# Cross validation procedures

.ml.gs   Grid-search functions
kfshuff      K-Fold cross validation with randomized indices
kfsplit      K-Fold cross validation with sequential indices
kfstrat      K-Fold cross validation with stratified indices
mcsplit      Monte-Carlo cross validation with random split indices
pcsplit      Percentage-split cross validation
tschain      Chain-forward cross validation
tsrolls      Roll-forward cross validation.ml.xv   Cross-validation functions
kfshuff      K-Fold cross validation with randomized indices
kfsplit      K-Fold cross validation with sequential indices
kfstrat      K-Fold cross validation with stratified indices
mcsplit      Monte-Carlo cross validation with random split indices
pcsplit      Percentage-split cross validation
tschain      Chain-forward cross validation
tsrolls      Roll-forward cross validation

The .ml.xv and .ml.gs namespaces contain functions related to cross-validation and grid-search algorithms. These algorithms test how robust or stable a model is to changes in the volume of data or the specific subsets of data used for validation.

Within the following examples, .ml.xv.fitscore is used extensively to fit models and return the score achieved on validation/test data. This function can be replaced by a user-defined alternative for tailored applications, e.g. a function to fit on training data and predict outputs for new data.

As of toolkit version 0.1.3, the distribution of cross-validation functions is invoked at console initialization. If a process is started with \$q -s -4 -p 4321 and xval.q is loaded into the process, then the cross-validation library will automatically make 4 worker processes available to execute jobs.

Interactive notebook implementations

Interactive notebook implementations of a large number of the functions outlined here are available within KxSystems/mlnotebooks

## .ml.gs.kfshuff

Cross-validated parameter grid search applied to data with shuffled split indices

Syntax: .ml.gs.kfshuff[k;n;x;y;f;p;h]

Where

• k is the integer number of folds
• n is the integer number of repetitions of the procedure
• x is a matrix of features
• y is a vector of targets
• f is a function that takes parameters and data as input and returns a score
• p is a dictionary of hyperparameters to be searched
• h is a float value denoting the size of the holdout set used in a fitted gridsearch, where the best model is fit to the holdout set. If 0 the function will return scores for each fold for the given hyperparameters. If negative the data will be shuffled prior to designation of the holdout set.

returns the scores for hyperparameter sets on each of the k folds for all values of h and additionally returns the best hyperparameters and score on the holdout set for 0 < h <=1.

q)m:10000
q)x:(m;10)#(m*10)?1f
q)yr:x[;0]+m?.05
// algo
q)rf:{.p.import[sklearn.linear_model]:LinearRegression}
// params
q)pr:fit_interceptnormalize!(01b;01b)
// 4 fold cross-validation no holdout
q).ml.gs.kfshuff[4;1;x;yr;.ml.xv.fitscore rf;pr;0]
fit_intercept normalize|
-----------------------| ---------------------------------------
0             0        | 0.9971193 0.9973079 0.997283  0.9972022
0             1        | 0.9973031 0.9971586 0.9972672 0.9971888
1             0        | 0.997407  0.9974875 0.9974944 0.9975418
1             1        | 0.997542  0.997472  0.9974438 0.9974849
// 5 fold cross-validated grid search fitted on 20% holdout set
q).ml.gs.kfshuff[5;1;x;yr;.ml.xv.fitscore rf;pr;.2]
(+fit_interceptnormalize!(0011b;0101b))!(0.9971515 0.9971983 0.9973203 ..
fit_interceptnormalize!11b
0.9975178


## .ml.gs.kfsplit

Cross-validated parameter grid search applied to data with ascending split indices

Syntax: .ml.gs.kfsplit[k;n;x;y;f;p;h]

Where

• k is the integer number of folds
• n is the integer number of repetitions of the procedure
• x is a matrix of features
• y is a vector of targets
• f is a function that takes parameters and data as input and returns a score
• p is a dictionary of hyperparameters to be searched
• h is a float value denoting the size of the holdout set used in a fitted gridsearch, where the best model is fit to the holdout set. If 0 the function will return scores for each fold for the given hyperparameters. If negative the data will be shuffled prior to designation of the holdout set.

returns the scores for hyperparameter sets on each of the k folds for all values of h and additionally returns the best hyperparameters and score on the holdout set for 0 < h <=1.

