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Monte Carlo Cross Validation

Evidentiary and interpretable prediction

Binary and Multi-classification algorithm for adverse outcome detection, survival classification, and endpoint prediction (see references for details)

Objectives of this project

  1. Build the mccv python package: easily implement and perform MCCV for learning and prediction tasks.
  2. Showcase accessibly to build, validate, and interpret MCCV classifiers.
  3. Demonstrate use in both python and R for diverse community implementations.

Installation

mkdir ~/my_directory #choose where to clone the mccv repository
cd ~/my_directory
git clone https://github.com/ngiangre/mccv.git
cd mccv/
python3 -m pip install .

Usage

Python

import pandas as pd
data = pd.read_csv('data/data.csv',index_col=0) # Feature column name is 'biomarker' and response column  name is 'status'
data.head()
     status  biomarker
obs                   
1         0   1.665731
2         0  -0.875837
3         0  -1.391374
4         0  -0.297352
5         1   0.189857

import mccv
mccv_obj = mccv.mccv(num_bootstraps=200)
mccv_obj.set_X( data.loc[:,['biomarker']] )
mccv_obj.set_Y( data.loc[:,['status']] )
mccv_obj.run_mccv()
mccv_obj.run_permuted_mccv()

#Output
for n in mccv_obj.mccv_data:
    print(n)
    mccv_obj.mccv_data[n].head()
Model Learning
   bootstrap                model  ...  train_roc_auc  validation_roc_auc
0          0  Logistic Regression  ...       0.529453            0.611111
1          1  Logistic Regression  ...       0.515235            0.732143
2          2  Logistic Regression  ...       0.543056            0.400000
3          3  Logistic Regression  ...       0.519728            0.727273
4          4  Logistic Regression  ...       0.554054            0.574074

[5 rows x 5 columns]
Feature Importance
   bootstrap    feature  importance                model
0          0  biomarker    1.009705  Logistic Regression
1          0  Intercept   -0.598575  Logistic Regression
0          1  biomarker    0.509433  Logistic Regression
1          1  Intercept   -0.226550  Logistic Regression
0          2  biomarker    1.598627  Logistic Regression
Patient Predictions
     bootstrap                model  y_pred   y_proba  y_true
obs                                                          
27           0  Logistic Regression       0  0.384723       1
87           0  Logistic Regression       1  0.601359       0
3            0  Logistic Regression       0  0.401320       0
56           0  Logistic Regression       1  0.512481       1
76           0  Logistic Regression       0  0.393009       0
Performance
                 model   metric  performance_bootstrap     value
0  Logistic Regression  roc_auc                      0  0.467487
1  Logistic Regression  roc_auc                      1  0.467776
2  Logistic Regression  roc_auc                      2  0.480176
3  Logistic Regression  roc_auc                      3  0.480679
4  Logistic Regression  roc_auc                      4  0.475859

for n in mccv_obj.mccv_permuted_data:
    print(n)
    mccv_obj.mccv_permuted_data[n].head()
Model Learning
   bootstrap                model  ...  train_roc_auc  validation_roc_auc
0          0  Logistic Regression  ...       0.506233            0.642857
1          1  Logistic Regression  ...       0.492030            0.703704
2          2  Logistic Regression  ...       0.510135            0.537037
3          3  Logistic Regression  ...       0.506944            0.703704
4          4  Logistic Regression  ...       0.589547            0.340909

[5 rows x 5 columns]
Feature Importance
   bootstrap    feature  importance                model
0          0  biomarker   -0.196116  Logistic Regression
1          0  Intercept    0.079220  Logistic Regression
0          1  biomarker   -0.628093  Logistic Regression
1          1  Intercept    0.236617  Logistic Regression
0          2  biomarker    0.166196  Logistic Regression
Patient Predictions
     bootstrap                model  y_pred   y_proba  y_true
obs                                                          
27           0  Logistic Regression       1  0.513536       1
87           0  Logistic Regression       0  0.470809       0
3            0  Logistic Regression       1  0.510160       0
56           0  Logistic Regression       0  0.488317       1
76           0  Logistic Regression       1  0.511844       1
Performance
                 model   metric  performance_bootstrap     value
0  Logistic Regression  roc_auc                      0  0.440616
1  Logistic Regression  roc_auc                      1  0.442506
2  Logistic Regression  roc_auc                      2  0.449941
3  Logistic Regression  roc_auc                      3  0.440162
4  Logistic Regression  roc_auc                      4  0.449896

R

if(!requireNamespace("readr")){install.packages("readr")}
Loading required namespace: readr

library(readr)
data <- read_csv("data/data.csv",col_types = c("iid")) #set obs as integer, status as integer, and biomarker as double
head(data)
# A tibble: 6 × 3
    obs status biomarker
  <int>  <int>     <dbl>
1     1      0     1.67 
2     2      0    -0.876
3     3      0    -1.39 
4     4      0    -0.297
5     5      1     0.190
6     6      0     2.20 

if(!requireNamespace("reticulate")){install.packages("reticulate")}
mccv = reticulate::import('mccv')
mccv_obj = mccv$mccv(num_bootstraps = as.integer(200))

