From 1a202963a82b7195b9253887847267f1f2de3753 Mon Sep 17 00:00:00 2001 From: Martin Bruse Date: Tue, 18 Jun 2024 15:30:40 +0000 Subject: [PATCH] Added MOS mapping optimization - Made it possible to optimize the MOS mapping for a given dataset with already-calculated Zimtohrli scores. - Made it possible to print the MOS MSE for a given dataset. --- go.mod | 3 + go.sum | 6 + go/bin/score/score.go | 42 +++- go/data/study.go | 120 ++++++++- python/mos_mapping.ipynb | 526 ++++++++++++++++++++++++++++++++++++--- 5 files changed, 643 insertions(+), 54 deletions(-) diff --git a/go.mod b/go.mod index 630092f..5bdb913 100644 --- a/go.mod +++ b/go.mod @@ -6,10 +6,13 @@ require ( github.com/PuerkitoBio/goquery v1.9.1 github.com/dgryski/go-onlinestats v0.0.0-20170612111826-1c7d19468768 github.com/mattn/go-sqlite3 v1.14.22 + gonum.org/v1/gonum v0.15.0 ) require ( github.com/aclements/go-moremath v0.0.0-20210112150236-f10218a38794 // indirect github.com/andybalholm/cascadia v1.3.2 // indirect + golang.org/x/exp v0.0.0-20231110203233-9a3e6036ecaa // indirect golang.org/x/net v0.23.0 // indirect + golang.org/x/tools v0.15.0 // indirect ) diff --git a/go.sum b/go.sum index a102665..9a988a0 100644 --- a/go.sum +++ b/go.sum @@ -11,6 +11,8 @@ github.com/mattn/go-sqlite3 v1.14.22/go.mod h1:Uh1q+B4BYcTPb+yiD3kU8Ct7aC0hY9fxU github.com/yuin/goldmark v1.4.13/go.mod h1:6yULJ656Px+3vBD8DxQVa3kxgyrAnzto9xy5taEt/CY= golang.org/x/crypto v0.0.0-20190308221718-c2843e01d9a2/go.mod h1:djNgcEr1/C05ACkg1iLfiJU5Ep61QUkGW8qpdssI0+w= golang.org/x/crypto v0.0.0-20210921155107-089bfa567519/go.mod h1:GvvjBRRGRdwPK5ydBHafDWAxML/pGHZbMvKqRZ5+Abc= +golang.org/x/exp v0.0.0-20231110203233-9a3e6036ecaa h1:FRnLl4eNAQl8hwxVVC17teOw8kdjVDVAiFMtgUdTSRQ= +golang.org/x/exp v0.0.0-20231110203233-9a3e6036ecaa/go.mod h1:zk2irFbV9DP96SEBUUAy67IdHUaZuSnrz1n472HUCLE= golang.org/x/mod v0.6.0-dev.0.20220419223038-86c51ed26bb4/go.mod h1:jJ57K6gSWd91VN4djpZkiMVwK6gcyfeH4XE8wZrZaV4= golang.org/x/mod v0.8.0/go.mod h1:iBbtSCu2XBx23ZKBPSOrRkjjQPZFPuis4dIYUhu/chs= golang.org/x/net v0.0.0-20190620200207-3b0461eec859/go.mod h1:z5CRVTTTmAJ677TzLLGU+0bjPO0LkuOLi4/5GtJWs/s= @@ -43,4 +45,8 @@ golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGm golang.org/x/tools v0.0.0-20191119224855-298f0cb1881e/go.mod h1:b+2E5dAYhXwXZwtnZ6UAqBI28+e2cm9otk0dWdXHAEo= golang.org/x/tools v0.1.12/go.mod h1:hNGJHUnrk76NpqgfD5Aqm5Crs+Hm0VOH/i9J2+nxYbc= golang.org/x/tools v0.6.0/go.mod h1:Xwgl3UAJ/d3gWutnCtw505GrjyAbvKui8lOU390QaIU= +golang.org/x/tools v0.15.0 h1:zdAyfUGbYmuVokhzVmghFl2ZJh5QhcfebBgmVPFYA+8= +golang.org/x/tools v0.15.0/go.mod h1:hpksKq4dtpQWS1uQ61JkdqWM3LscIS6Slf+VVkm+wQk= golang.org/x/xerrors v0.0.0-20190717185122-a985d3407aa7/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0= +gonum.org/v1/gonum v0.15.0 h1:2lYxjRbTYyxkJxlhC+LvJIx3SsANPdRybu1tGj9/OrQ= +gonum.org/v1/gonum v0.15.0/go.mod h1:xzZVBJBtS+Mz4q0Yl2LJTk+OxOg4jiXZ7qBoM0uISGo= diff --git a/go/bin/score/score.go b/go/bin/score/score.go index 300ecb2..65d7381 100644 --- a/go/bin/score/score.go +++ b/go/bin/score/score.go @@ -54,6 +54,7 @@ func main() { leaderboard := flag.String("leaderboard", "", "Glob to directories with databases to compute leaderboard for.") report := flag.String("report", "", "Glob to directories with databases to generate a report for.") accuracy := flag.String("accuracy", "", "Glob to directories with databases to provide JND accuracy for.") + mos_mse := flag.String("mos_mse", "", "Glob to directories with databases to provide Zimtohrli-MOS to regular-MOS MSE for.") optimize := flag.String("optimize", "", "Glob to directories with databases to optimize for.") optimizeLogfile := flag.String("optimize_logfile", "", "File to write optimization events to.") optimizeStartStep := flag.Float64("optimize_start_step", 1, "Start step for the simulated annealing.") @@ -63,7 +64,7 @@ func main() { optimizeMapping := flag.String("optimize_mapping", "", "Glob to directories with databases to optimize the MOS mapping for.") flag.Parse() - if *details == "" && *calculate == "" && *correlate == "" && *accuracy == "" && *leaderboard == "" && *report == "" && *optimize == "" && *optimizeMapping == "" { + if *details == "" && *calculate == "" && *correlate == "" && *accuracy == "" && *leaderboard == "" && *report == "" && *optimize == "" && *optimizeMapping == "" && *mos_mse == "" { flag.Usage() os.Exit(1) } @@ -99,11 +100,20 @@ func main() { if err != nil { log.Fatal(err) } - params, err := bundles.OptimizeMapping() + result, err := bundles.OptimizeMapping() if err != nil { log.Fatal(err) } - fmt.Println(params) + fmt.Printf("%+v\n", result) + } + + makeZimtohrli := func() *goohrli.Goohrli { + if !reflect.DeepEqual(zimtohrliParameters, goohrli.DefaultParameters(zimtohrliParameters.SampleRate)) { + log.Printf("Using %+v", zimtohrliParameters) + } + zimtohrliParameters.SampleRate = sampleRate + z := goohrli.New(zimtohrliParameters) + return z } if *calculate != "" { @@ -115,11 +125,7 @@ func main() { for _, study := range studies { measurements := map[data.ScoreType]data.Measurement{} if *calculateZimtohrli { - if !reflect.DeepEqual(zimtohrliParameters, goohrli.DefaultParameters(zimtohrliParameters.SampleRate)) { - log.Printf("Using %+v", zimtohrliParameters) - } - zimtohrliParameters.SampleRate = sampleRate - z := goohrli.New(zimtohrliParameters) + z := makeZimtohrli() measurements[data.ScoreType(*zimtohrliScoreType)] = z.NormalizedAudioDistance } if *calculateViSQOL { @@ -203,6 +209,26 @@ func main() { } } + if *mos_mse != "" { + bundles, err := data.OpenBundles(*mos_mse) + if err != nil { + log.Fatal(err) + } + for _, bundle := range bundles { + if bundle.IsJND() { + fmt.Printf("Not computing MOS MSE for JND dataset %q\n\n", bundle.Dir) + } else { + z := makeZimtohrli() + mse, err := bundle.ZimtohrliMOSMSE(z) + if err != nil { + log.Fatal(err) + } + fmt.Printf("## %v\n", bundle.Dir) + fmt.Printf("MSE between human MOS and Zimtohrli MOS: %.15f\n", mse) + } + } + } + if *report != "" { bundles, err := data.OpenBundles(*report) if