all: fix the warnings from linter revive

This rename all variable "Ids" into "ListID".

6 files changed, 59 insertions, 59 deletions

diff --git a/lib/mining/classifier/cm.go b/lib/mining/classifier/cm.go
index 786cf4e4..19d86349 100644
--- a/lib/mining/classifier/cm.go
+++ b/lib/mining/classifier/cm.go
@@ -16,14 +16,14 @@ type CM struct {
 	// rowNames contain name in each row.
 	rowNames []string
 
-	// tpIds contain index of true-positive samples.
-	tpIds []int
-	// fpIds contain index of false-positive samples.
-	fpIds []int
-	// tnIds contain index of true-negative samples.
-	tnIds []int
-	// fnIds contain index of false-negative samples.
-	fnIds []int
+	// tpListID contain index of true-positive samples.
+	tpListID []int
+	// fpListID contain index of false-positive samples.
+	fpListID []int
+	// tnListID contain index of true-negative samples.
+	tnListID []int
+	// fnListID contain index of false-negative samples.
+	fnListID []int
 
 	tabula.Dataset
 
@@ -201,7 +201,7 @@ func (cm *CM) computeClassError() {
 // GroupIndexPredictions given index of samples, group the samples by their
 // class of prediction. For example,
 //
-//	sampleIds:   [0, 1, 2, 3, 4, 5]
+//	sampleListID:   [0, 1, 2, 3, 4, 5]
 //	actuals:     [1, 1, 0, 0, 1, 0]
 //	predictions: [1, 0, 1, 0, 1, 1]
 //
@@ -214,18 +214,18 @@ func (cm *CM) computeClassError() {
 //	     false-negative indices: [1]
 //
 // This function assume that positive value as "1" and negative value as "0".
-func (cm *CM) GroupIndexPredictions(sampleIds []int,
+func (cm *CM) GroupIndexPredictions(sampleListID []int,
 	actuals, predictions []int64,
 ) {
 	// Reset indices.
-	cm.tpIds = nil
-	cm.fpIds = nil
-	cm.tnIds = nil
-	cm.fnIds = nil
+	cm.tpListID = nil
+	cm.fpListID = nil
+	cm.tnListID = nil
+	cm.fnListID = nil
 
 	// Make sure we are not out-of-range when looping, always pick the
 	// minimum length between the three parameters.
-	min := len(sampleIds)
+	min := len(sampleListID)
 	if len(actuals) < min {
 		min = len(actuals)
 	}
@@ -236,15 +236,15 @@ func (cm *CM) GroupIndexPredictions(sampleIds []int,
 	for x := 0; x < min; x++ {
 		if actuals[x] == 1 {
 			if predictions[x] == 1 {
-				cm.tpIds = append(cm.tpIds, sampleIds[x])
+				cm.tpListID = append(cm.tpListID, sampleListID[x])
 			} else {
-				cm.fnIds = append(cm.fnIds, sampleIds[x])
+				cm.fnListID = append(cm.fnListID, sampleListID[x])
 			}
 		} else {
 			if predictions[x] == 1 {
-				cm.fpIds = append(cm.fpIds, sampleIds[x])
+				cm.fpListID = append(cm.fpListID, sampleListID[x])
 			} else {
-				cm.tnIds = append(cm.tnIds, sampleIds[x])
+				cm.tnListID = append(cm.tnListID, sampleListID[x])
 			}
 		}
 	}
@@ -252,22 +252,22 @@ func (cm *CM) GroupIndexPredictions(sampleIds []int,
 
 // GroupIndexPredictionsStrings is an alternative to GroupIndexPredictions
 // which work with string class.
-func (cm *CM) GroupIndexPredictionsStrings(sampleIds []int,
+func (cm *CM) GroupIndexPredictionsStrings(sampleListID []int,
 	actuals, predictions []string,
 ) {
-	if len(sampleIds) == 0 {
+	if len(sampleListID) == 0 {
 		return
 	}
 
