##### 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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