9. Directly access model output
# This file was generated by PredictMD version 0.31.0, code name Basophil
# For help, please visit https://predictmd.net

import PredictMDExtra
PredictMDExtra.import_all()

import PredictMD
PredictMD.import_all()

### Begin project-specific settings

LOCATION_OF_PREDICTMD_GENERATED_EXAMPLE_FILES = "/home/travis/build/bcbi/PredictMD.jl/docs/src/examples"

PROJECT_OUTPUT_DIRECTORY = joinpath(
    LOCATION_OF_PREDICTMD_GENERATED_EXAMPLE_FILES,
    "cpu_examples",
    "breast_cancer_biopsy",
    "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 model output 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,
        )
    )

logistic_classifier_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "models",
    "logistic_classifier.jld2",
    )
random_forest_classifier_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "models",
    "random_forest_classifier.jld2",
    )
c_svc_svm_classifier_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "models",
    "c_svc_svm_classifier.jld2",
    )
nu_svc_svm_classifier_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "models",
    "nu_svc_svm_classifier.jld2",
    )
knet_mlp_classifier_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "models",
    "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



# This file was generated by PredictMD version 0.31.0, code name Basophil
# For help, please visit https://predictmd.net

This page was generated using Literate.jl.