 	// Reset indices.
-	cm.tpIds = nil
-	cm.fpIds = nil
-	cm.tnIds = nil
-	cm.fnIds = nil
+	cm.tpListID = nil
+	cm.fpListID = nil
+	cm.tnListID = nil
+	cm.fnListID = nil
 
 	// Make sure we are not out-of-range when looping, always pick the
 	// minimum length between the three parameters.
-	min := len(sampleIds)
+	min := len(sampleListID)
 	if len(actuals) < min {
 		min = len(actuals)
 	}
@@ -278,15 +278,15 @@ func (cm *CM) GroupIndexPredictionsStrings(sampleIds []int,
 	for x := 0; x < min; x++ {
 		if actuals[x] == "1" {
 			if predictions[x] == "1" {
-				cm.tpIds = append(cm.tpIds, sampleIds[x])
+				cm.tpListID = append(cm.tpListID, sampleListID[x])
 			} else {
-				cm.fnIds = append(cm.fnIds, sampleIds[x])
+				cm.fnListID = append(cm.fnListID, sampleListID[x])
 			}
 		} else {
 			if predictions[x] == "1" {
-				cm.fpIds = append(cm.fpIds, sampleIds[x])
+				cm.fpListID = append(cm.fpListID, sampleListID[x])
 			} else {
-				cm.tnIds = append(cm.tnIds, sampleIds[x])
+				cm.tnListID = append(cm.tnListID, sampleListID[x])
 			}
 		}
 	}
@@ -356,22 +356,22 @@ func (cm *CM) TN() int {
 
 // TPIndices return indices of all true-positive samples.
 func (cm *CM) TPIndices() []int {
-	return cm.tpIds
+	return cm.tpListID
 }
 
 // FNIndices return indices of all false-negative samples.
 func (cm *CM) FNIndices() []int {
-	return cm.fnIds
+	return cm.fnListID
 }
 
 // FPIndices return indices of all false-positive samples.
 func (cm *CM) FPIndices() []int {
-	return cm.fpIds
+	return cm.fpListID
 }
 
 // TNIndices return indices of all true-negative samples.
 func (cm *CM) TNIndices() []int {
-	return cm.tnIds
+	return cm.tnListID
 }
 
 // String will return the output of confusion matrix in table like format.
@@ -395,8 +395,8 @@ func (cm *CM) String() (s string) {
 		s += "\n"
 	}
 
-	s += fmt.Sprintf("TP-FP indices %d %d\n", len(cm.tpIds), len(cm.fpIds))
-	s += fmt.Sprintf("FN-TN indices %d %d\n", len(cm.fnIds), len(cm.tnIds))
+	s += fmt.Sprintf("TP-FP indices %d %d\n", len(cm.tpListID), len(cm.fpListID))
+	s += fmt.Sprintf("FN-TN indices %d %d\n", len(cm.fnListID), len(cm.tnListID))
 
 	return
 }
diff --git a/lib/mining/classifier/cm_test.go b/lib/mining/classifier/cm_test.go
index 30041388..9c2b023e 100644
--- a/lib/mining/classifier/cm_test.go
+++ b/lib/mining/classifier/cm_test.go
@@ -48,7 +48,7 @@ func TestComputeStrings(t *testing.T) {
 }
 
 func TestGroupIndexPredictions(t *testing.T) {
-	testIds := []int{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}
+	testListID := []int{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}
 	actuals := []int64{1, 1, 1, 1, 0, 0, 0, 0, 0, 0}
 	predics := []int64{1, 1, 0, 1, 0, 0, 0, 0, 1, 0}
 	exp := [][]int{
@@ -60,7 +60,7 @@ func TestGroupIndexPredictions(t *testing.T) {
 
 	cm := &CM{}
 
-	cm.GroupIndexPredictions(testIds, actuals, predics)
+	cm.GroupIndexPredictions(testListID, actuals, predics)
 
 	test.Assert(t, "", exp[0], cm.TPIndices())
 	test.Assert(t, "", exp[1], cm.FNIndices())
diff --git a/lib/mining/classifier/crf/crf.go b/lib/mining/classifier/crf/crf.go
index eba0dab7..f49e35d4 100644
--- a/lib/mining/classifier/crf/crf.go
+++ b/lib/mining/classifier/crf/crf.go
@@ -328,8 +328,8 @@ func (crf *Runtime) runTPSet(samples tabula.ClasetInterface) {
 		return
 	}
 
