KnetMetrics.jl

A standalone machine learning metrics library developed for Knet
Author KnetML
Popularity
2 Stars
Updated Last
1 Year Ago
Started In
November 2020

KnetMetrics

A standalone machine learning metrics library implemented in pure Julia by Emirhan Kurtuluş. A vast collection of classification, regression and pairwise metrics and related visualizations are included. This package is created as a part of the Knet ecosystem; however, built-in Julia arrays and all other types that support the same set of operations are compatible.

Examples

julia> Pkg.add("KnetMetrics"); using KnetMetrics
# dummy data
julia> y_true = [2, 3, 2, 2, 2, 1, 3, 3, 2, 1, 2, 3, 2, 3, 2, 3, 1,1];
julia> y_pred = [1, 3, 1, 1, 3, 2, 2, 1, 3, 2, 3, 3, 2, 3, 2, 1, 1, 3];
# creating a confusion matrix
julia> c = confusion_matrix( y_true, y_pred, labels = [1,2,3]) #labels are truly optional

            Expected

      1      2      3
_____________________
      1      2      1   │1
      3      2      3   │2
      2      1      3   │3      Predicted

# testing some metrics
julia> f1_score(c) # f1_score(y_true, y_pred)
0.32307692307692304

julia> f1_score(c, average = "binary")
3-element Array{Float64,1}:
 0.2
 0.3076923076923077
 0.4615384615384615

julia> f1_score(c, average = "binary", normalize=true)
3-element Array{Float64,1}:
 0.33918173268560714
 0.5218180502855494
 0.7827270754283241

julia> f1_score(c, class_name = 3)
0.4615384615384615

julia> matthews_correlation_coeff(c, average = "micro")
0.20396752553080869

julia> matthews_correlation_coeff(c, average = "weighted")
0.07138375997792953

julia> matthews_correlation_coeff(c, average = "sample-weights", weights = [3,2,1])
0.03263110671272045

julia> minkowski_distance(y_true, y_pred)
21

julia> mean_absolute_error(y_true, y_pred)
0.8333333333333334

Currently Supported Metrics

Note: (*) symbol denotes that the function has a built-in visualization through visualize function.

Classification

  • Confusion Matrix *
  • Condition Positive and Negative *
  • Predicted Positive and Negative *
  • Correctly and Incorrectly Classified
  • True Positive Rate (Sensitivity Score, Recall Score) *
  • True Negative Rate (Specificity Score) *
  • Positive Predictive Value (Precision Score) *
  • Accuracy Score *
  • Balanced Accuracy Score *
  • Negative Predictive Value *
  • False Negative Rate *
  • False Positive Rate *
  • False Discovery Rate *
  • False Omission Rate *
  • Fbeta Score (F1 Score) *
  • Prevalence Threshold *
  • Threat Score *
  • Matthews Correlation Coefficient *
  • Fowlkes Mallows Index *
  • Informedness *
  • Markedness *
  • Cohen Kappa Score
  • Hamming Loss
  • Jaccard Score *

Regression

  • Maximum Residual Error
  • Mean Absolute Error
  • Mean Squared Error
  • Mean Squared Log Error
  • Median Absolute Error
  • Mean Absolute Percentage Error

Pairwise

  • Minkowski Distance
  • Euclidian Distance
  • Manhattan Distance
  • Chebyshev Distance
  • Braycurtis Distance
  • Canberra Distance
  • Cityblock Distance
  • Mahalanobis Distance
  • Correlation
  • Cosine Distance
  • Cosine Similarity

TO-DO

  1. A greater range of Roc Curve related functions
  2. A greater range of regression functions
  3. A greater range of pairwise functions and kernels
  4. Clustering metrics

Used By Packages

No packages found.