diff --git a/applyforest/applyforest.go b/applyforest/applyforest.go index 572c78c..a0731c1 100644 --- a/applyforest/applyforest.go +++ b/applyforest/applyforest.go @@ -24,7 +24,7 @@ func main() { var sum bool flag.BoolVar(&sum, "sum", false, "Force numeric sum voting (for gradient boosting etc).") var expit bool - flag.BoolVar(&expit, "expit", false, "Expit (inverst logit) transform data (for gradient boosting classification).") + flag.BoolVar(&expit, "expit", false, "Expit (inverse logit) transform data (for gradient boosting classification).") var cat bool flag.BoolVar(&cat, "mode", false, "Force categorical (mode) voting.") @@ -46,6 +46,10 @@ func main() { if err != nil { log.Fatal(err) } + if forest.PredCfg.Gradboost != 0 { + sum = true + } + fmt.Printf("Forest is of type %v.\n", forest.Type.String()) var predfile *os.File if *predfn != "" { diff --git a/catballotbox.go b/catballotbox.go index bb688ac..8ae5471 100644 --- a/catballotbox.go +++ b/catballotbox.go @@ -126,3 +126,9 @@ func (bb *CatBallotBox) TallyError(feature Feature) float64 { e = 1.0 - e return e } + +// TallyNum always returns NaN for a SumBallotBox; we +// meet the demands of the expanded VoteTallyer interface. +func (bb *CatBallotBox) TallyNum(i int) (predicted float64) { + return nan +} diff --git a/cfpred/cfpred.go b/cfpred/cfpred.go new file mode 100644 index 0000000..dbb4371 --- /dev/null +++ b/cfpred/cfpred.go @@ -0,0 +1,73 @@ +package main + +import ( + "flag" + "fmt" + "log" + "os" + + "github.com/lytics/CloudForest" +) + +func main() { + fm := flag.String("fm", + "featurematrix.afm", "AFM formated feature matrix containing data.") + rf := flag.String("rfpred", + "rface.sf", "A predictor forest.") + predfn := flag.String("preds", + "", "The name of a file to write the predictions into.") + + flag.Parse() + + //Parse Data + data, err := CloudForest.LoadAFM(*fm) + if err != nil { + log.Fatal(err) + } + + forestfile, err := os.Open(*rf) // For read access. + if err != nil { + log.Fatal(err) + } + defer forestfile.Close() + forestreader := CloudForest.NewForestReader(forestfile) + forest, err := forestreader.ReadForest() + if err != nil { + log.Fatal(err) + } + fmt.Printf("Forest is of type %v.\n", forest.Type.String()) + + var predfile *os.File + if *predfn != "" { + predfile, err = os.Create(*predfn) + if err != nil { + log.Fatal(err) + } + defer predfile.Close() + } + targeti, hasTarget := data.Map[forest.Target] + + preds, err := CloudForest.Predict(data, forest) + if err != nil { + log.Fatal(err) + } + if *predfn != "" { + fmt.Printf("Outputting label predicted actual tsv to %v\n", *predfn) + for i, l := range data.CaseLabels { + actual := "NA" + if hasTarget { + actual = data.Data[targeti].GetStr(i) + } + + var result string + if preds.IsReal { + result = fmt.Sprintf("%v", preds.Real[i]) + } else { + result = preds.Cat[i] + } + + fmt.Fprintf(predfile, "%v\t%v\t%v\n", l, result, actual) + } + } + +} diff --git a/data/pred_test_expected_predictions.tsv 