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Merge pull request #129 from IBM/refactor_format
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refactor and reformat by lsp
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@ppalmes
ppalmes authored Oct 18, 2024
2 parents dca2f23 + 77c12f1 commit a12fd62
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Showing 3 changed files with 350 additions and 355 deletions.
112 changes: 56 additions & 56 deletions src/skcrossvalidator.jl
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
module SKCrossValidators

import PythonCall
const PYC=PythonCall
const PYC = PythonCall

# standard included modules
using DataFrames
Expand All @@ -16,49 +16,49 @@ const metric_dict = Dict{String,PYC.Py}()
const SKM = PYC.pynew()

function __init__()
PYC.pycopy!(SKM, PYC.pyimport("sklearn.metrics"))

metric_dict["roc_auc_score"] = SKM.roc_auc_score
metric_dict["accuracy_score"] = SKM.accuracy_score
metric_dict["auc"] = SKM.auc
metric_dict["average_precision_score"] = SKM.average_precision_score
metric_dict["balanced_accuracy_score"] = SKM.balanced_accuracy_score
metric_dict["brier_score_loss"] = SKM.brier_score_loss
metric_dict["classification_report"] = SKM.classification_report
metric_dict["cohen_kappa_score"] = SKM.cohen_kappa_score
metric_dict["confusion_matrix"] = SKM.confusion_matrix
metric_dict["f1_score"] = SKM.f1_score
metric_dict["fbeta_score"] = SKM.fbeta_score
metric_dict["hamming_loss"] = SKM.hamming_loss
metric_dict["hinge_loss"] = SKM.hinge_loss
metric_dict["log_loss"] = SKM.log_loss
metric_dict["matthews_corrcoef"] = SKM.matthews_corrcoef
metric_dict["multilabel_confusion_matrix"] = SKM.multilabel_confusion_matrix
metric_dict["precision_recall_curve"] = SKM.precision_recall_curve
metric_dict["precision_recall_fscore_support"] = SKM.precision_recall_fscore_support
metric_dict["precision_score"] = SKM.precision_score
metric_dict["recall_score"] = SKM.recall_score
metric_dict["roc_auc_score"] = SKM.roc_auc_score
metric_dict["roc_curve"] = SKM.roc_curve
metric_dict["jaccard_score"] = SKM.jaccard_score
metric_dict["zero_one_loss"] = SKM.zero_one_loss
# regression
metric_dict["mean_squared_error"] = SKM.mean_squared_error
metric_dict["mean_squared_log_error"] = SKM.mean_squared_log_error
metric_dict["mean_absolute_error"] = SKM.mean_absolute_error
metric_dict["median_absolute_error"] = SKM.median_absolute_error
metric_dict["r2_score"] = SKM.r2_score
metric_dict["max_error"] = SKM.max_error
metric_dict["mean_poisson_deviance"] = SKM.mean_poisson_deviance
metric_dict["mean_gamma_deviance"] = SKM.mean_gamma_deviance
metric_dict["mean_tweedie_deviance"] = SKM.mean_tweedie_deviance
metric_dict["explained_variance_score"] = SKM.explained_variance_score
PYC.pycopy!(SKM, PYC.pyimport("sklearn.metrics"))

