##### 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",
    "boston_housing_example",
    )



### End project-specific settings

### Begin random forest regression code

import PredictMDFull

import Pkg
try Pkg.add("StatsBase") catch end
import StatsBase

import Statistics

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,
    )

categorical_feature_names_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "categorical_feature_names.jld2",
    )
continuous_feature_names_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "continuous_feature_names.jld2",
    )
categorical_feature_names = FileIO.load(
    categorical_feature_names_filename,
    "categorical_feature_names",
    )
continuous_feature_names = FileIO.load(
    continuous_feature_names_filename,
    "continuous_feature_names",
    )
feature_names = vcat(categorical_feature_names, continuous_feature_names)

single_label_name = :MedV

continuous_label_names = Symbol[single_label_name]
categorical_label_names = Symbol[]
label_names = vcat(categorical_label_names, continuous_label_names)

feature_contrasts = PredictMD.generate_feature_contrasts(
    training_features_df,
    feature_names,
    )

random_forest_regression =
    PredictMD.single_labeldataframerandomforestregression(
        feature_names,
        single_label_name;
        nsubfeatures = 2,
        ntrees = 20,
        package = :DecisionTree,
        name = "Random forest",
        feature_contrasts = feature_contrasts,
        )

PredictMD.fit!(
    random_forest_regression,
    training_features_df,
    training_labels_df,
    )

random_forest_regression_plot_training =
    PredictMD.plotsinglelabelregressiontrueversuspredicted(
        random_forest_regression,
        training_features_df,
        training_labels_df,
        single_label_name,
        );



display(random_forest_regression_plot_training)

random_forest_regression_plot_testing =
    PredictMD.plotsinglelabelregressiontrueversuspredicted(
        random_forest_regression,
        testing_features_df,
        testing_labels_df,
        single_label_name,
        );



display(random_forest_regression_plot_testing)

PredictMD.singlelabelregressionmetrics(
    random_forest_regression,
    training_features_df,
    training_labels_df,
    single_label_name,
    )

PredictMD.singlelabelregressionmetrics(
    random_forest_regression,
    testing_features_df,
    testing_labels_df,
    single_label_name,
    )

random_forest_regression_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "random_forest_regression.jld2",
    )

PredictMD.save_model(
    random_forest_regression_filename,
    random_forest_regression
    )

### End random forest regression code



##### End of file

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