-	tnIds := numbers.IntCreateSeq(0, crf.tnset.Len()-1)
-	_, cm, _ := crf.ClassifySetByWeight(crf.tnset, tnIds)
+	tnListID := numbers.IntCreateSeq(0, crf.tnset.Len()-1)
+	_, cm, _ := crf.ClassifySetByWeight(crf.tnset, tnListID)
 
 	crf.refillWithFP(samples, crf.tnset, cm)
 }
@@ -358,7 +358,7 @@ func (crf *Runtime) runTPSet(samples tabula.ClasetInterface) {
 // (1.4) Save stage probabilities for positive class.
 // (2) Compute confusion matrix.
 func (crf *Runtime) ClassifySetByWeight(samples tabula.ClasetInterface,
-	sampleIds []int,
+	sampleListID []int,
 ) (
 	predicts []string, cm *classifier.CM, probs []float64,
 ) {
@@ -410,7 +410,7 @@ func (crf *Runtime) ClassifySetByWeight(samples tabula.ClasetInterface,
 
 	// (2)
 	actuals := samples.GetClassAsStrings()
-	cm = crf.ComputeCM(sampleIds, vs, actuals, predicts)
+	cm = crf.ComputeCM(sampleListID, vs, actuals, predicts)
 
 	crf.ComputeStatFromCM(&stat, cm)
 	stat.End()
diff --git a/lib/mining/classifier/crf/crf_test.go b/lib/mining/classifier/crf/crf_test.go
index 402f6213..d84045f0 100644
--- a/lib/mining/classifier/crf/crf_test.go
+++ b/lib/mining/classifier/crf/crf_test.go
@@ -23,7 +23,7 @@ func runCRF(t *testing.T, sampleFile, statFile, perfFile string, nstage, ntree i
 	}
 
 	nbag := (samples.Len() * 63) / 100
-	train, test, _, testIds := tabula.RandomPickRows(&samples, nbag, false)
+	train, test, _, testListID := tabula.RandomPickRows(&samples, nbag, false)
 
 	trainset := train.(tabula.ClasetInterface)
 	testset := test.(tabula.ClasetInterface)
@@ -45,7 +45,7 @@ func runCRF(t *testing.T, sampleFile, statFile, perfFile string, nstage, ntree i
 	testset.RecountMajorMinor()
 	fmt.Println("Testset:", testset)
 
-	predicts, cm, probs := crfRuntime.ClassifySetByWeight(testset, testIds)
+	predicts, cm, probs := crfRuntime.ClassifySetByWeight(testset, testListID)
 
 	fmt.Println("Confusion matrix:", cm)
 
diff --git a/lib/mining/classifier/rf/rf.go b/lib/mining/classifier/rf/rf.go
index f725fff1..6a1ae6fd 100644
--- a/lib/mining/classifier/rf/rf.go
+++ b/lib/mining/classifier/rf/rf.go
@@ -228,9 +228,9 @@ func (forest *Runtime) GrowTree(samples tabula.ClasetInterface) (
 