X = reticulate::r_to_py(data[,c('obs','biomarker')])
X = X$set_index(reticulate::r_to_py('obs'))

y = reticulate::r_to_py(data[,c('obs','status')])
y = y$set_index(reticulate::r_to_py('obs'))

mccv_obj$set_X(X)
mccv_obj$set_Y(y)
mccv_obj$run_mccv()
mccv_obj$run_permuted_mccv()

#Output
lapply(mccv_obj$mccv_data,head)
Warning in py_to_r.pandas.core.frame.DataFrame(object): index contains
duplicated values: row names not set

Warning in py_to_r.pandas.core.frame.DataFrame(object): index contains
duplicated values: row names not set

$`Model Learning`
  bootstrap               model test_roc_auc train_roc_auc validation_roc_auc
1         0 Logistic Regression       1.0000     0.5294525          0.6111111
2         1 Logistic Regression       0.8000     0.5152355          0.7321429
3         2 Logistic Regression       1.0000     0.5430556          0.4000000
4         3 Logistic Regression       0.8750     0.5197279          0.7272727
5         4 Logistic Regression       0.8125     0.5540541          0.5740741
6         5 Logistic Regression       1.0000     0.5499325          0.5357143

$`Feature Importance`
  bootstrap   feature importance               model
1         0 biomarker  1.0097049 Logistic Regression
2         0 Intercept -0.5985751 Logistic Regression
3         1 biomarker  0.5094328 Logistic Regression
4         1 Intercept -0.2265503 Logistic Regression
5         2 biomarker  1.5986271 Logistic Regression
6         2 Intercept -0.9420031 Logistic Regression

$`Patient Predictions`
  bootstrap               model y_pred   y_proba y_true
1         0 Logistic Regression      0 0.3847230      1
2         0 Logistic Regression      1 0.6013587      0
3         0 Logistic Regression      0 0.4013202      0
4         0 Logistic Regression      1 0.5124811      1
5         0 Logistic Regression      0 0.3930090      0
6         0 Logistic Regression      0 0.4660667      1

$Performance
                model  metric performance_bootstrap     value
1 Logistic Regression roc_auc                     0 0.4674874
2 Logistic Regression roc_auc                     1 0.4677764
3 Logistic Regression roc_auc                     2 0.4801763
4 Logistic Regression roc_auc                     3 0.4806793
5 Logistic Regression roc_auc                     4 0.4758592
6 Logistic Regression roc_auc                     5 0.4687351

lapply(mccv_obj$mccv_permuted_data,head)
Warning in py_to_r.pandas.core.frame.DataFrame(object): index contains
duplicated values: row names not set

Warning in py_to_r.pandas.core.frame.DataFrame(object): index contains
duplicated values: row names not set

$`Model Learning`
  bootstrap               model test_roc_auc train_roc_auc validation_roc_auc
1         0 Logistic Regression       0.5500     0.5062327          0.6428571
2         1 Logistic Regression       0.8000     0.4920305          0.7037037
3         2 Logistic Regression       0.5625     0.5101351          0.5370370
4         3 Logistic Regression       0.8000     0.5069444          0.7037037
5         4 Logistic Regression       0.9000     0.5895470          0.3409091
6         5 Logistic Regression       0.7000     0.5360111          0.5178571

$`Feature Importance`
  bootstrap   feature  importance               model
1         0 biomarker -0.19611610 Logistic Regression
2         0 Intercept  0.07921951 Logistic Regression
3         1 biomarker -0.62809256 Logistic Regression
4         1 Intercept  0.23661698 Logistic Regression
5         2 biomarker  0.16619555 Logistic Regression
6         2 Intercept -0.01455491 Logistic Regression

$`Patient Predictions`
  bootstrap               model y_pred   y_proba y_true
1         0 Logistic Regression      1 0.5135363      1
2         0 Logistic Regression      0 0.4708091      0
3         0 Logistic Regression      1 0.5101595      0
4         0 Logistic Regression      0 0.4883168      1
5         0 Logistic Regression      1 0.5118443      1
6         0 Logistic Regression      0 0.4973405      1

$Performance
                model  metric performance_bootstrap     value
1 Logistic Regression roc_auc                     0 0.4406164
2 Logistic Regression roc_auc                     1 0.4425061
3 Logistic Regression roc_auc                     2 0.4499406
4 Logistic Regression roc_auc                     3 0.4401616
5 Logistic Regression roc_auc                     4 0.4498963
6 Logistic Regression roc_auc                     5 0.4436607

Contribute

Please do! Reach out to Nick directly ([email protected]), make an issue, or make a pull request.

License

This software is released under the MIT license, which can be found in LICENSE in the root directory of this repository.

Citation

Giangreco, N.P., Lebreton, G., Restaino, S. et al. Alterations in the kallikrein-kinin system predict death after heart transplant. Sci Rep 12, 14167 (2022). https://doi.org/10.1038/s41598-022-18573-2

Giangreco et al. 2021. Plasma kallikrein predicts primary graft dysfunction after heart transplant. Journal of Heart and Lung Transplantation, 40(10), 1199-1211. https://doi.org/10.1016/j.healun.2021.07.001.