err != nil { diff --git a/go/data/study.go b/go/data/study.go index fbf8a91..9fd92a4 100644 --- a/go/data/study.go +++ b/go/data/study.go @@ -38,6 +38,7 @@ import ( "github.com/google/zimtohrli/go/goohrli" "github.com/google/zimtohrli/go/progress" "github.com/google/zimtohrli/go/worker" + "gonum.org/v1/gonum/optimize" _ "github.com/mattn/go-sqlite3" // To open sqlite3-databases. ) @@ -95,9 +96,10 @@ type Study struct { // ReferenceBundle is a plain data type containing a bunch of references, typicall the content of a study. type ReferenceBundle struct { - Dir string - References []*Reference - ScoreTypes map[ScoreType]int + Dir string + References []*Reference + ScoreTypes map[ScoreType]int + ScoreTypeLimits map[ScoreType][2]*float64 } // ReferenceBundles is a slice of ReferenceBundle. @@ -122,8 +124,20 @@ func (r *ReferenceBundle) SortedTypes() ScoreTypes { // Add adds a reference to a bundle. func (r *ReferenceBundle) Add(ref *Reference) { for _, dist := range ref.Distortions { - for scoreType := range dist.Scores { + for scoreType, value := range dist.Scores { r.ScoreTypes[scoreType]++ + if r.ScoreTypeLimits[scoreType][0] == nil || *r.ScoreTypeLimits[scoreType][0] > value { + valueCopy := value + limits := r.ScoreTypeLimits[scoreType] + limits[0] = &valueCopy + r.ScoreTypeLimits[scoreType] = limits + } + if r.ScoreTypeLimits[scoreType][1] == nil || *r.ScoreTypeLimits[scoreType][1] < value { + valueCopy := value + limits := r.ScoreTypeLimits[scoreType] + limits[1] = &valueCopy + r.ScoreTypeLimits[scoreType] = limits + } } } r.References = append(r.References, ref) @@ -132,8 +146,9 @@ func (r *ReferenceBundle) Add(ref *Reference) { // ToBundle returns a reference bundle for this study. func (s *Study) ToBundle() (*ReferenceBundle, error) { result := &ReferenceBundle{ - Dir: s.dir, - ScoreTypes: map[ScoreType]int{}, + Dir: s.dir, + ScoreTypes: map[ScoreType]int{}, + ScoreTypeLimits: map[ScoreType][2]*float64{}, } if err := s.ViewEachReference(func(ref *Reference) error { result.Add(ref) @@ -384,6 +399,44 @@ func (r *ReferenceBundle) JNDAccuracy() (JNDAccuracyScores, error) { return result, nil } +// MOSMSE returns the precision when predicting the MOS score. +func (r *ReferenceBundle) ZimtohrliMOSMSE(z *goohrli.Goohrli) (float64, error) { + if r.IsJND() { + return 0, fmt.Errorf("cannot compute MOS precision on JND references") + } + if _, found := r.ScoreTypes[MOS]; !found { + return 0, fmt.Errorf("cannot compute MOS precision on a data set without MOS") + } + + var mosScaler func(mos float64) float64 + if math.Abs(*r.ScoreTypeLimits[MOS][0]-1) < 0.2 && math.Abs(*r.ScoreTypeLimits[MOS][1]-5) < 0.2 { + mosScaler = func(mos float64) float64 { + return mos + } + } else if math.Abs(*r.ScoreTypeLimits[MOS][0]) < 0.2 && math.Abs(*r.ScoreTypeLimits[MOS][1]-100) < 0.2 { + mosScaler = func(mos float64) float64 { + return 1 + 0.04*mos + } + } else { + return 0, fmt.Errorf("minimum MOS %v and maximum MOS %v are confusing", *r.ScoreTypeLimits[MOS][0], *r.ScoreTypeLimits[MOS][1]) + } + + sumOfSquares := 0.0 + count := 0 + for _, ref := range r.References { + for _, dist := range ref.Distortions { + mos, found := dist.Scores[MOS] + if !found { + return 0, fmt.Errorf("%+v doesn't have a MOS score", ref) + } + delta := mosScaler(mos) - z.MOSFromZimtohrli(dist.Scores[Zimtohrli]) + sumOfSquares += delta * delta + count++ + } + } + return sumOfSquares / float64(count), nil +} + // Studies is a slice of studies. type Studies []*Study @@ -516,8 +569,59 @@ func (r ReferenceBundles) Split(rng *rand.Rand, split float64) (ReferenceBundles return left, right } -func (r ReferenceBundles) OptimizeMapping() ([]float32, error) { - return nil, nil +type MappingOptimizationResult struct { + ParamsBefore []float64 + MSEBefore float64 + ParamsAfter []float64 + MSEAfter float64 +} + +func (r ReferenceBundles) OptimizeMapping() (*MappingOptimizationResult, error) { + z := goohrli.New(goohrli.DefaultParameters(48000)) + errors := []error{} + p := optimize.Problem{ + Func: func(x []float64) float64 { + params := z.Parameters() + for index := range params.MOSMapperParams { + params.MOSMapperParams[index] = math.Abs(x[index]) + } + z.Set(params) + sum := 0.0 + count := 0 + for _, bundle := range r { + if !bundle.IsJND() { + mse, err := bundle.ZimtohrliMOSMSE(z) + if err != nil { + errors = append(errors, err) + } + sum += mse + count += 1 + } + } + return sum / float64(count) + }, + Status: func() (optimize.Status, error) { + if len(errors) > 0 { + return optimize.Failure, fmt.Errorf("%+v", errors) + } + return optimize.NotTerminated, nil + }, + } + startParams := z.Parameters().MOSMapperParams + result := &MappingOptimizationResult{ + ParamsBefore: startParams[:], + MSEBefore: p.Func(startParams[:]), + } + optResult, err := optimize.Minimize(p, startParams[:], nil, nil) + if err != nil { + return nil, err + } + if err := optResult.Status.Err(); err != nil { + return nil, err + } + result.ParamsAfter = optResult.X + result.MSEAfter = optResult.F + return result, nil } // OptimizationEvent is a step in the optimization process. diff --git a/python/mos_mapping.ipynb b/python/mos_mapping.ipynb index da00db3..9ab15fe 100644 --- a/python/mos_mapping.ipynb +++ b/python/mos_mapping.ipynb @@ -36,17 +36,19 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "z_scores.shape=(2990,)\n", - "mos_scores.shape=(2990,)\n", - "mos_extremes=array([5., 1.])\n", - "z_extremes=array([0.94465321, 0. ])\n" + "len(z_scores)=8\n", + "len(mos_scores)=8\n", + "all_z_scores.shape=(13588,)\n", + "all_mos_scores.shape=(13588,)\n", + "mos_extremes=(5.0, 1.0)\n", + "z_extremes=(0.9446699023246765, 0.0)\n" ] } ], @@ -66,57 +68,474 @@ " min_mos = mos\n", " if max_mos is None or mos > max_mos:\n", " max_mos = mos\n", - " if min_mos and max_mos:\n", - " mos_range_reciprocal = 1.0 / (max_mos - min_mos)\n", - " for mos, z in each_pair(references):\n", - " yield (1 + (mos - min_mos) * 4 * mos_range_reciprocal, z)\n", + " if min_mos is not None and max_mos is not None:\n", + " if