Revert "Please"

This reverts commit d17d8cd.

src/evaluation.jl

@@ -3,14 +3,8 @@ abstract type EvaluationMethod end
 abstract type Accuracy <: EvaluationMethod end
 abstract type CrossEntropy <: EvaluationMethod end
 abstract type MeanSquaredError <: EvaluationMethod end
-abstract type FalsePositive <: EvaluationMethod end
-abstract type TruePositive <: EvaluationMethod end
-abstract type FalseNegative <: EvaluationMethod end
-abstract type TrueNegative <: EvaluationMethod end
-abstract type Precision <: EvaluationMethod end
-abstract type Recall <: EvaluationMethod end
 
-export Accuracy, MeanSquaredError, CrossEntropy, FalsePositive, TruePositive, FalseNegative, TrueNegative, Precision, Recall
+export Accuracy, MeanSquaredError, CrossEntropy
 
 function _evaluate(::Type{Accuracy}, metrics_dict::Dict{String, Vector{Float64}}, epoch::Int, dataset_size::Int; kwargs...)
     sample = kwargs[:y_sample]
@@ -19,56 +13,6 @@ function _evaluate(::Type{Accuracy}, metrics_dict::Dict{String, Vector{Float64}}
     return metrics_dict["accuracy"][epoch] += tp / dataset_size
 end
 
-function _evaluate(::Type{FalsePositive}, metrics_dict::Dict{String, Vector{Float64}}, epoch::Int, dataset_size::Int; kwargs...)
-    sample = kwargs[:y_sample]
-    predicted = kwargs[:y_pred]
-    tp = 0
-    if sample[1] == 0 && predicted[1] == 1
-        tp = 1
-    end
-    return metrics_dict["false_positive"][epoch] += tp 
-end
-
-function _evaluate(::Type{TruePositive}, metrics_dict::Dict{String, Vector{Float64}}, epoch::Int, dataset_size::Int; kwargs...)
-    sample = kwargs[:y_sample]
-    predicted = kwargs[:y_pred]
-    tp = 0
-    if sample[1] == 1 && predicted[1] == 1
-        tp = 1
-    end
-    return metrics_dict["true_positive"][epoch] += tp
-end
-
-function _evaluate(::Type{FalseNegative}, metrics_dict::Dict{String, Vector{Float64}}, epoch::Int, dataset_size::Int; kwargs...)
-    sample = kwargs[:y_sample]
-    predicted = kwargs[:y_pred]
-    tp = 0
-    if sample[1] == 1 && predicted[1] == 0
-        tp = 1
-    end
-    return metrics_dict["false_negative"][epoch] += tp
-end
-
-function _evaluate(::Type{Precision}, metrics_dict::Dict{String, Vector{Float64}}, epoch::Int, dataset_size::Int; kwargs...)
-    return metrics_dict["precision"][epoch]  = metrics_dict["true_positive"][epoch]/(metrics_dict["true_positive"][epoch] + metrics_dict["false_positive"][epoch])
-end
-
-
-function _evaluate(::Type{Recall}, metrics_dict::Dict{String, Vector{Float64}}, epoch::Int, dataset_size::Int; kwargs...)
-    pre = 
-    return metrics_dict["recall"][epoch]  = metrics_dict["true_positive"][epoch]/(metrics_dict["true_positive"][epoch] + metrics_dict["false_negative"][epoch])
-end
-
-function _evaluate(::Type{TrueNegative}, metrics_dict::Dict{String, Vector{Float64}}, epoch::Int, dataset_size::Int; kwargs...)
-    sample = kwargs[:y_sample]
-    predicted = kwargs[:y_pred]
-    tp = 0
-    if sample[1] == 0 && predicted[1] == 0
-        tp = 1
-    end
-    return metrics_dict["true_negative"][epoch] += tp
-end
-
 function _evaluate(::Type{MeanSquaredError}, metrics_dict::Dict{String, Vector{Float64}}, epoch::Int, dataset_size::Int; kwargs...)
     sample = kwargs[:x_sample]
     predicted = kwargs[:x_pred]
@@ -130,18 +74,6 @@ function _initialize_metrics(metrics::Vector{<:DataType})
             metrics_dict["mse"] = Float64[]
         elseif metric == CrossEntropy
             metrics_dict["cross_entropy"] = Float64[]
-        elseif metric == FalseNegative
-            metrics_dict["false_negative"] = Float64[]
-        elseif metric == FalsePositive
-            metrics_dict["false_positive"] = Float64[]
-        elseif metric == TrueNegative
-            metrics_dict["true_negative"] = Float64[]
-        elseif metric == TruePositive
-            metrics_dict["true_positive"] = Float64[]
-        elseif metric == Precision
-            metrics_dict["precision"] = Float64[]
-        elseif metric == Recall
-            metrics_dict["recall"] = Float64[]
         end
     end
     return metrics_dict