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ML - перед RandomForest

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Mikhalkovich Stanislav 2026-02-19 22:06:38 +03:00
parent 5ddc24e36a
commit 0c4aaad2d1
3 changed files with 173 additions and 39 deletions
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15
bin/Lib/LinearAlgebraML.pas
View file

@ -53,12 +53,14 @@ type
function Min: real;
/// Скалярное произведение
function Dot(b: Vector): real;
// ---------- Сервисные методы ----------
function ToString: string; override := $'{data.Select(x -> x.ToString(''G3''))}';
function ToString(digits: integer): string := $'{data.Select(x -> x.ToString(''G''+digits))}';
procedure Print := data.Print;
procedure Println := data.Println;
function SubvectorBy(indices: array of integer): Vector;
end;
Matrix = class
@ -449,6 +451,17 @@ begin
Result[i] := f(self[i]);
end;
function Vector.SubvectorBy(indices: array of integer): Vector;
begin
var n := indices.Length;
var resultVec := new Vector(n);
for var i := 0 to n - 1 do
resultVec[i] := self[indices[i]];
Result := resultVec;
end;
//-----------------------------
// Matrix

5
bin/Lib/MLCoreABC.pas
View file

@ -16,6 +16,11 @@ type
function Predict(X: Matrix): Vector;
function Clone: IModel;
end;
/// Интерфейс древовидной модели машинного обучения
ITreeModel = interface(IModel)
function FeatureImportances: Vector;
end;
/// Интерфейс классификатора.
/// Наследуется от IModel.