abs(1 - min_mos) < 0.2 and abs(5 - max_mos) < 0.2:\n", + " for mos, z in each_pair(references):\n", + " yield mos, z\n", + " elif abs(min_mos) < 0.2 and abs(100 - max_mos) < 0.2:\n", + " for mos, z in each_pair(references):\n", + " yield 1 + mos * 0.04, z\n", + " else:\n", + " raise ValueError(f'{min_mos=} {max_mos=}')\n", "\n", "\n", "z_scores = []\n", "mos_scores = []\n", + "all_z_scores = []\n", + "all_mos_scores = []\n", "with open(\"../scores.json\") as json_file:\n", " for data_set in json.load(json_file):\n", + " study_z = []\n", + " study_mos = []\n", " for mos, z in each_normalized_pair(data_set[\"References\"]):\n", - " z_scores.append(z)\n", - " mos_scores.append(mos)\n", + " study_z.append(z)\n", + " study_mos.append(mos)\n", + " all_z_scores.append(z)\n", + " all_mos_scores.append(mos)\n", + " if len(study_z) > 0:\n", + " z_scores.append(np.asarray(study_z))\n", + " mos_scores.append(np.asarray(study_mos))\n", "\n", - "z_scores = np.asarray(z_scores)\n", - "mos_scores = np.asarray(mos_scores)\n", - "mos_extremes = np.asarray([np.max(mos_scores), np.min(mos_scores)])\n", - "z_extremes = np.asarray([np.max(z_scores), np.min(z_scores)])\n", + "all_z_scores = np.asarray(all_z_scores)\n", + "all_mos_scores = np.asarray(all_mos_scores)\n", "\n", - "print(f\"{z_scores.shape=}\")\n", - "print(f\"{mos_scores.shape=}\")\n", + "mos_extremes = max([np.max(s) for s in mos_scores]), min([np.min(s) for s in mos_scores])\n", + "z_extremes = max(np.max(s) for s in z_scores), min(np.min(s) for s in z_scores)\n", + "\n", + "print(f\"{len(z_scores)=}\")\n", + "print(f\"{len(mos_scores)=}\")\n", + "print(f\"{all_z_scores.shape=}\")\n", + "print(f\"{all_mos_scores.shape=}\")\n", "print(f\"{mos_extremes=}\")\n", "print(f\"{z_extremes=}\")\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "params=array([1., 1., 1.]) result=3.434709402324488\n", + "params=array([1.00000001, 1. , 1. ]) result=3.434709402324488\n", + "params=array([1. , 1.00000001, 1. ]) result=3.4347094125550726\n", + "params=array([1. , 1. , 1.00000001]) result=3.4347093778098983\n", + "params=array([1. , 0.6110146 , 1.93208924]) result=1.8939908848446885\n", + "params=array([1.00000001, 0.6110146 , 1.93208924]) result=1.8939908848446878\n", + "params=array([1. , 0.61101461, 1.93208924]) result=1.8939908976347193\n", + "params=array([1. , 0.6110146 , 1.93208926]) result=1.8939908706072108\n", + "params=array([ 1.0000001 , -1.12345646, 3.63128219]) result=1.193248803218663\n", + "params=array([ 1.00000011, -1.12345646, 3.63128219]) result=1.1932488032186632\n", + "params=array([ 1.0000001 , -1.12345645, 3.63128219]) result=1.1932487973174661\n", + "params=array([ 1.0000001 , -1.12345646, 3.6312822 ]) result=1.1932487981486903\n", + "params=array([ 1.0000001 , -0.90849519, 3.89278154]) result=1.0388598245721532\n", + "params=array([ 1.00000012, -0.90849519, 3.89278154]) result=1.0388598245721532\n", + "params=array([ 1.0000001 , -0.90849518, 3.89278154]) result=1.0388598194919634\n", + "params=array([ 