q)m:10000
q)x:(m;10)#(m*10)?1f
q)yc:(raze flip(0N;4)#m#abcd)rank x[;0]
// algos
q)cf:{.p.import[sklearn.tree]:DecisionTreeClassifier}
// params
q)pc:enlist[max_depth]!enlist(::;1;2;3;4;5)
// 5 fold cross-validation no holdout
q).ml.gs.kfsplit[5;1;x;yc;.ml.xv.fitscore cf;pc;0]
max_depth|
---------| ----------------------------------
::       | 1      0.9995 0.9995 0.9985 0.999
1        | 0.4965 0.4715 0.486  0.4775 0.4815
2        | 0.7435 0.734  0.741  0.9985 0.735
3        | 1      0.9995 0.9995 0.9985 0.999
4        | 1      0.9995 0.9995 0.9985 0.999
5        | 1      0.9995 0.9995 0.9985 0.999
// 5 fold cross-validated grid search fitted on 20% holdout set
q).ml.gs.kfsplit[5;1;x;yc;.ml.xv.fitscore cf;pc;.2]
(+(,max_depth)!,(::;1;2;3;4;5))!(1 0.999375 0.998125 1 0.99875;0.475 0.475..
(,max_depth)!,::
1f
// 10 fold cross-validated grid search fitted on 10% holdout
// with initial data shuffle
q).ml.gs.kfsplit[10;1;x;yc;.ml.xv.fitscore cf;pc;-.1]
(+(,max_depth)!,(::;1;2;3;4;5))!(1 1 1 0.9988889 0.9977778 1 1 1 1 0.99888..
(,max_depth)!,::
1f


## .ml.gs.kfstrat

Cross-validated parameter grid search applied to data with an equi-distributions of targets per fold

Syntax: .ml.gs.kfstrat[k;n;x;y;f;p;h]

Where

• k is the integer number of folds
• n is the integer number of repetitions of the procedure
• x is a matrix of features
• y is a vector of targets
• f is a function that takes parameters and data as input and returns a score
• p is a dictionary of hyperparameters to be searched
• h is a float value denoting the size of the holdout set used in a fitted gridsearch, where the best model is fit to the holdout set. If 0 the function will return scores for each fold for the given hyperparameters. If negative the data will be shuffled prior to designation of the holdout set.

returns the scores for hyperparameter sets on each of the k folds for all values of h and additionally returns the best hyperparameters and score on the holdout set for 0 < h <=1.

q)m:10000
q)x:(m;10)#(m*10)?1f
q)yc:(raze flip(0N;4)#m#abcd)rank x[;0]
// algos
q)cf:{.p.import[sklearn.tree]:DecisionTreeClassifier}
// params
q)pc:enlist[max_depth]!enlist(::;1;2;3;4;5)
// 5 fold cross-validation no holdout
q).ml.gs.kfstrat[5;1;x;yc;.ml.xv.fitscore cf;pc;0]
max_depth|
---------| -------------------------------
::       | 0.999  1      1      0.9995 1
1        | 0.5    0.5    0.5    0.5    0.5
2        | 0.9995 0.999  0.9995 1      1
3        | 0.9995 0.9985 1      0.9995 1
4        | 1      0.999  0.9995 1      1
5        | 0.9995 1      1      0.999  1
// 4 fold cross-validated grid search fitted on 20% holdout set
q).ml.gs.kfstrat[4;1;x;yc;.ml.xv.fitscore cf;pc;.2]
(+(,max_depth)!,(::;1;2;3;4;5))!(0.9995005 1 1 0.998999;0.501998 0.5022511..
(,max_depth)!,5
0.9995


## .ml.gs.mcsplit

Cross-validated parameter grid search applied to randomly shuffled data and validated on a percentage holdout set

Syntax: .ml.gs.mcsplit[pc;n;x;y;f;p;h]

Where

• pc is a float between 0 and 1 denoting the percentage of data in the holdout validation set
• n is the integer number of repetitions of the procedure
• x is a matrix of features
• y is a vector of targets
• f is a function that takes parameters and data as input and returns a score
• p is a dictionary of hyperparameters to be searched
• h is a float value denoting the size of the holdout set used in a fitted gridsearch, where the best model is fit to the holdout set. If 0 the function will return scores for each fold for the given hyperparameters. If negative the data will be shuffled prior to designation of the holdout set.

returns the scores for hyperparameter sets on each of the k folds for all values of h and additionally returns the best hyperparameters and score on the holdout set for 0 < h <=1.

