##### Beginning of file

# This file was generated by PredictMD version 0.29.0
# For help, please visit https://predictmd.net

import PredictMD

### Begin project-specific settings

PredictMD.require_julia_version("v1.1.0")

PredictMD.require_predictmd_version("0.29.0")

# PredictMD.require_predictmd_version("0.29.0", "0.30.0-")

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



### End project-specific settings

### Begin linear regression code

import PredictMDFull

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 = 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,
        )
    )

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)

linear_regression = PredictMD.single_labeldataframelinearregression(
    feature_names,
    single_label_name;
    package = :GLM,
    intercept = true,
    interactions = 1,
    name = "Linear regression",
    )

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

PredictMD.get_underlying(linear_regression)

linear_regression_plot_training =
    PredictMD.plotsinglelabelregressiontrueversuspredicted(
        linear_regression,
        training_features_df,
        training_labels_df,
        single_label_name,
        );



display(linear_regression_plot_training)
PredictMD.save_plot(
    joinpath(
        PROJECT_OUTPUT_DIRECTORY,
        "plots",
        "linear_regression_plot_training.pdf",
        ),
    linear_regression_plot_training,
    )

linear_regression_plot_testing =
    PredictMD.plotsinglelabelregressiontrueversuspredicted(
        linear_regression,
        testing_features_df,
        testing_labels_df,
        single_label_name
        );



display(linear_regression_plot_testing)
PredictMD.save_plot(
    joinpath(
        PROJECT_OUTPUT_DIRECTORY,
        "plots",
        "linear_regression_plot_testing.pdf",
        ),
    linear_regression_plot_testing,
    )

show(
    PredictMD.singlelabelregressionmetrics(
        linear_regression,
        training_features_df,
        training_labels_df,
        single_label_name,
        );
    allrows = true,
    allcols = true,
    splitcols = false,
    )

show(
    PredictMD.singlelabelregressionmetrics(
        linear_regression,
        testing_features_df,
        testing_labels_df,
        single_label_name,
        );
    allrows = true,
    allcols = true,
    splitcols = false,
    )

linear_regression_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "linear_regression.jld2",
    )

PredictMD.save_model(linear_regression_filename, linear_regression)

### End linear regression code



##### End of file

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