1.0000001 , -0.90849519, 3.89278156]) result=1.0388598210821653\n", + "params=array([ 1.00000014, -0.28240301, 4.31298199]) result=0.8243722141104614\n", + "params=array([ 1.00000015, -0.28240301, 4.31298199]) result=0.8243722141104612\n", + "params=array([ 1.00000014, -0.28240299, 4.31298199]) result=0.8243722120539925\n", + "params=array([ 1.00000014, -0.28240301, 4.312982 ]) result=0.8243722137435218\n", + "params=array([1.00000016, 0.10591622, 4.39865415]) result=0.8086392778342267\n", + "params=array([1.00000018, 0.10591622, 4.39865415]) result=0.8086392778342267\n", + "params=array([1.00000016, 0.10591624, 4.39865415]) result=0.8086392783565501\n", + "params=array([1.00000016, 0.10591622, 4.39865417]) result=0.8086392782262002\n", + "params=array([1.00000016, 0.02684716, 4.35582085]) result=0.8059468035381968\n", + "params=array([1.00000017, 0.02684716, 4.35582085]) result=0.8059468035381968\n", + "params=array([1.00000016, 0.02684718, 4.35582085]) result=0.805946803462382\n", + "params=array([1.00000016, 0.02684716, 4.35582087]) result=0.8059468041559786\n", + "params=array([ 1.00000015, -0.05638057, 4.21442093]) result=0.8022680530556958\n", + "params=array([ 1.00000016, -0.05638057, 4.21442093]) result=0.8022680530556958\n", + "params=array([ 1.00000015, -0.05638056, 4.21442093]) result=0.802268052295488\n", + "params=array([ 1.00000015, -0.05638057, 4.21442095]) result=0.8022680533492818\n", + "params=array([ 1.00000015, -0.01472949, 4.19360523]) result=0.8001001682313968\n", + "params=array([ 1.00000016, -0.01472949, 4.19360523]) result=0.8001001682313968\n", + "params=array([ 1.00000015, -0.01472948, 4.19360523]) result=0.8001001678068568\n", + "params=array([ 1.00000015, -0.01472949, 4.19360525]) result=0.8001001686578644\n", + "params=array([1.00000014, 0.04260846, 4.09767338]) result=0.7996542476058521\n", + "params=array([1.00000015, 0.04260846, 4.09767338]) result=0.7996542476058521\n", + "params=array([1.00000014, 0.04260848, 4.09767338]) result=0.7996542487075058\n", + "params=array([1.00000014, 0.04260846, 4.09767339]) result=0.7996542477581412\n", + "params=array([1.00000014, 0.03118503, 4.09217264]) result=0.7987804419199129\n", + "params=array([1.00000015, 0.03118503, 4.09217264]) result=0.798780441919913\n", + "params=array([1.00000014, 0.03118505, 4.09217264]) result=0.7987804429323027\n", + "params=array([1.00000014, 0.03118503, 4.09217266]) result=0.7987804421105288\n", + "params=array([ 1.00000014, -0.01450868, 4.0701697 ]) result=0.7973843997555085\n", + 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result=0.7950279871296405\n", + "params=array([ 1.00000012e+00, -3.27625727e-09, 3.91493943e+00]) result=0.7950279863010958\n", + "params=array([ 1.00000012e+00, -3.27625727e-09, 3.91493942e+00]) result=0.7950279863014735\n", + "params=array([ 1.00000013e+00, -3.27625727e-09, 3.91493942e+00]) result=0.7950279863014735\n", + "params=array([1.00000012e+00, 1.16249039e-08, 3.91493942e+00]) result=0.7950279871296405\n", + "params=array([ 