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+FOREST=0,TARGET="N:X",PREDCONFIG={"Type":"Regressor","Targetname":"N:X","Costs":"","Dentropy":"","Adacosts":"","Rfweights":"","Blacklist":"","L1":false,"Density":false,"Positive":"True","Entropy":false,"Adaboost":false,"Gradboost":0,"Ordinal":false,"Expit":false} +TREE=0,TARGET="N:X" +NODE=*,SPLITTER=N:area,SPLITTERTYPE=NUMERICAL,LVALUES=0.845,RVALUES=0.845 +NODE=*L,SPLITTER=N:DC,SPLITTERTYPE=NUMERICAL,LVALUES=665.05,RVALUES=665.05 +NODE=*LL,SPLITTER=N:DC,SPLITTERTYPE=NUMERICAL,LVALUES=454.75,RVALUES=454.75 +NODE=*LLL,SPLITTER=N:Y,SPLITTERTYPE=NUMERICAL,LVALUES=4.5,RVALUES=4.5 +NODE=*LLLL,SPLITTER=N:Y,SPLITTERTYPE=NUMERICAL,LVALUES=2.5,RVALUES=2.5 +NODE=*LLLLL,SPLITTER=N:FFMC,SPLITTERTYPE=NUMERICAL,LVALUES=88.3,RVALUES=88.3 +NODE=*LLLLLL,PRED=2 +NODE=*LLLLLR,PRED=1 +NODE=*LLLLR,SPLITTER=N:DMC,SPLITTERTYPE=NUMERICAL,LVALUES=26.549999999999997,RVALUES=26.549999999999997 +NODE=*LLLLRL,SPLITTER=C:day,SPLITTERTYPE=CATEGORICAL,LVALUES="fri:sun:wed" +NODE=*LLLLRLL,SPLITTER=C:day,SPLITTERTYPE=CATEGORICAL,LVALUES="sun" +NODE=*LLLLRLLL,PRED=5 +NODE=*LLLLRLLR,PRED=6 +NODE=*LLLLRLR,SPLITTER=N:temp,SPLITTERTYPE=NUMERICAL,LVALUES=14.649999999999999,RVALUES=14.649999999999999 +NODE=*LLLLRLRL,SPLITTER=N:DC,SPLITTERTYPE=NUMERICAL,LVALUES=47.45,RVALUES=47.45 +NODE=*LLLLRLRLL,PRED=3 +NODE=*LLLLRLRLR,PRED=4 +NODE=*LLLLRLRR,PRED=2.5 +NODE=*LLLLRR,SPLITTER=N:ISI,SPLITTERTYPE=NUMERICAL,LVALUES=31.2,RVALUES=31.2 +NODE=*LLLLRRL,PRED=6 +NODE=*LLLLRRR,PRED=7 +NODE=*LLLR,SPLITTER=N:wind,SPLITTERTYPE=NUMERICAL,LVALUES=4.25,RVALUES=4.25 +NODE=*LLLRL,SPLITTER=N:temp,SPLITTERTYPE=NUMERICAL,LVALUES=13.7,RVALUES=13.7 +NODE=*LLLRLL,SPLITTER=N:Y,SPLITTERTYPE=NUMERICAL,LVALUES=5.5,RVALUES=5.5 +NODE=*LLLRLLL,SPLITTER=N:temp,SPLITTERTYPE=NUMERICAL,LVALUES=11.2,RVALUES=11.2 +NODE=*LLLRLLLL,SPLITTER=C:day,SPLITTERTYPE=CATEGORICAL,LVALUES="sun" +NODE=*LLLRLLLLL,PRED=4 +NODE=*LLLRLLLLR,PRED=5.25 +NODE=*LLLRLLLR,PRED=7 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+NODE=*RRLLR,SPLITTER=C:month,SPLITTERTYPE=CATEGORICAL,LVALUES="aug:jul" +NODE=*RRLLRL,SPLITTER=N:DC,SPLITTERTYPE=NUMERICAL,LVALUES=639.1,RVALUES=639.1 +NODE=*RRLLRLL,SPLITTER=N:DMC,SPLITTERTYPE=NUMERICAL,LVALUES=132.64999999999998,RVALUES=132.64999999999998 +NODE=*RRLLRLLL,PRED=5 +NODE=*RRLLRLLR,PRED=3.6666666666666665 +NODE=*RRLLRLR,PRED=2.5 +NODE=*RRLLRR,SPLITTER=N:Y,SPLITTERTYPE=NUMERICAL,LVALUES=3,RVALUES=3 +NODE=*RRLLRRL,PRED=2 +NODE=*RRLLRRR,SPLITTER=C:month,SPLITTERTYPE=CATEGORICAL,LVALUES="sep" +NODE=*RRLLRRRL,PRED=7 +NODE=*RRLLRRRR,PRED=8 +NODE=*RRLR,SPLITTER=N:FFMC,SPLITTERTYPE=NUMERICAL,LVALUES=93.65,RVALUES=93.65 +NODE=*RRLRL,SPLITTER=N:wind,SPLITTERTYPE=NUMERICAL,LVALUES=5.15,RVALUES=5.15 +NODE=*RRLRLL,SPLITTER=N:FFMC,SPLITTERTYPE=NUMERICAL,LVALUES=92.05,RVALUES=92.05 +NODE=*RRLRLLL,SPLITTER=N:area,SPLITTERTYPE=NUMERICAL,LVALUES=1.09,RVALUES=1.09 +NODE=*RRLRLLLL,PRED=7 +NODE=*RRLRLLLR,PRED=8 +NODE=*RRLRLLR,SPLITTER=N:Y,SPLITTERTYPE=NUMERICAL,LVALUES=4.5,RVALUES=4.5 +NODE=*RRLRLLRL,PRED=5 +NODE=*RRLRLLRR,PRED=6 +NODE=*RRLRLR,SPLITTER=N:ISI,SPLITTERTYPE=NUMERICAL,LVALUES=7.4,RVALUES=7.4 +NODE=*RRLRLRL,SPLITTER=N:DMC,SPLITTERTYPE=NUMERICAL,LVALUES=221.60000000000002,RVALUES=221.60000000000002 +NODE=*RRLRLRLL,SPLITTER=N:Y,SPLITTERTYPE=NUMERICAL,LVALUES=3,RVALUES=3 +NODE=*RRLRLRLLL,PRED=1 +NODE=*RRLRLRLLR,SPLITTER=N:area,SPLITTERTYPE=NUMERICAL,LVALUES=1.3,RVALUES=1.3 +NODE=*RRLRLRLLRL,PRED=6 +NODE=*RRLRLRLLRR,PRED=5 +NODE=*RRLRLRLR,PRED=4 +NODE=*RRLRLRR,SPLITTER=N:ISI,SPLITTERTYPE=NUMERICAL,LVALUES=8.1,RVALUES=8.1 +NODE=*RRLRLRRL,PRED=7 +NODE=*RRLRLRRR,PRED=4 +NODE=*RRLRR,SPLITTER=N:RH,SPLITTERTYPE=NUMERICAL,LVALUES=64.5,RVALUES=64.5 +NODE=*RRLRRL,PRED=2 +NODE=*RRLRRR,SPLITTER=N:Y,SPLITTERTYPE=NUMERICAL,LVALUES=4.5,RVALUES=4.5 +NODE=*RRLRRRL,PRED=4 +NODE=*RRLRRRR,SPLITTER=N:Y,SPLITTERTYPE=NUMERICAL,LVALUES=5.5,RVALUES=5.5 +NODE=*RRLRRRRL,PRED=7 +NODE=*RRLRRRRR,PRED=8 +NODE=*RRR,SPLITTER=N:DC,SPLITTERTYPE=NUMERICAL,LVALUES=654.05,RVALUES=654.05 +NODE=*RRRL,SPLITTER=N:DC,SPLITTERTYPE=NUMERICAL,LVALUES=578.6,RVALUES=578.6 +NODE=*RRRLL,SPLITTER=N:temp,SPLITTERTYPE=NUMERICAL,LVALUES=23.049999999999997,RVALUES=23.049999999999997 +NODE=*RRRLLL,PRED=2 +NODE=*RRRLLR,SPLITTER=C:month,SPLITTERTYPE=CATEGORICAL,LVALUES="aug" +NODE=*RRRLLRL,PRED=6 +NODE=*RRRLLRR,PRED=9 +NODE=*RRRLR,SPLITTER=N:RH,SPLITTERTYPE=NUMERICAL,LVALUES=58,RVALUES=58 +NODE=*RRRLRL,SPLITTER=N:FFMC,SPLITTERTYPE=NUMERICAL,LVALUES=91.9,RVALUES=91.9 +NODE=*RRRLRLL,SPLITTER=N:ISI,SPLITTERTYPE=NUMERICAL,LVALUES=7.699999999999999,RVALUES=7.699999999999999 +NODE=*RRRLRLLL,SPLITTER=N:area,SPLITTERTYPE=NUMERICAL,LVALUES=3.125,RVALUES=3.125 +NODE=*RRRLRLLLL,PRED=2 +NODE=*RRRLRLLLR,PRED=3 +NODE=*RRRLRLLR,PRED=3.3333333333333335 +NODE=*RRRLRLR,SPLITTER=N:DC,SPLITTERTYPE=NUMERICAL,LVALUES=590.6500000000001,RVALUES=590.6500000000001 +NODE=*RRRLRLRL,PRED=2 +NODE=*RRRLRLRR,PRED=1.6666666666666667 +NODE=*RRRLRR,SPLITTER=C:day,SPLITTERTYPE=CATEGORICAL,LVALUES="fri:sun" +NODE=*RRRLRRL,SPLITTER=N:DC,SPLITTERTYPE=NUMERICAL,LVALUES=619.9,RVALUES=619.9 +NODE=*RRRLRRLL,PRED=2 +NODE=*RRRLRRLR,PRED=1 +NODE=*RRRLRRR,PRED=7.5 +NODE=*RRRR,SPLITTER=N:DC,SPLITTERTYPE=NUMERICAL,LVALUES=756.05,RVALUES=756.05 +NODE=*RRRRL,SPLITTER=N:RH,SPLITTERTYPE=NUMERICAL,LVALUES=55.5,RVALUES=55.5 +NODE=*RRRRLL,SPLITTER=N:wind,SPLITTERTYPE=NUMERICAL,LVALUES=5.15,RVALUES=5.15 +NODE=*RRRRLLL,SPLITTER=N:temp,SPLITTERTYPE=NUMERICAL,LVALUES=24.35,RVALUES=24.35 +NODE=*RRRRLLLL,SPLITTER=N:ISI,SPLITTERTYPE=NUMERICAL,LVALUES=8.25,RVALUES=8.25 +NODE=*RRRRLLLLL,PRED=4 +NODE=*RRRRLLLLR,PRED=2 +NODE=*RRRRLLLR,PRED=6.666666666666667 +NODE=*RRRRLLR,SPLITTER=N:RH,SPLITTERTYPE=NUMERICAL,LVALUES=37,RVALUES=37 +NODE=*RRRRLLRL,PRED=1 +NODE=*RRRRLLRR,PRED=2 +NODE=*RRRRLR,SPLITTER=N:RH,SPLITTERTYPE=NUMERICAL,LVALUES=58.5,RVALUES=58.5 +NODE=*RRRRLRL,PRED=8 +NODE=*RRRRLRR,PRED=5 +NODE=*RRRRR,SPLITTER=N:temp,SPLITTERTYPE=NUMERICAL,LVALUES=16.9,RVALUES=16.9 +NODE=*RRRRRL,SPLITTER=N:wind,SPLITTERTYPE=NUMERICAL,LVALUES=5.8,RVALUES=5.8 +NODE=*RRRRRLL,PRED=1 +NODE=*RRRRRLR,PRED=7 +NODE=*RRRRRR,SPLITTER=N:wind,SPLITTERTYPE=NUMERICAL,LVALUES=4.25,RVALUES=4.25 +NODE=*RRRRRRL,PRED=2.5 +NODE=*RRRRRRR,SPLITTER=N:ISI,SPLITTERTYPE=NUMERICAL,LVALUES=7.6,RVALUES=7.6 +NODE=*RRRRRRRL,SPLITTER=N:RH,SPLITTERTYPE=NUMERICAL,LVALUES=31,RVALUES=31 +NODE=*RRRRRRRLL,PRED=5 +NODE=*RRRRRRRLR,PRED=6 +NODE=*RRRRRRRR,PRED=8 diff --git a/forest.go b/forest.go index fba6182..18d50c4 100644 --- a/forest.go +++ b/forest.go @@ -10,15 +10,46 @@ import ( ) var ( + nan = math.NaN() mTryRegression = func(x int) int { return int(math.Ceil(float64(x) / 3.0)) } mTryClassification = func(x int) int { return int(math.Ceil(math.Sqrt(float64(x)))) } ) +type ForestType int + +const ( + Classifier ForestType = iota + Regressor +) + +func (ftype ForestType) String() string { + switch ftype { + case Classifier: + return "Classifier" + case Regressor: + return "Regressor" + } + panic(fmt.Sprintf("ForestType.String() error: unknown ftype %d", ftype)) +} + +func StringToForestType(s string) ForestType { + switch s { + case "Classifier": + return Classifier + case "Regressor": + return Regressor + } + panic(fmt.Sprintf("StringToForestType error: unknown ForestType: '%s'", s)) +} + //Forest represents a collection of decision trees grown to predict the Target type Forest struct { Target string Trees []*Tree Intercept float64 + + Type ForestType + PredCfg *PredictConfig } func (f *Forest) Copy() *Forest { @@ -35,6 +66,7 @@ func (f *Forest) Copy() *Forest { Target: f.Target, Intercept: f.Intercept, Trees: trees, + Type: f.Type, } } @@ -211,7 +243,7 @@ func (f *Forest) PredictAll(fm *FeatureMatrix) [][]float64 { if val, err := strconv.ParseFloat(n.Pred, 64); err == nil { predictions[j][i] = val } else { - predictions[j][i] = math.NaN() + predictions[j][i] = nan } } } diff --git a/forestreader.go b/forestreader.go index a28025a..392f8a5 100644 --- a/forestreader.go +++ b/forestreader.go @@ -2,6 +2,7 @@ package CloudForest import ( "bufio" + "encoding/json" "errors" "fmt" "io" @@ -87,6 +88,7 @@ func (fr *ForestReader) ReadTree() (tree *Tree, forest *Forest, err error) { return } parsed := fr.ParseRfAcePredictorLine(line) + switch { case strings.HasPrefix(line, "FOREST"): forest = new(Forest) @@ -99,6 +101,16 @@ func (fr *ForestReader) ReadTree() (tree *Tree, forest *Forest, err error) { } forest.Intercept = intercept } + predCfgJSON, hasPredCfg := parsed["PREDCONFIG"] + if hasPredCfg { + predCfg := &PredictConfig{} + err := json.Unmarshal([]byte(predCfgJSON), predCfg) + if err != nil { + log.Print("Error parsing forest PREDCONFIG value ", err) + } + forest.PredCfg = predCfg + forest.Type = StringToForestType(predCfg.Type) + } case strings.HasPrefix(line, "TREE"): intree = true diff --git a/forestwriter.go b/forestwriter.go index 8009459..9be99c0 100644 --- a/forestwriter.go +++ b/forestwriter.go @@ -1,6 +1,7 @@ package CloudForest import ( + "encoding/json" "fmt" "io" "strings" @@ -24,9 +25,7 @@ func NewForestWriter(w io.Writer) *ForestWriter { //WriteForest writes an entire forest including all headers. func (fw *ForestWriter) WriteForest(forest *Forest) { - if forest.Intercept != 0.0 { - fw.WriteForestHeader(0, forest.Target, forest.Intercept) - } + fw.WriteForestHeader(0, forest.Target, forest.Intercept, forest.PredCfg) for i, tree := range forest.Trees { fw.WriteTree(tree, i) } @@ -48,12 +47,29 @@ func (fw *ForestWriter) WriteTreeHeader(ntree int, target string, weight float64 } //WrieTreeHeader writes only the header line for a tree. -func (fw *ForestWriter) WriteForestHeader(nforest int, target string, intercept float64) { +func (fw *ForestWriter) WriteForestHeader( + nforest int, + target string, + intercept float64, + predCfg *PredictConfig, +) { interceptterm := "" if intercept != 0.0 { interceptterm = fmt.Sprintf(",INTERCEPT=%v", intercept) } - fmt.Fprintf(fw.w, "FOREST=%v,TARGET=\"%v\"%v\n", nforest, target, interceptterm) + var cfgJson string + if predCfg != nil { + by, err := json.Marshal(predCfg) + if err != nil { + panic(err) + } + cfgJson = fmt.Sprintf(`,PREDCONFIG=%s`, string(by)) + } + fmt.Fprintf(fw.w, "FOREST=%v,TARGET=\"%v\"%v%v\n", + nforest, + target, + interceptterm, + cfgJson) } //WriteNodeAndChildren recursively writes out the target node and all of its children. diff --git a/growforest/growforest.go b/growforest/growforest.go index ad9a942..e9a2f69 100644 --- a/growforest/growforest.go +++ b/growforest/growforest.go @@ -21,6 +21,7 @@ import ( ) func main() { + fm := flag.String("train", "featurematrix.afm", "AFM formated feature matrix containing training data.") rf := flag.String("rfpred", @@ -185,6 +186,9 @@ func main() { var noseed bool flag.BoolVar(&noseed, "noseed", false, "Don't seed the random number generator from time.") + var expit bool + flag.BoolVar(&expit, "expit", false, "Expit (inverse logit) transform data (for gradient boosting classification).") + flag.Parse() nForest := 1 @@ -391,6 +395,7 @@ func main() { if caseoob != "" { oob = true } + var ftype CloudForest.ForestType var oobVotes CloudForest.VoteTallyer if oob { fmt.Println("Recording oob error.") @@ -413,6 +418,7 @@ func main() { switch targetf.(type) { case CloudForest.NumFeature: + ftype = CloudForest.Regressor fmt.Println("Performing regression.") if l1 { fmt.Println("Using l1/absolute deviance error.") @@ -434,6 +440,7 @@ func main() { target = targetf case CloudForest.CatFeature: + ftype = CloudForest.Classifier fmt.Printf("Performing classification with %v categories.\n", targetf.NCats()) switch { case NP: @@ -514,6 +521,24 @@ func main() { } } + // things that applyforest will need to know. + predCfg := &CloudForest.PredictConfig{ + Type: ftype.String(), + Targetname: *targetname, + Costs: *costs, + Dentropy: *dentropy, + Adacosts: *adacosts, + Rfweights: *rfweights, + Blacklist: *blacklist, + L1: l1, + Density: density, + Positive: positive, + Entropy: entropy, + Adaboost: adaboost, + Gradboost: gradboost, + Ordinal: ordinal, + } + var forestwriter *CloudForest.ForestWriter if *rf != "" { forestfile, err := os.Create(*rf) @@ -522,10 +547,12 @@ func main() { } defer forestfile.Close() forestwriter = CloudForest.NewForestWriter(forestfile) + var icept float64 switch target.