fix bug in predicting with KNN models for :brutetree algorithm and metric.

[OkonSamuel]

julia> using MLJ, NearestNeighborModels, DataFrames

julia> X_rand = rand(50,3);

julia> coef = [1; 0; 1];

julia> y_rand = X_rand*coef + rand(50,1);

julia> X_rand_df = DataFrames.DataFrame(X_rand, :auto) #convert to dataframe for MLJ inputs

julia> y_rand_df = DataFrames.DataFrame(y_rand, :auto);

julia> train_idx, test_idx = partition(eachindex(y_rand_df[:,1]), 0.7, shuffle=true);

julia> tree1 = KNNRegressor(algorithm=:brutetree, metric= Euclidean(), reorder = false, leafsize=0)
KNNRegressor(
    K = 5,
    algorithm = :brutetree,
    metric = Euclidean(0.0),
    leafsize = 0,
    reorder = false,
    weights = Uniform())

julia> tree2 = KNNRegressor(algorithm=:brutetree, metric= Cityblock(), reorder = false, leafsize=0)
KNNRegressor(
    K = 5,
    algorithm = :brutetree,
    metric = Cityblock(),
    leafsize = 0,
    reorder = false,
    weights = Uniform())

julia> tree3 = KNNRegressor(algorithm=:brutetree)
KNNRegressor(
    K = 5,
    algorithm = :brutetree,
    metric = Euclidean(0.0),
    leafsize = 0,
    reorder = false,
    weights = Uniform())

julia> mach1 = machine(tree1,  X_rand_df[train_idx, :], y_rand_df[train_idx, 1]);

julia> mach2 = machine(tree2,  X_rand_df[train_idx, :], y_rand_df[train_idx, 1]);

julia> mach3 = machine(tree3,  X_rand_df[train_idx, :], y_rand_df[train_idx, 1]);

julia> fit!(mach1)
[ Info: Training Machine{KNNRegressor,…}.
Machine{KNNRegressor,…} trained 1 time; caches data
  model: KNNRegressor
  args:
    1:  Source @344 ⏎ `Table{AbstractVector{Continuous}}`
    2:  Source @234 ⏎ `AbstractVector{Continuous}`


julia> fit!(mach2)
[ Info: Training Machine{KNNRegressor,…}.
Machine{KNNRegressor,…} trained 1 time; caches data
  model: KNNRegressor
  args:
    1:  Source @582 ⏎ `Table{AbstractVector{Continuous}}`
    2:  Source @640 ⏎ `AbstractVector{Continuous}`

julia> fit!(mach3)
[ Info: Training Machine{KNNRegressor,…}.
Machine{KNNRegressor,…} trained 1 time; caches data
  model: KNNRegressor
  args:
    1:  Source @492 ⏎ `Table{AbstractVector{Continuous}}`
    2:  Source @836 ⏎ `AbstractVector{Continuous}`

julia> preds1 = MLJ.predict(mach1, X_rand_df[test_idx,:]);

julia> preds2 = MLJ.predict(mach2, X_rand_df[test_idx,:]);

julia> preds1 == preds2
false

julia> preds3 = MLJ.predict(mach3, X_rand_df[test_idx,:])
15-element Vector{Float64}:
 1.7029412653743503
 1.4591779632945534
 1.582290743528469
 2.101230332732603
 2.2316740656510454
 1.4370807503964989
 1.70609816653188
 1.5000572634358513
 1.6903333540586924
 1.5000572634358516
 1.5193869250110221
 1.0055116437574894
 1.1873231287446022
 1.3289743625868122
 1.1843582933771475

closes #46, closes #45.

[codecov-commenter]

Codecov Report

Merging #47 (d79bc0a) into dev (faca85c) will increase coverage by 0.91%.
The diff coverage is 94.87%.

@@            Coverage Diff             @@
##              dev      #47      +/-   ##
==========================================
+ Coverage   92.39%   93.31%   +0.91%     
==========================================
  Files           3        4       +1     
  Lines         342      389      +47     
==========================================
+ Hits          316      363      +47     
  Misses         26       26              

Impacted Files	Coverage Δ
src/NearestNeighborModels.jl	100.00% <ø> (ø)
src/models.jl	92.42% <94.87%> (+2.03%)	⬆️
src/utils.jl	100.00% <0.00%> (ø)
src/kernels.jl	90.35% <0.00%> (+0.17%)	⬆️

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[ablaom]

Thanks for this!

I will try to review in the next 24 hours.

[ablaom]

Just the minor comments on the doc-strings.

Very thorough refactoring of the default constructor, thanks.

Please merge, make a release PR onto master, and tag a new release at your leisure.