q)m:10000
q)x:(m;10)#(m*10)?1f
q)yr:x[;0]+m?.05
// algo
q)rf:{.p.import[sklearn.linear_model]:LinearRegression}
// params
q)pr:fit_interceptnormalize!(01b;01b)
// 20% validation set with 5 repetitions, no fit on holdout
q).ml.gs.mcsplit[0.2;5;x;yr;.ml.xv.fitscore rf;pr;0]
fit_intercept normalize|
-----------------------| -------------------------------------------------
0             0        | 0.9971461 0.9973725 0.9972689 0.9973013 0.9972352
0             1        | 0.997338  0.9973676 0.9972201 0.9973773 0.9972741
1             0        | 0.9975606 0.9975717 0.9974275 0.9974351 0.9973509
1             1        | 0.9973651 0.9974569 0.9974633 0.9974753 0.9975381
// 10% validation set with 3 repetitions, fit on 20% holdout set
q).ml.gs.mcsplit[0.1;3;x;yr;.ml.xv.fitscore rf;pr;.2]
(+fit_interceptnormalize!(0011b;0101b))!(0.9971063 0.9971009 0.997168;0.9..
fit_interceptnormalize!11b
0.9975662


## .ml.gs.pcsplit

Cross-validated parameter grid search applied to percentage split dataset

Syntax: .ml.gs.pcsplit[pc;n;x;y;f;p;h]

Where

• pc is a float between 0 and 1 denoting the percentage of data in the holdout validation set
• n is the integer number of repetitions of the procedure
• x is a matrix of features
• y is a vector of targets
• f is a function that takes parameters and data as input and returns a score
• p is a dictionary of hyperparameters to be searched
• h is a float value denoting the size of the holdout set used in a fitted gridsearch, where the best model is fit to the holdout set. If 0 the function will return scores for each fold for the given hyperparameters. If negative the data will be shuffled prior to designation of the holdout set.

returns the scores for hyperparameter sets on each of the k folds for all values of h and additionally returns the best hyperparameters and score on the holdout set for 0 < h <=1.

q)m:10000
q)x:(m;10)#(m*10)?1f
q)yr:x[;0]+m?.05
// algo
q)rf:{.p.import[sklearn.linear_model]:LinearRegression}
// params
q)pr:fit_interceptnormalize!(01b;01b)
// 20% validation set with 1 repetition, no fit on holdout
q).ml.gs.pcsplit[0.2;1;x;yr;.ml.xv.fitscore rf;pr;0]
fit_intercept normalize|
-----------------------| ---------
0             0        | 0.9972247
0             1        | 0.9972247
1             0        | 0.9974099
1             1        | 0.9974099
// 10% validation set with 3 repetitions, fit on 20% holdout set
q).ml.gs.pcsplit[0.1;3;x;yr;.ml.xv.fitscore rf;pr;.2]
(+fit_interceptnormalize!(0011b;0101b))!(0.9972871 0.9972871 0.9972871;0...
fit_interceptnormalize!10b
0.9974099


This form of cross validation is also known as repeated random sub-sampling validation. This has advantages over K-fold when observations are not wanted in equi-sized bins or where outliers could heavily bias a classifier.

## .ml.gs.tschain

Cross-validated parameter grid search applied to chain forward time-series sets

Syntax: .ml.gs.tschain[k;n;x;y;f;p;h]

Where

• k is the integer number of folds
• n is the integer number of repetitions of the procedure
• x is a matrix of features
• y is a vector of targets
• f is a function that takes parameters and data as input and returns a score
• p is a dictionary of hyperparameters to be searched
• h is a float value denoting the size of the holdout set used in a fitted gridsearch, where the best model is fit to the holdout set. If 0 the function will return scores for each fold for the given hyperparameters. If negative the data will be shuffled prior to designation of the holdout set.

returns the scores for hyperparameter sets on each of the k folds for all values of h and additionally returns the best hyperparameters and score on the holdout set for 0 < h <=1.

q)m:10000
q)x:(m;10)#(m*10)?1f
q)yc:(raze flip(0N;4)#m#abcd)rank x[;0]
// algos
q)cf:{.p.import[sklearn.tree]:DecisionTreeClassifier}
// params
q)pc:enlist[max_depth]!enlist(::;1;2;3;4;5)
// 5 fold cross-validation no holdout
q).ml.gs.tschain[5;1;x;yc;.ml.xv.fitscore cf;pc;0]
max_depth|
---------| ---------------------------
::       | 1      0.9995 0.999  0.9995
1        | 0.491  0.507  0.491  0.49
2        | 0.7445 0.7545 0.7425 0.744
3        | 1      0.9995 0.999  0.9995
4        | 1      0.9995 0.999  0.9995
5        | 1      0.9995 0.999  0.9995
// 4 fold cross-validated grid search fitted on 20% holdout set
q).ml.gs.tschain[4;1;x;yc;.ml.xv.fitscore cf;pc;.2]
(+(,max_depth)!,(::;1;2;3;4;5))!(1 0.9995 0.999;0.491 0.507 0.491;0.7445 ..
(,max_depth)!,::
0.9995


This works as shown in the following image:

The data is split into equi-sized bins with increasing amounts of the data incorporated into the testing set at each step. This avoids testing a model on historical information which would be counter-productive for time-series forecasting. It also allows users to test the robustness of the model when passed increasing volumes of data.