(type) { case CloudForest.TargetWithIntercept: - forestwriter.WriteForestHeader(0, *targetname, target.(CloudForest.TargetWithIntercept).Intercept()) + icept = target.(CloudForest.TargetWithIntercept).Intercept() } + forestwriter.WriteForestHeader(0, *targetname, icept, predCfg) } //****************** Setup For ACE ********************************// var aceImps [][]float64 diff --git a/pred_test.go b/pred_test.go new file mode 100644 index 0000000..d441e03 --- /dev/null +++ b/pred_test.go @@ -0,0 +1,56 @@ +package CloudForest + +import ( + "fmt" + "log" + "os" + "testing" +) + +// Check the Predict() functionality, comparing +// to output from earlier applyforest code. We +// use a model from an already serialized +// test forest, saved in the data/ dir. +func TestPredict(t *testing.T) { + + model := "data/pred_test_forestfire_model.sf" + fd, err := os.Open(model) + if err != nil { + t.Fatal(err) + } + defer fd.Close() + forestreader := NewForestReader(fd) + forest, err := forestreader.ReadForest() + if err != nil { + t.Fatal(err) + } + + // data to predict + forestFireDataFn := "data/forestfires.trans.fm" + data, err := LoadAFM(forestFireDataFn) + if err != nil { + log.Fatal(err) + } + + // expected predictions + expectedFilename := "data/pred_test_expected_predictions.tsv" + expected, err := ReadTabSeparatedPredictions(expectedFilename) + if err != nil { + t.Fatal(err) + } + + preds, err := Predict(data, forest) + if err != nil { + t.Fatal(err) + } + if !preds.IsReal { + t.Fatalf("expected Real predictions from forest fire regression") + } + N := data.Data[0].Length() + for i := 0; i < N; i++ { + if !closeEnough(preds.Real[i], expected.Prediction[i]) { + t.Fatalf("i=%v was off: preds.Real[i]=%v was not close to expected.Prediction[i]=%v", i, preds.Real[i], expected.Prediction[i]) + } + } + fmt.Printf("all %v rows matched expected\n", N) +} diff --git a/predcfg.go b/predcfg.go new file mode 100644 index 0000000..fe9987d --- /dev/null +++ b/predcfg.go @@ -0,0 +1,53 @@ +package CloudForest + +// PredictConfig holds options that are set +// during growforest and that are needed during applyforest +// to correctly predict from the forest. +// +type PredictConfig struct { + + // Type is "Classifier" or "Regressor" + Type string + + // Targetname gives the row header of the target in the feature matrix. + Targetname string + + // Costs is for categorical targets: a json string to float map of the cost of falsely identifying each category. + Costs string + + // Dentropy gives the Class disutilities for disutility entropy. + Dentropy string + + // Adacosts gives the Json costs for cost sentive AdaBoost. + Adacosts string + + // Rfweights gives, for categorical targets, a json string to float map of the weights to use for each category in Weighted RF. + Rfweights string + + // Blacklist is a list of feature id's to exclude from the set of predictors. + Blacklist string + + // L1 means use l1 norm regression (target must be numeric) + L1 bool + + // Density means build density estimating