##### Beginning of file
# This file was generated by PredictMD version 0.19.0
# For help, please visit https://www.predictmd.net
import PredictMD
### Begin project-specific settings
PredictMD.require_julia_version("v0.6")
PredictMD.require_predictmd_version("0.19.0")
# PredictMD.require_predictmd_version("0.19.0", "0.20.0-")
PROJECT_OUTPUT_DIRECTORY = PredictMD.project_directory(
homedir(),
"Desktop",
"boston_housing_example",
)
### End project-specific settings
### Begin model output code
import CSV
import Compat
import DataFrames
import FileIO
import JLD2
import Knet
srand(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)
PredictMD.predict(linear_regression,training_features_df,)
PredictMD.predict(random_forest_regression,training_features_df,)
PredictMD.predict(knet_mlp_regression,training_features_df,)
PredictMD.predict(linear_regression,testing_features_df,)
PredictMD.predict(random_forest_regression,testing_features_df,)
PredictMD.predict(knet_mlp_regression,testing_features_df,)
### End model output code
##### End of file
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