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108 lines
3.0 KiB
108 lines
3.0 KiB
# RELOG: Reverse Logistics Optimization
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# Copyright (C) 2020-2024, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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using Geodesy
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using NearestNeighbors
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using DataFrames
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using CRC
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using ZipFile
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using Statistics
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crc32 = crc(CRC_32)
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abstract type DistanceMetric end
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Base.@kwdef mutable struct KnnDrivingDistance <: DistanceMetric
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tree = nothing
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ratios = nothing
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end
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mutable struct EuclideanDistance <: DistanceMetric end
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function _calculate_distance(
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source_lat,
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source_lon,
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dest_lat,
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dest_lon,
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::EuclideanDistance,
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)::Float64
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x = LLA(source_lat, source_lon, 0.0)
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y = LLA(dest_lat, dest_lon, 0.0)
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return round(euclidean_distance(x, y) / 1000.0, digits=3)
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end
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function _download_file(url, output, expected_crc32)::Nothing
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if isfile(output)
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return
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end
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mkpath(dirname(output))
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@info "Downloading: $url"
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fname = download(url)
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actual_crc32 = open(crc32, fname)
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expected_crc32 == actual_crc32 || error("CRC32 mismatch")
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cp(fname, output)
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return
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end
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function _download_zip(url, outputdir, expected_output_file, expected_crc32)::Nothing
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if isfile(expected_output_file)
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return
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end
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mkpath(outputdir)
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@info "Downloading: $url"
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zip_filename = download(url)
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actual_crc32 = open(crc32, zip_filename)
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expected_crc32 == actual_crc32 || error("CRC32 mismatch")
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open(zip_filename) do zip_file
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zr = ZipFile.Reader(zip_file)
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for file in zr.files
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open(joinpath(outputdir, file.name), "w") do output_file
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write(output_file, read(file))
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end
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end
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end
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return
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end
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function _calculate_distance(
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source_lat,
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source_lon,
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dest_lat,
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dest_lon,
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metric::KnnDrivingDistance,
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)::Float64
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if metric.tree === nothing
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basedir = joinpath(dirname(@__FILE__), "data")
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csv_filename = joinpath(basedir, "dist_driving.csv")
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# Download pre-computed driving data
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if !isfile(csv_filename)
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_download_zip(
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"https://axavier.org/RELOG/0.6/data/dist_driving_0b9a6ad6.zip",
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basedir,
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csv_filename,
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0x0b9a6ad6,
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)
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end
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# Fit kNN model
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df = DataFrame(CSV.File(csv_filename, missingstring="NaN"))
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dropmissing!(df)
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coords = Matrix(df[!, [:source_lat, :source_lon, :dest_lat, :dest_lon]])'
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metric.ratios = Matrix(df[!, [:ratio]])
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metric.tree = KDTree(coords)
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end
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# Compute Euclidean distance
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dist_euclidean =
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_calculate_distance(source_lat, source_lon, dest_lat, dest_lon, EuclideanDistance())
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# Predict ratio
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idxs, _ = knn(metric.tree, [source_lat, source_lon, dest_lat, dest_lon], 5)
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ratio_pred = mean(metric.ratios[idxs])
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dist_pred = round(dist_euclidean * ratio_pred, digits=3)
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isfinite(dist_pred) || error("non-finite distance detected: $dist_pred")
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return dist_pred
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end
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