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The following commit(s) were added to refs/heads/master by this push:
new 5b22c42 [MINOR] Force ytest argument in lmPredict
5b22c42 is described below
commit 5b22c4246cb88d7e63e99d7e1a463a255c0bc666
Author: baunsgaard <[email protected]>
AuthorDate: Fri Apr 30 12:10:13 2021 +0200
[MINOR] Force ytest argument in lmPredict
This is not the ideal solution, but just to make the test pass the
LMPredict now require ytest argument,
The reason i simply because the python generators does not like default
parameters on matrice arguments yet.
---
docs/site/builtins-reference.md | 6 +++---
scripts/builtin/cvlm.dml | 2 +-
scripts/builtin/hyperband.dml | 2 +-
scripts/builtin/mice.dml | 2 +-
scripts/builtin/outlierByArima.dml | 2 +-
src/test/scripts/functions/builtin/lmpredict.dml | 2 +-
src/test/scripts/functions/lineage/LineageReuseAlg6.dml | 2 +-
src/test/scripts/functions/recompile/IPAFunctionArgsFor.dml | 4 ++--
src/test/scripts/functions/recompile/IPAFunctionArgsParfor.dml | 4 ++--
9 files changed, 13 insertions(+), 13 deletions(-)
diff --git a/docs/site/builtins-reference.md b/docs/site/builtins-reference.md
index e03f7b1..c3d21ca 100644
--- a/docs/site/builtins-reference.md
+++ b/docs/site/builtins-reference.md
@@ -824,7 +824,7 @@ The `lmPredict`-function predicts the class of a feature
vector.
### Usage
```r
-lmPredict(X=X, B=w)
+lmPredict(X=X, B=w, ytest= Y)
```
### Arguments
@@ -833,7 +833,7 @@ lmPredict(X=X, B=w)
| :------ | :------------- | -------- | :---------- |
| X | Matrix[Double] | required | Matrix of feature vector(s). |
| B | Matrix[Double] | required | 1-column matrix of weights. |
-| ytest | Matrix[Double] | optional | Optional test labels, used only for
verbose output. |
+| ytest | Matrix[Double] | required | test labels, used only for verbose
output. can be set to matrix(0,1,1) if verbose output is not wanted |
| icpt | Integer | 0 | Intercept presence, shifting and
rescaling of X ([Details](#icpt-argument))|
| verbose | Boolean | FALSE | Print various statistics for
evaluating accuracy. |
@@ -850,7 +850,7 @@ lmPredict(X=X, B=w)
X = rand (rows = 50, cols = 10)
y = X %*% rand(rows = ncol(X), cols = 1)
w = lm(X = X, y = y)
-yp = lmPredict(X = X, B = w)
+yp = lmPredict(X = X, B = w, ytest=matrix(0,1,1))
```
## `mice`-Function
diff --git a/scripts/builtin/cvlm.dml b/scripts/builtin/cvlm.dml
index 4836d0b..f607e74 100644
--- a/scripts/builtin/cvlm.dml
+++ b/scripts/builtin/cvlm.dml
@@ -42,7 +42,7 @@ m_cvlm = function(Matrix[Double] X, Matrix[Double] y, Integer
k, Integer icpt =
}
beta = lm(X=trainSet, y=trainRes, icpt=icpt, reg=reg);
- pred = lmPredict(X=testSet, B=beta, icpt=icpt);
+ pred = lmPredict(X=testSet, B=beta, ytest=matrix(0,1,1), icpt=icpt);
y_predict[testS:testE,] = pred;
allbeta[i,] = t(beta);
}
diff --git a/scripts/builtin/hyperband.dml b/scripts/builtin/hyperband.dml
index e394ccc..fd4754d 100644
--- a/scripts/builtin/hyperband.dml
+++ b/scripts/builtin/hyperband.dml
@@ -104,7 +104,7 @@ m_hyperband = function(Matrix[Double] X_train,
Matrix[Double] y_train,
tol=as.scalar(args[1]), reg=as.scalar(args[2]), maxi=r_i,
verbose=FALSE));
candidateWeights[curCandidate] = t(weights)
- preds = lmPredict(X=X_val, B=weights);
+ preds = lmPredict(X=X_val, B=weights, ytest= matrix(0,1,1));
scoreboard[curCandidate,1] = as.matrix(sum((y_val - preds)^2));
}
diff --git a/scripts/builtin/mice.dml b/scripts/builtin/mice.dml
index 3372886..a5243c0 100644
--- a/scripts/builtin/mice.dml
+++ b/scripts/builtin/mice.dml
@@ -140,7 +140,7 @@ m_mice= function(Matrix[Double] X, Matrix[Double] cMask,
Integer iter = 3,
# learn a regression line
beta = lm(X=train_X, y=train_Y, verbose=FALSE, icpt=1, reg = 1e-7, tol
= 1e-7);
# predicting missing values
- pred = lmPredict(X=test_X, B=beta, icpt=1)
+ pred = lmPredict(X=test_X, B=beta, ytest= matrix(0,1,1), icpt=1)
