##### 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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