1.00000012e+00, -3.27625727e-09, 3.91493943e+00]) result=0.7950279863010958\n", + "params=array([ 1.00000012e+00, -3.27625727e-09, 3.91493942e+00]) result=0.7950279863014735\n", + "params=array([ 1.00000013e+00, -3.27625727e-09, 3.91493942e+00]) result=0.7950279863014735\n", + "params=array([1.00000012e+00, 1.16249039e-08, 3.91493942e+00]) result=0.7950279871296405\n", + "params=array([ 1.00000012e+00, -3.27625727e-09, 3.91493943e+00]) result=0.7950279863010958\n", + "params=array([ 1.00000012e+00, -3.27625727e-09, 3.91493942e+00]) result=0.7950279863014738\n", + "params=array([ 1.00000013e+00, -3.27625727e-09, 3.91493942e+00]) result=0.7950279863014738\n", + "params=array([1.00000012e+00, 1.16249039e-08, 3.91493942e+00]) result=0.7950279871296404\n", + "params=array([ 1.00000012e+00, -3.27625727e-09, 3.91493943e+00]) result=0.7950279863010958\n", + "params=array([ 1.00000012e+00, -3.27625727e-09, 3.91493942e+00]) result=0.7950279863014735\n", + "params=array([ 1.00000013e+00, -3.27625727e-09, 3.91493942e+00]) result=0.7950279863014735\n", + "params=array([1.00000012e+00, 1.16249039e-08, 3.91493942e+00]) result=0.7950279871296405\n", + "params=array([ 1.00000012e+00, -3.27625727e-09, 3.91493943e+00]) result=0.7950279863010958\n", + "params=array([ 1.00000012e+00, -3.27625727e-09, 3.91493942e+00]) result=0.7950279863014738\n", + "params=array([ 1.00000013e+00, -3.27625727e-09, 3.91493942e+00]) result=0.7950279863014738\n", + "params=array([1.00000012e+00, 1.16249039e-08, 3.91493942e+00]) result=0.7950279871296404\n", + "params=array([ 1.00000012e+00, -3.27625727e-09, 3.91493943e+00]) result=0.7950279863010958\n", + "params=array([ 1.00000012e+00, -3.27625727e-09, 3.91493942e+00]) result=0.7950279863014735\n", + "params=array([ 1.00000013e+00, -3.27625727e-09, 3.91493942e+00]) result=0.7950279863014735\n", + "params=array([1.00000012e+00, 1.16249039e-08, 3.91493942e+00]) result=0.7950279871296405\n", + "params=array([ 1.00000012e+00, -3.27625727e-09, 3.91493943e+00]) result=0.7950279863010958\n", + "params=array([ 1.00000012e+00, -3.27625727e-09, 3.91493942e+00]) result=0.7950279863014735\n", + "params=array([ 1.00000013e+00, -3.27625727e-09, 3.91493942e+00]) result=0.7950279863014735\n", + "params=array([1.00000012e+00, 1.16249039e-08, 3.91493942e+00]) result=0.7950279871296405\n", + "params=array([ 1.00000012e+00, -3.27625727e-09, 3.91493943e+00]) result=0.7950279863010958\n", + "params=array([ 1.00000012e+00, -2.53458755e-07, 3.91506263e+00]) result=0.7950280089897778\n", + "params=array([ 1.00000012e+00, -5.69104409e-08, 3.91496583e+00]) result=0.7950279909978448\n", + "params=array([ 1.00000012e+00, -1.50051685e-08, 3.91494520e+00]) result=0.7950279873206803\n", + "params=array([ 1.00000012e+00, -5.85220054e-09, 3.91494069e+00]) result=0.7950279865249404\n", + "params=array([ 1.00000012e+00, -3.84252568e-09, 3.91493970e+00]) result=0.7950279863505801\n", + "params=array([ 1.00000012e+00, -3.40076481e-09, 3.91493948e+00]) result=0.7950279863122699\n", + "params=array([ 