# imputing missing column values (assumes Mask_Filled being 0/1-matrix)
R = removeEmpty(target=Mask_Filled[, in_c] * seq(1,n), margin="rows");
# TODO modify removeEmpty to return zero row and n columns
diff --git a/scripts/builtin/outlierByArima.dml
b/scripts/builtin/outlierByArima.dml
index 114c28d..8c3ca3a 100644
--- a/scripts/builtin/outlierByArima.dml
+++ b/scripts/builtin/outlierByArima.dml
@@ -64,7 +64,7 @@ m_outlierByArima = function(Matrix[Double] X, Double k = 3,
Integer repairMethod
# TODO replace by ARIMA once fully supported, LM only emulated the AR part
model = lm(X=features, y=X_adapted)
- y_hat = lmPredict(X=features, B=model)
+ y_hat = lmPredict(X=features, B=model, ytest=matrix(0,1,1))
upperBound = sd(X) + k * y_hat
lowerBound = sd(X) - k * y_hat
diff --git a/src/test/scripts/functions/builtin/lmpredict.dml
b/src/test/scripts/functions/builtin/lmpredict.dml
index 1b0c2ec..d99e7e2 100644
--- a/src/test/scripts/functions/builtin/lmpredict.dml
+++ b/src/test/scripts/functions/builtin/lmpredict.dml
@@ -23,5 +23,5 @@ X = read($1) # Training data
y = read($2) # response values
p = read($3) # random data to predict
w = lmDS(X = X, y = y, icpt = 1, reg = 1e-12)
-p = lmPredict(X = X, B = w, icpt = 1)
+p = lmPredict(X = X, B = w, ytest=matrix(0,1,1), icpt = 1)
write(p, $4)
diff --git a/src/test/scripts/functions/lineage/LineageReuseAlg6.dml
b/src/test/scripts/functions/lineage/LineageReuseAlg6.dml
index 082580b..5dc64b0 100644
--- a/src/test/scripts/functions/lineage/LineageReuseAlg6.dml
+++ b/src/test/scripts/functions/lineage/LineageReuseAlg6.dml
@@ -89,7 +89,7 @@ Kc = floor(ncol(A) * 0.8);
for (i in 1:10) {
newA1 = PCA(A=A, K=Kc+i);
beta1 = lm(X=newA1, y=y, icpt=1, reg=0.0001, verbose=FALSE);
- y_predict1 = lmPredict(X=newA1, B=beta1, icpt=1);
+ y_predict1 = lmPredict(X=newA1, B=beta1, ytest=matrix(0,1,1), icpt=1);
R2_ad1 = checkR2(newA1, y, y_predict1, beta1, 1);
R[,i] = R2_ad1;
}
diff --git a/src/test/scripts/functions/recompile/IPAFunctionArgsFor.dml
b/src/test/scripts/functions/recompile/IPAFunctionArgsFor.dml
index 9b32d39..0570693 100644
--- a/src/test/scripts/functions/recompile/IPAFunctionArgsFor.dml
+++ b/src/test/scripts/functions/recompile/IPAFunctionArgsFor.dml
@@ -92,7 +92,7 @@ Kc = floor(ncol(A) * 0.8);
for (i in 1:10) {
newA1 = PCA(A=A, K=Kc+i);
beta1 = lm(X=newA1, y=y, icpt=1, reg=0.0001, verbose=FALSE);
- y_predict1 = lmPredict(X=newA1, B=beta1, icpt=1);
+ y_predict1 = lmPredict(X=newA1, B=beta1, ytest=matrix(0,1,1), icpt=1);
R2_ad1 = checkR2(newA1, y, y_predict1, beta1, 1);
R[,i] = R2_ad1;
}
@@ -100,7 +100,7 @@ for (i in 1:10) {
for (i in 1:10) {
newA3 = PCA(A=A, K=Kc+5);
beta3 = lm(X=newA3, y=y, icpt=1, reg=0.001*i, verbose=FALSE);
- y_predict3 = lmPredict(X=newA3, B=beta3, icpt=1);
+ y_predict3 = lmPredict(X=newA3, B=beta3, ytest=matrix(0,1,1), icpt=1);
R2_ad3 = checkR2(newA3, y, y_predict3, beta3, 1);
R[,10+i] = R2_ad3;
}
diff --git a/src/test/scripts/functions/recompile/IPAFunctionArgsParfor.dml
b/src/test/scripts/functions/recompile/IPAFunctionArgsParfor.dml
index 415e72e..795f507 100644
--- a/src/test/scripts/functions/recompile/IPAFunctionArgsParfor.dml
+++ b/src/test/scripts/functions/recompile/IPAFunctionArgsParfor.dml
@@ -92,7 +92,7 @@ Kc = floor(ncol(A) * 0.8);
for (i in 1:10) {
newA1 = PCA(A=A, K=Kc+i);
beta1 = lm(X=newA1, y=y, icpt=1, reg=0.0001, verbose=FALSE);
- y_predict1 = lmPredict(X=newA1, B=beta1, icpt=1);
+ y_predict1 = lmPredict(X=newA1, B=beta1, ytest=matrix(0,1,1), icpt=1);
R2_ad1 = checkR2(newA1, y, y_predict1, beta1, 1);
R[,i] = R2_ad1;
}
@@ -100,7 +100,7 @@ for (i in 1:10) {
parfor (i in 1:10) {
newA3 = PCA(A=A, K=Kc+5);
beta3 = lm(X=newA3, y=y, icpt=1, reg=0.001*i, verbose=FALSE);
- y_predict3 = lmPredict(X=newA3, B=beta3, icpt=1);
+ y_predict3 = lmPredict(X=newA3, B=beta3, ytest=matrix(0,1,1), icpt=1);
R2_ad3 = checkR2(newA3, y, y_predict3, beta3, 1);
R[,10+i] = R2_ad3;
}