trees instead of classification/regression trees + Density bool + + // Positive class to output probabilities for. + Positive string + + // Entropy means use entropy minimizing classification (target must be categorical). + Entropy bool + + // Adaptive boosting for regression/classification. + Adaboost bool + + // Gradboost means do gradient boosting with the specified learning rate. + Gradboost float64 + + // Ordinal means use ordinal regression (target must be numeric). + Ordinal bool + + // Expit means transform with expit (inverse logit): for gradient boosting classification. + Expit bool +} diff --git a/predict.go b/predict.go new file mode 100644 index 0000000..39afb99 --- /dev/null +++ b/predict.go @@ -0,0 +1,131 @@ +package CloudForest + +import ( + "bufio" + "fmt" + "os" + "strconv" + "strings" +) + +// Predictions is returned by Predict(). +// Only one of Real or Cat will be populated. +type Predictions struct { + // Real will be populated for regression predictions. + Real []float64 + + // Cat (category) will be populated for classifiers. + Cat []string + + // IsReal will be true when Real is populated. Otherwise + // Cat (above) is in use. + IsReal bool +} + +// helper for Predict() +func getTallyerForForest(fm *FeatureMatrix, f *Forest) VoteTallyer { + n := fm.Data[fm.Map[f.Target]].Length() + + if f.PredCfg.Gradboost != 0 || f.PredCfg.Adaboost { + return NewSumBallotBox(n) + } + + switch f.Type { + case Regressor: + return NewNumBallotBox(n) + case Classifier: + return NewCatBallotBox(n) + } + panic(fmt.Sprintf("which BallotBox to use for ForestType %v ?", f.Type)) +} + +// Predict is ForestType agnostic, generating preditions +// with whichever ForestType (Classifier, Regressor, GBM) +// is provided in f. Argument fm must have features comparable to +// the FeatureMatrix that f was trained on, but fm may, and +// frequently will, contain previously unseen data. +// +func Predict(fm *FeatureMatrix, f *Forest) (*Predictions, error) { + tallyer := getTallyerForForest(fm, f) + for _, tree := range f.Trees { + tree.Vote(fm, tallyer) + } + + N := fm.Data[0].Length() + + switch box := tallyer.(type) { + case *CatBallotBox: + var preds []string + for i := 0; i < N; i++ { + preds = append(preds, box.Tally(i)) + } + return &Predictions{Cat: preds}, nil + + case *NumBallotBox: + var preds []float64 + for i := 0; i < N; i++ { + pred := box.TallyNum(i) + f.Intercept + preds = append(preds, pred) + } + return &Predictions{Real: preds, IsReal: true}, nil + + case *SumBallotBox: + var preds []float64 + for i := 0; i < N; i++ { + pred := box.TallyNum(i) + f.Intercept + if f.PredCfg.Expit { + pred = Expit(pred) + } + preds = append(preds, pred) + } + return &Predictions{Real: preds, IsReal: true}, nil + default: + return nil, fmt.Errorf("Unknown tallyer type %T in Predict()", tallyer) + } +} + +// read back predictions written to file +// by applyforest + +type RealPredFile struct { + Rowname []string + Prediction []float64 + Actual []float64 +} + +func ReadTabSeparatedPredictions(fn string) (rpf *RealPredFile, err error) { + + var f *os.File + f, err = os.Open(fn) + if err != nil { + return + } + defer f.Close() + scanner := bufio.NewScanner(f) + + rpf = &RealPredFile{} + + for scanner.Scan() { + line := scanner.Text() + fld := strings.Split(line, "\t") + for j := range fld { + fld[j] = strings.TrimSpace(fld[j]) + } + + rpf.Rowname = append(rpf.Rowname, fld[0]) + + f64, err := strconv.ParseFloat(fld[1], 64) + if err != nil { + f64 = nan + } + rpf.Prediction = append(rpf.Prediction, f64) + + f64, err = strconv.ParseFloat(fld[2], 64) + if err != nil { + f64 = nan + } + rpf.Actual = append(rpf.Actual, f64) + } + err = scanner.Err() + return +} diff --git a/serz_test.go b/serz_test.go new file mode 100644 index 0000000..561962a --- /dev/null +++ b/serz_test.go @@ -0,0 +1,55 @@ +package CloudForest + +import ( + "bytes" + "math" + "testing" +) + +// Spot check serialization of strings, float64, and bool +// fields in the forest header. +func TestForestSerializationAndBack(t *testing.T) { + ftype := Regressor + targetName := "myTarget" + l1 := true + gradboost := 5.2 + predCfg := &PredictConfig{ + Type: ftype.String(), + Targetname: targetName, + L1: l1, + Gradboost: gradboost, + } + + // write + var buf bytes.Buffer + forestwriter := NewForestWriter(&buf) + icept := 0.5 + forestwriter.WriteForestHeader(0, targetName, icept, predCfg) + + // read back + forestreader := NewForestReader(&buf) + forest, err := forestreader.ReadForest() + if err != nil { + t.Fatal(err) + } + + // check + if !closeEnough(forest.PredCfg.Gradboost, predCfg.Gradboost) { + t.Errorf("Gradboost did not serialize and restore") + } + if !closeEnough(forest.Intercept, icept) { + t.Errorf("Intercept did not serialize and restore") + } + if forest.PredCfg.L1 != predCfg.L1 { + t.Errorf("L1 did not serialization and restore") + } + if forest.Type != ftype { + t.Errorf("forest.Type did not serialization and restore") + } +} + +const eps = 1e-6 + +func closeEnough(a, b float64) bool { + return math.Abs(a-b) < eps +} diff --git a/voter.go b/voter.go index d9ce491..334ad06 100644 --- a/voter.go +++ b/voter.go @@ -1,11 +1,20 @@ package CloudForest -//VoteTallyer is used to tabulate votes by trees and is implemented by feature type specific -//structs like NumBallotBox and CatBallotBox. -//Vote should register a cote that casei should be predicted as pred. -//TallyError returns the error vs the supplied feature. +// VoteTallyer is used to tabulate votes by +// trees and is implemented by feature type specific +// structs like NumBallotBox and CatBallotBox. type VoteTallyer interface { + + // Vote should register a vote that casei should be predicted as pred. Vote(casei int, pred string, weight float64) + + // TallyError returns the error vs the supplied feature. TallyError(feature Feature) float64 + + // Tally returns the predicted category string for casei. Tally(casei int) string + + // TallyNum returns the predicted regression for case i. + // Classifiers should just return NaN. + TallyNum(i int) (predicted float64) }