## .ml.gs.tsroll

Cross-validated parameter grid search applied to roll forward time-series sets

Syntax: .ml.gs.tsroll[k;n;x;y;f;p;h]

Where

• k is the integer number of folds
• n is the integer number of repetitions of the procedure
• x is a matrix of features
• y is a vector of targets
• f is a function that takes parameters and data as input and returns a score
• p is a dictionary of hyperparameters to be searched
• h is a float value denoting the size of the holdout set used in a fitted gridsearch, where the best model is fit to the holdout set. If 0 the function will return scores for each fold for the given hyperparameters. If negative the data will be shuffled prior to designation of the holdout set.

returns the scores for hyperparameter sets on each of the k folds for all values of h and additionally returns the best hyperparameters and score on the holdout set for 0 < h <=1.

q)m:10000
q)x:(m;10)#(m*10)?1f
q)yc:(raze flip(0N;4)#m#abcd)rank x[;0]
// algos
q)cf:{.p.import[sklearn.tree]:DecisionTreeClassifier}
// params
q)pc:enlist[max_depth]!enlist(::;1;2;3;4;5)
// 6-fold cross validation no holdout
q).ml.gs.tsrolls[6;1;x;yc;.ml.xv.fitscore cf;pc;0]
max_depth|
---------| -------------------------------------------------
::       | 0.9994001 0.9988002 1         0.9988002 1
1        | 0.4871026 0.4907019 0.4897959 0.5122975 0.490102
2        | 0.7444511 0.7396521 0.7460984 0.7432513 0.7306539
3        | 0.9994001 0.9988002 1         0.9988002 1
4        | 0.9994001 0.9988002 1         0.9988002 1
5        | 0.9994001 0.9988002 1         0.9988002 1
// 5 fold cross-validated grid search fitted on 20% holdout set
q).ml.gs.tsrolls[4;1;x;yc;.ml.xv.fitscore cf;pc;.2]
(+(,max_depth)!,(::;1;2;3;4;5))!(0.9995 0.999 0.999;0.4965 0.506 0.5015;0...
(,max_depth)!,::
1f


This works as shown in the following image:

Successive equi-sized bins are taken as training and validation sets at each step. This avoids testing a model on historical information which would be counter-productive for time-series forecasting.

## .ml.xv.kfshuff

K-Fold cross validation for randomized non-repeating indices

Syntax: .ml.xv.kfshuff[k;n;x;y;f]

Where

• k is the integer number of folds
• n is the integer number of repetitions of the procedure
• x is a matrix of features
• y is a vector of targets
• f is a function which takes data as input

returns output of f applied to each of the cross-validation splits.

q)m:10000
q)x:(m;10)#(m*10)?1f
q)yr:x[;0]+m?.05
q)k:5
q)n:1
q)ar:{.p.import[sklearn.linear_model]:LinearRegression}
q)mdlfn:.ml.xv.fitscore[ar][]
q).ml.xv.kfshuff[k;n;x;yr;mdlfn]
0.9999935 0.9999934 0.9999935 0.9999935 0.9999935


## .ml.xv.kfsplit

K-Fold cross-validation for ascending indices split into K-folds

Syntax: .ml.xv.kfsplit[k;n;x;y;f]

Where

• k is the integer number of folds
• n is the integer number of repetitions of the procedure
• x is a matrix of features
• y is a vector of targets
• f is a function which takes data as input

returns output of f applied to each of the cross-validation splits.

q)m:10000
q)x:(m;10)#(m*10)?1f
q)yr:x[;0]+m?.05
q)k:5
q)n:1
q)ar:{.p.import[sklearn.linear_model]:LinearRegression}
q)mdlfn:.ml.xv.fitscore[ar][]
q).ml.xv.kfsplit[k;n;x;yr;mdlfn]
0.9953383 0.9995422 0.9985156 0.9995144 0.9952133


## .ml.xv.kfstrat

Stratified K-Fold cross-validation with an approximately equal distribution of classes per fold

Syntax: .ml.xv.kfstrat[k;n;x;y;f]

