_predictmd_version("0.20.0")

# PredictMD.require_predictmd_version("0.20.0", "0.21.0-")

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

### End project-specific settings

### Begin model output code

import Pkg

try Pkg.add("CSV") catch end
try Pkg.add("DataFrames") catch end
try Pkg.add("DecisionTree") catch end
try Pkg.add("Distributions") catch end
try Pkg.add("FileIO") catch end
try Pkg.add("GLM") catch end
try Pkg.add("JLD2") catch end
try Pkg.add("Knet") catch end
try Pkg.add("LIBSVM") catch end
try Pkg.add("StatsModels") catch end
try Pkg.add("ValueHistories") catch end

import CSV
import DataFrames
import DecisionTree
import Distributions
import FileIO
import GLM
import JLD2
import Knet
import LIBSVM
Kernel = LIBSVM.Kernel
import LinearAlgebra
import Random
import StatsModels
import ValueHistories

Random.seed!(999)

trainingandvalidation_features_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "trainingandvalidation_features_df.csv",
    )
trainingandvalidation_labels_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "trainingandvalidation_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",
    )
validation_features_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "validation_features_df.csv",
    )
validation_labels_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "validation_labels_df.csv",
    )
trainingandvalidation_features_df = CSV.read(
    trainingandvalidation_features_df_filename,
    DataFrames.DataFrame;
    rows_for_type_detect = 100,
    )
trainingandvalidation_labels_df = CSV.read(
    trainingandvalidation_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,
    )
validation_features_df = CSV.read(
    validation_features_df_filename,
    DataFrames.DataFrame;
    rows_for_type_detect = 100,
    )
validation_labels_df = CSV.read(
    validation_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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