 // ClassifySet given a samples predict their class by running each sample in
 // forest, and return their class prediction with confusion matrix.
-// `samples` is the sample that will be predicted, `sampleIds` is the index of
+// `samples` is the sample that will be predicted, `sampleListID` is the index of
 // samples.
-// If `sampleIds` is not nil, then sample index will be checked in each tree,
+// If `sampleListID` is not nil, then sample index will be checked in each tree,
 // if the sample is used for training, their vote is not counted.
 //
 // Algorithm,
@@ -242,17 +242,17 @@ func (forest *Runtime) GrowTree(samples tabula.ClasetInterface) (
 // (1.3) compute and save the actual class probabilities.
 // (2) Compute confusion matrix from predictions.
 // (3) Compute stat from confusion matrix.
-// (4) Write the stat to file only if sampleIds is empty, which mean its run
+// (4) Write the stat to file only if sampleListID is empty, which mean its run
 // not from OOB set.
 func (forest *Runtime) ClassifySet(samples tabula.ClasetInterface,
-	sampleIds []int,
+	sampleListID []int,
 ) (
 	predicts []string, cm *classifier.CM, probs []float64,
 ) {
 	stat := classifier.Stat{}
 	stat.Start()
 
-	if len(sampleIds) == 0 {
+	if len(sampleListID) == 0 {
 		fmt.Println(tag, "Classify set:", samples)
 		fmt.Println(tag, "Classify set sample (one row):",
 			samples.GetRow(0))
@@ -267,8 +267,8 @@ func (forest *Runtime) ClassifySet(samples tabula.ClasetInterface,
 	rows := samples.GetRows()
 	for x, row := range *rows {
 		// (1.1)
-		if len(sampleIds) > 0 {
-			sampleIdx = sampleIds[x]
+		if len(sampleListID) > 0 {
+			sampleIdx = sampleListID[x]
 		}
 		votes := forest.Votes(row, sampleIdx)
 
@@ -286,13 +286,13 @@ func (forest *Runtime) ClassifySet(samples tabula.ClasetInterface,
 	}
 
 	// (2)
-	cm = forest.ComputeCM(sampleIds, vs, actuals, predicts)
+	cm = forest.ComputeCM(sampleListID, vs, actuals, predicts)
 
 	// (3)
 	forest.ComputeStatFromCM(&stat, cm)
 	stat.End()
 
-	if len(sampleIds) == 0 {
+	if len(sampleListID) == 0 {
 		fmt.Println(tag, "CM:", cm)
 		fmt.Println(tag, "Classifying stat:", stat)
 		_ = stat.Write(forest.StatFile)
diff --git a/lib/mining/classifier/runtime.go b/lib/mining/classifier/runtime.go
index 09804f11..56296c7d 100644
--- a/lib/mining/classifier/runtime.go
+++ b/lib/mining/classifier/runtime.go
@@ -99,7 +99,7 @@ func (rt *Runtime) AddStat(stat *Stat) {
 
 // ComputeCM will compute confusion matrix of sample using value space, actual
 // and prediction values.
-func (rt *Runtime) ComputeCM(sampleIds []int,
+func (rt *Runtime) ComputeCM(sampleListID []int,
 	vs, actuals, predicts []string,
 ) (
 	cm *CM,
@@ -107,7 +107,7 @@ func (rt *Runtime) ComputeCM(sampleIds []int,
 	cm = &CM{}
 
 	cm.ComputeStrings(vs, actuals, predicts)
-	cm.GroupIndexPredictionsStrings(sampleIds, actuals, predicts)
+	cm.GroupIndexPredictionsStrings(sampleListID, actuals, predicts)
 
 	return cm
 }
@@ -309,12 +309,12 @@ func (rt *Runtime) Performance(samples tabula.ClasetInterface,
 ) {
 	// (1)
 	actuals := samples.GetClassAsStrings()
-	sortedIds := numbers.IntCreateSeq(0, len(probs)-1)
-	floats64.InplaceMergesort(probs, sortedIds, 0, len(probs), false)
+	sortedListID := numbers.IntCreateSeq(0, len(probs)-1)
+	floats64.InplaceMergesort(probs, sortedListID, 0, len(probs), false)
 
 	// (2)
-	libstrings.SortByIndex(&actuals, sortedIds)
-	libstrings.SortByIndex(&predicts, sortedIds)
+	libstrings.SortByIndex(&actuals, sortedListID)
+	libstrings.SortByIndex(&predicts, sortedListID)
 
 	// (3)
 	rt.computePerfByProbs(samples, actuals, probs)