Where

• k is the integer number of folds
• n is the integer number of repetitions of the procedure
• x is a matrix of features
• y is a vector of targets
• f is a function which takes data as input

returns output of f applied to each of the cross-validation splits.

q)m:10000
q)x:(m;10)#(m*10)?1f
q)yc:(raze flip(0N;4)#m#abcd)rank x[;0]
q)k:5
q)n:1
q)ac:{.p.import[sklearn.tree]:DecisionTreeClassifier}
q)mdlfn:.ml.xv.fitscore[ac][]
q).ml.xv.kfsplit[k;n;x;yc;mdlfn]
0.9995 0.9995 0.9995 1 1


This is used extensively where the distribution of classes in the data is unbalanced.

## .ml.xv.mcsplit

Monte-Carlo cross validation using randomized non-repeating indices

Syntax: .ml.xv.mcsplit[p;n;x;y;f]

Where

• p is a float between 0 and 1 representing the percentage of data within the validation set
• n is the integer number of repetitions of the procedure
• x is a matrix of features
• y is a vector of targets
• f is a function which takes data as input

returns output of f applied to each of the cross-validation splits.

q)m:10000
q)x:(m;10)#(m*10)?1f
q)yr:x[;0]+m?.05
q)p:0.2
q)n:5
q)ar:{.p.import[sklearn.linear_model]:LinearRegression}
q)mdlfn:.ml.xv.fitscore[ar][]
q).ml.xv.mcsplit[p;n;x;yr;mdlfn]
0.9999905 0.9999906 0.9999905 0.9999905 0.9999905


This form of cross validation is also known as repeated random sub-sampling validation. This has advantages over k-fold when equi-sized bins of observations are not wanted or where outliers could heavily bias the classifier.

## .ml.xv.pcsplit

Percentage split cross-validation procedure

Syntax: .ml.xv.pcsplit[p;n;x;y;f]

Where

• p is a float between 0 and 1 representing the percentage of data within the validation set
• n is the integer number of repetitions of the procedure
• x is a matrix of features
• y is a vector of targets
• f is a function which takes data as input

returns output of f applied to each of the cross-validation splits.

q)m:10000
q)x:(m;10)#(m*10)?1f
q)yr:x[;0]+m?.05
q)p:0.2
q)n:4
q)ar:{.p.import[sklearn.linear_model]:LinearRegression}
q)mdlfn:.ml.xv.fitscore[ar][]
q).ml.xv.pcsplit[p;n;x;yr;mdlfn]
0.9975171 0.9975171 0.9975171 0.9975171


## .ml.xv.tschain

Chain-forward cross-validation procedure

Syntax: .ml.xv.tschain[k;n;x;y;f]

Where

• k is the integer number of folds
• n is the integer number of repetitions of the procedure
• x is a matrix of features
• y is a vector of targets
• f is a function which takes data as input

returns output of f applied to each of the chained iterations.

q)m:10000
q)x:(m;10)#(m*10)?1f
q)yr:x[;0]+m?.05
q)k:5
q)n:1
q)ar:{.p.import[sklearn.linear_model]:LinearRegression}
q)mdlfn:.ml.xv.fitscore[ar][]
q).ml.xv.tschain[k;n;x;yr;mdlfn]
0.9973771 0.9992741 0.9996898 0.9997031


This works as shown in the following image.

The data is split into equi-sized bins with increasing amounts of the data incorporated in the testing set at each step. This avoids testing a model on historical information which would be counter-productive for time-series forecasting. It also allows users to test the robustness of the model with data of increasing volumes.

## .ml.xv.tsrolls

Roll-forward cross-validation procedure

Syntax: .ml.xv.tsrolls[k;n;x;y;f]

Where

• k is the integer number of folds
• n is the integer number of repetitions of the procedure
• x is a matrix of features
• y is a vector of targets
• f is a function which takes data as input

returns output of f applied to each of the chained iterations.

q)m:10000
q)x:(m;10)#(m*10)?1f
q)yc:(raze flip(0N;4)#m#abcd)rank x[;0]
q)k:5
q)n:1
q)ac:{.p.import[sklearn.tree]:DecisionTreeClassifier}
q)mdlfn:.ml.xv.fitscore[ac][]
q).ml.xv.tsrolls[k;n;x;yc;mdlfn]
0.9973771 0.9995615 0.9999869 0.999965


This works as shown in the following image.

Successive equi-sized bins are taken as validation and training sets for each step. This avoids testing a model on historical information which would be counter-productive for time-series forecasting.