metric_dict["roc_auc_score"] = SKM.roc_auc_score
metric_dict["accuracy_score"] = SKM.accuracy_score
metric_dict["auc"] = SKM.auc
metric_dict["average_precision_score"] = SKM.average_precision_score
metric_dict["balanced_accuracy_score"] = SKM.balanced_accuracy_score
metric_dict["brier_score_loss"] = SKM.brier_score_loss
metric_dict["classification_report"] = SKM.classification_report
metric_dict["cohen_kappa_score"] = SKM.cohen_kappa_score
metric_dict["confusion_matrix"] = SKM.confusion_matrix
metric_dict["f1_score"] = SKM.f1_score
metric_dict["fbeta_score"] = SKM.fbeta_score
metric_dict["hamming_loss"] = SKM.hamming_loss
metric_dict["hinge_loss"] = SKM.hinge_loss
metric_dict["log_loss"] = SKM.log_loss
metric_dict["matthews_corrcoef"] = SKM.matthews_corrcoef
metric_dict["multilabel_confusion_matrix"] = SKM.multilabel_confusion_matrix
metric_dict["precision_recall_curve"] = SKM.precision_recall_curve
metric_dict["precision_recall_fscore_support"] = SKM.precision_recall_fscore_support
metric_dict["precision_score"] = SKM.precision_score
metric_dict["recall_score"] = SKM.recall_score
metric_dict["roc_auc_score"] = SKM.roc_auc_score
metric_dict["roc_curve"] = SKM.roc_curve
metric_dict["jaccard_score"] = SKM.jaccard_score
metric_dict["zero_one_loss"] = SKM.zero_one_loss
# regression
metric_dict["mean_squared_error"] = SKM.mean_squared_error
metric_dict["mean_squared_log_error"] = SKM.mean_squared_log_error
metric_dict["mean_absolute_error"] = SKM.mean_absolute_error
metric_dict["median_absolute_error"] = SKM.median_absolute_error
metric_dict["r2_score"] = SKM.r2_score
metric_dict["max_error"] = SKM.max_error
metric_dict["mean_poisson_deviance"] = SKM.mean_poisson_deviance
metric_dict["mean_gamma_deviance"] = SKM.mean_gamma_deviance
metric_dict["mean_tweedie_deviance"] = SKM.mean_tweedie_deviance
metric_dict["explained_variance_score"] = SKM.explained_variance_score
end

function checkfun(sfunc::String)
if !(sfunc in keys(metric_dict))
println("$sfunc metric is not supported")
println("metric: ",keys(metric_dict))
println("metric: ", keys(metric_dict))
error("Metric keyword error")
end
end
Expand Down Expand Up @@ -87,39 +87,39 @@ and the following metrics for regression:
- "max_error"
- "explained_variance_score"
"""
function crossvalidate(pl::Machine,X::DataFrame,Y::Vector,
sfunc::String; nfolds=10,verbose::Bool=true)
function crossvalidate(pl::Machine, X::DataFrame, Y::Vector,
sfunc::String; nfolds=10, verbose::Bool=true)

YC=Y
YC = Y
if !(eltype(YC) <: Real)
YC = Y |> Vector{String}
YC = Y |> Vector{String}
end

checkfun(sfunc)
pfunc = metric_dict[sfunc]
metric(a,b) = pfunc(a,b) |> (x -> PYC.pyconvert(Float64,x))
crossvalidate(pl,X,YC,metric,nfolds,verbose)
metric(a, b) = pfunc(a, b) |> (x -> PYC.pyconvert(Float64, x))
crossvalidate(pl, X, YC, metric, nfolds, verbose)
end

function crossvalidate(pl::Machine,X::DataFrame,Y::Vector,sfunc::String,nfolds::Int)
crossvalidate(pl,X,Y,sfunc; nfolds)
function crossvalidate(pl::Machine, X::DataFrame, Y::Vector, sfunc::String, nfolds::Int)
crossvalidate(pl, X, Y, sfunc; nfolds)
end

function crossvalidate(pl::Machine,X::DataFrame,Y::Vector,sfunc::String,verbose::Bool)
crossvalidate(pl,X,Y,sfunc; verbose)
function crossvalidate(pl::Machine, X::DataFrame, Y::Vector, sfunc::String, verbose::Bool)
crossvalidate(pl, X, Y, sfunc; verbose)
end

function crossvalidate(pl::Machine,X::DataFrame,Y::Vector,
sfunc::String, nfolds::Int,verbose::Bool)
crossvalidate(pl,X,Y,sfunc; nfolds,verbose)
function crossvalidate(pl::Machine, X::DataFrame, Y::Vector,
sfunc::String, nfolds::Int, verbose::Bool)
crossvalidate(pl, X, Y, sfunc; nfolds, verbose)
end

function crossvalidate(pl::Machine,X::DataFrame,Y::Vector,
sfunc::String,averagetype::String;nfolds=10,verbose::Bool=true)
function crossvalidate(pl::Machine, X::DataFrame, Y::Vector,
sfunc::String, averagetype::String; nfolds=10, verbose::Bool=true)
checkfun(sfunc)
pfunc = metric_dict[sfunc]
metric(a,b) = pfunc(a,b,average=averagetype) |> (x -> PYC.pyconvert(Float64,x))
crossvalidate(pl,X,Y,metric,nfolds,verbose)
metric(a, b) = pfunc(a, b, average=averagetype) |> (x -> PYC.pyconvert(Float64, x))
crossvalidate(pl, X, Y, metric, nfolds, verbose)
end


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