##### 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 model comparison code
import PredictMDFull
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
import Pkg
try Pkg.add("StatsBase") catch end
import StatsBase
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,
)
linear_regression_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"linear_regression.jld2",
)
random_forest_regression_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"random_forest_regression.jld2",
)
knet_mlp_regression_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"knet_mlp_regression.jld2",
)
linear_regression =
PredictMD.load_model(linear_regression_filename)
random_forest_regression =
PredictMD.load_model(random_forest_regression_filename)
knet_mlp_regression =
PredictMD.load_model(knet_mlp_regression_filename)
PredictMD.parse_functions!(knet_mlp_regression)
all_models = PredictMD.Fittable[
linear_regression,
random_forest_regression,
knet_mlp_regression,
]
single_label_name = :MedV
continuous_label_names = Symbol[single_label_name]
categorical_label_names = Symbol[]
label_names = vcat(categorical_label_names, continuous_label_names)
println("Single label regression metrics, training set: ")
show(
PredictMD.singlelabelregressionmetrics(
all_models,
training_features_df,
training_labels_df,
single_label_name,
),
allcols=true,
)
println("Single label regression metrics, testing set: ")
show(
PredictMD.singlelabelregressionmetrics(
all_models,
testing_features_df,
testing_labels_df,
single_label_name,
),
allcols=true,
)
### End model comparison code
##### End of file
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