1.00000012e+00, -3.30363390e-09, 3.91493943e+00]) result=0.7950279863038474\n", + "params=array([ 1.00000012e+00, -3.28227628e-09, 3.91493942e+00]) result=0.7950279863019957\n", + "params=array([ 1.00000012e+00, -3.27757972e-09, 3.91493942e+00]) result=0.7950279863015884\n", + "params=array([ 1.00000012e+00, -3.27654699e-09, 3.91493942e+00]) result=0.7950279863014988\n", + "params=array([ 1.00000012e+00, -3.27631997e-09, 3.91493942e+00]) result=0.795027986301479\n", + "params=array([ 1.00000012e+00, -3.27627033e-09, 3.91493942e+00]) result=0.7950279863014749\n", "res= message: Desired error not necessarily achieved due to precision loss.\n", " success: False\n", " status: 2\n", - " fun: 45.192310319301704\n", - " x: [ 1.000e+00 -7.449e-09 3.344e+00]\n", - " nit: 17\n", - " jac: [ 0.000e+00 5.536e-04 0.000e+00]\n", - " hess_inv: [[ 1.000e+00 3.961e-07 -6.590e-04]\n", - " [ 3.961e-07 3.654e-09 8.073e-08]\n", - " [-6.590e-04 8.073e-08 4.317e-01]]\n", - " nfev: 416\n", - " njev: 101\n" + " fun: 0.7950279863014736\n", + " x: [ 1.000e+00 -3.276e-09 3.915e+00]\n", + " nit: 19\n", + " jac: [-7.451e-09 5.558e-02 -2.536e-05]\n", + " hess_inv: [[ 1.000e+00 -2.894e-08 -1.959e-04]\n", + " [-2.894e-08 4.363e-06 -3.025e-04]\n", + " [-1.959e-04 -3.025e-04 4.195e+00]]\n", + " nfev: 400\n", + " njev: 97\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -126,28 +545,26 @@ } ], "source": [ - "\n", - "\n", "def sigmoid(x, params):\n", " return np.abs(params[0]) / (np.abs(params[1]) + np.exp(np.abs(params[2] * x)))\n", "\n", "\n", - "def sigmoid_combination(z_score, params):\n", - " return sigmoid(z_score, params[0:3])\n", - "\n", - "\n", "def predict(z_score, params):\n", - " zero_crossing = sigmoid_combination(0, params)\n", - " return 1 + 4 * sigmoid_combination(z_score, params) / zero_crossing\n", + " return 1 + 4 * sigmoid(z_score, params) / sigmoid(0, params)\n", "\n", "\n", "def loss(params):\n", - " return np.linalg.norm(mos_scores - predict(z_scores, params))\n", + " sum = 0\n", + " for i in range(len(mos_scores)):\n", + " sum += np.sum((mos_scores[i] - predict(z_scores[i], params)) ** 2) / mos_scores[i].shape[0]\n", + " result = sum / len(mos_scores)\n", + " print(f'{params=} {result=}')\n", + " return result\n", "\n", "\n", "res = scipy.optimize.minimize(loss, np.ones((3,)), method='BFGS')\n", "print(f\"{res=}\")\n", - "plt.scatter(z_scores, mos_scores)\n", + "plt.scatter(all_z_scores, all_mos_scores)\n", "x = np.linspace(0, z_extremes[0], 1000)\n", "plt.plot(x, predict(x, res.x), \"r\", label=\"Predicted\")\n", "plt.xlabel(\"Zimtohrli\")\n", @@ -155,6 +572,39 @@ "plt.legend()\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(z_scores, mos_scores)\n", + "x = np.linspace(0, z_extremes[0], 1000)\n", + "plt.plot(x, predict(x, [4.568494445942755, -0.9999059785585912, 0.0006734929810393679]), \"r\", label=\"Predicted\")\n", + "plt.xlabel(\"Zimtohrli\")\n", + "plt.ylabel(\"MOS\")\n", + "plt.legend()\n", + "plt.show()" + ] } ], "metadata": {