##### Beginning of file
# This file was generated by PredictMD version 0.20.0
# For help, please visit https://www.predictmd.net
import PredictMD
### Begin project-specific settings
PredictMD.require_julia_version("v0.7.0")
PredictMD.require_predictmd_version("0.20.0")
# PredictMD.require_predictmd_version("0.20.0", "0.21.0-")
PROJECT_OUTPUT_DIRECTORY = PredictMD.project_directory(
homedir(),
"Desktop",
"boston_housing_example",
)
### End project-specific settings
### Begin model comparison code
import Pkg
try Pkg.add("CSV") catch end
try Pkg.add("DataFrames") catch end
try Pkg.add("DecisionTree") catch end
try Pkg.add("Distributions") catch end
try Pkg.add("FileIO") catch end
try Pkg.add("GLM") catch end
try Pkg.add("JLD2") catch end
try Pkg.add("Knet") catch end
try Pkg.add("StatsModels") catch end
try Pkg.add("ValueHistories") catch end
import CSV
import DataFrames
import DecisionTree
import Distributions
import FileIO
import GLM
import JLD2
import Knet
import LinearAlgebra
import Random
import StatsModels
import ValueHistories
Random.seed!(999)
trainingandvalidation_features_df_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"trainingandvalidation_features_df.csv",
)
trainingandvalidation_labels_df_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"trainingandvalidation_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",
)
validation_features_df_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"validation_features_df.csv",
)
validation_labels_df_filename = joinpath(
PROJECT_OUTPUT_DIRECTORY,
"validation_labels_df.csv",
)
trainingandvalidation_features_df = CSV.read(
trainingandvalidation_features_df_filename,
DataFrames.DataFrame;
rows_for_type_detect = 100,
)
trainingandvalidation_labels_df = CSV.read(
trainingandvalidation_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,
)
validation_features_df = CSV.read(
validation_features_df_filename,
DataFrames.DataFrame;
rows_for_type_detect = 100,
)
validation_labels_df = CSV.read(
validation_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,
),
true,
)
println("Single label regression metrics, testing set: ")
show(
PredictMD.singlelabelregressionmetrics(
all_models,
testing_features_df,
testing_labels_df,
single_label_name,
),
true,
)
### End model comparison code
##### End of file
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