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Dist: Drop NaN in training dataset

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This commit is contained in:
Alinson S. Xavier
2023-01-24 10:27:41 -06:00
parent d1f6796c96
commit 79748e3c13
1 changed files with 5 additions and 2 deletions
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7
src/graph/dist.jl
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@@ -40,7 +40,8 @@ function _calculate_distance(
end end
# Fit kNN model # Fit kNN model
df = DataFrame(CSV.File(csv_filename)) df = DataFrame(CSV.File(csv_filename, missingstring="NaN"))
dropmissing!(df)
coords = Matrix(df[!, [:source_lat, :source_lon, :dest_lat, :dest_lon]])' coords = Matrix(df[!, [:source_lat, :source_lon, :dest_lat, :dest_lon]])'
metric.ratios = Matrix(df[!, [:ratio]]) metric.ratios = Matrix(df[!, [:ratio]])
metric.tree = KDTree(coords) metric.tree = KDTree(coords)
@@ -53,5 +54,7 @@ function _calculate_distance(
# Predict ratio # Predict ratio
idxs, _ = knn(metric.tree, [source_lat, source_lon, dest_lat, dest_lon], 5) idxs, _ = knn(metric.tree, [source_lat, source_lon, dest_lat, dest_lon], 5)
ratio_pred = mean(metric.ratios[idxs]) ratio_pred = mean(metric.ratios[idxs])
return round(dist_euclidean * ratio_pred, digits = 3) dist_pred = round(dist_euclidean * ratio_pred, digits = 3)
isfinite(dist_pred) || error("non-finite distance detected: $dist_pred")
return dist_pred
end end