192
bin/Lib/MLModelsABC.pas
View file

@ -249,13 +249,30 @@ type
Threshold: real;
end;
DecisionTreeBase = abstract class
ISplitCriterion = interface
function Impurity(y: Vector): real;
end;
GiniCriterion = class(ISplitCriterion)
public
function Impurity(y: Vector): real;
end;
VarianceCriterion = class(ISplitCriterion)
public
function Impurity(y: Vector): real;
end;
DecisionTreeBase = abstract class(ITreeModel)
protected
fRoot: DecisionTreeNode;
fMaxDepth: integer;
fMinSamplesSplit: integer;
fMinSamplesLeaf: integer;
fFitted: boolean;
fCriterion: ISplitCriterion;
fFeatureImportances: Vector;
fRandomSeed: integer;
function BuildTree(X: Matrix; y: Vector; indices: array of integer; depth: integer): DecisionTreeNode;
@ -265,13 +282,19 @@ type
function LeafNode(value: real): DecisionTreeNode;
function NodeImpurity(y: Vector; indices: array of integer): real; virtual; abstract;
procedure CopyBaseState(dest: DecisionTreeBase);
function GetFeatureSubset(nFeatures: integer): array of integer; virtual;
public
constructor Create(maxDepth: integer := 10; minSamplesSplit: integer := 2;
minSamplesLeaf: integer := 1);
constructor Create(maxDepth: integer := 10; minSamplesSplit: integer := 2; minSamplesLeaf: integer := 1);
function FeatureImportances: Vector;
function Fit(X: Matrix; y: Vector): IModel; virtual; abstract;
function Predict(X: Matrix): Vector; virtual; abstract;
function Clone: IModel; virtual; abstract;
function IsFitted: boolean;
end;
@ -283,34 +306,34 @@ type
function PredictOne(x: Vector): integer;
function MajorityClass(y: Vector; indices: array of integer): integer;
function Gini(y: Vector; indices: array of integer): real;
//function Gini(y: Vector; indices: array of integer): real;
public
constructor Create(maxDepth: integer := 10;
minSamplesSplit: integer := 2;
minSamplesLeaf: integer := 1);
constructor Create(maxDepth: integer := 10; minSamplesSplit: integer := 2; minSamplesLeaf: integer := 1);
function Fit(X: Matrix; y: Vector): IModel;
function Predict(X: Matrix): Vector;
function Clone: IModel;
function Fit(X: Matrix; y: Vector): IModel; override;
function Predict(X: Matrix): Vector; override;
function Clone: IModel; override;
protected
function LeafValue(y: Vector; indices: array of integer): real; override;
function NodeImpurity(y: Vector; indices: array of integer): real; override;
//function NodeImpurity(y: Vector; indices: array of integer): real; override;
end;
DecisionTreeRegressor = class(DecisionTreeBase, IRegressor)
protected
function LeafValue(y: Vector; indices: array of integer): real; override;
function NodeImpurity(y: Vector; indices: array of integer): real; override;
//function NodeImpurity(y: Vector; indices: array of integer): real; override;
private
function PredictOne(x: Vector): real;
public
function Fit(X: Matrix; y: Vector): IModel;
function Predict(X: Matrix): Vector;
function Clone: IModel;
constructor Create(maxDepth: integer := 10; minSamplesSplit: integer := 2; minSamplesLeaf: integer := 1);
function Fit(X: Matrix; y: Vector): IModel; override;
function Predict(X: Matrix): Vector; override;
function Clone: IModel; override;
end;
@ -1221,6 +1244,47 @@ begin
Result := m;
end;
function GiniCriterion.Impurity(y: Vector): real;
begin
if y.Length = 0 then exit(0.0);
var counts := Dict&<integer,integer>;
foreach var v in y.data do
begin
var c := integer(v);
if counts.ContainsKey(c) then
counts[c] += 1
else
counts[c] := 1;
end;
var n := y.Length;
var sumsq := 0.0;
foreach var kv in counts do
begin
var p := kv.Value / n;
sumsq += p * p;
end;
Result := 1.0 - sumsq;
end;
function VarianceCriterion.Impurity(y: Vector): real;
begin
if y.Length = 0 then exit(0.0);
var mean := y.Mean;
var s := 0.0;
foreach var v in y.data do
begin
var d := v - mean;
s += d*d;
end;
Result := s / y.Length;
end;
function LeafClass(c: integer): DecisionTreeNode;
begin
@ -1268,16 +1332,12 @@ begin
Result := n;
end;
constructor DecisionTreeBase.Create(
maxDepth: integer;
minSamplesSplit: integer;
minSamplesLeaf: integer
);
constructor DecisionTreeBase.Create(maxDepth: integer; minSamplesSplit: integer; minSamplesLeaf: integer);
begin
fMaxDepth := maxDepth;
fMinSamplesSplit := minSamplesSplit;
fMinSamplesLeaf := minSamplesLeaf;
fRandomSeed := 0;
end;
procedure DecisionTreeBase.CopyBaseState(dest: DecisionTreeBase);
@ -1291,6 +1351,18 @@ begin
dest.fRoot := fRoot.Clone;
end;
function DecisionTreeBase.GetFeatureSubset(nFeatures: integer): array of integer;
begin
Result := new integer[nFeatures];
for var i := 0 to nFeatures-1 do
Result[i] := i;
end;
function DecisionTreeBase.FeatureImportances: Vector;
begin
Result := fFeatureImportances.Clone;
end;
function DecisionTreeBase.IsFitted: boolean;
begin
Result := fFitted;
@ -1316,7 +1388,7 @@ begin
exit(LeafNode(LeafValue(y, indices)));
// Если узел уже чистый
if NodeImpurity(y, indices) = 0.0 then
if fCriterion.Impurity(y.SubvectorBy(indices)) = 0.0 then
exit(LeafNode(LeafValue(y, indices)));
// Поиск лучшего разбиения
@ -1365,8 +1437,17 @@ begin
var bestThreshold := 0.0;
var n := indices.Length;
var parentImp := fCriterion.Impurity(y.SubvectorBy(indices));
for var j := 0 to X.Cols - 1 do
var bestLeftImp := 0.0;
var bestRightImp := 0.0;
var bestLeftCount := 0;
var bestRightCount := 0;
var features := GetFeatureSubset(X.Cols);
foreach var j in features do
begin
// --- собрать пары (value, rowIndex)
var pairs: array of (real, integer);
@ -1407,8 +1488,8 @@ begin
var leftArr := left.ToArray;
var rightArr := right.ToArray;
var leftImp := NodeImpurity(y, leftArr);
var rightImp := NodeImpurity(y, rightArr);
var leftImp := fCriterion.Impurity(y.SubvectorBy(leftArr));
var rightImp := fCriterion.Impurity(y.SubvectorBy(rightArr));
var weighted :=
(leftArr.Length / n) * leftImp +
@ -1416,6 +1497,11 @@ begin
if weighted < bestScore then
begin
bestLeftImp := leftImp;
bestRightImp := rightImp;
bestLeftCount := leftArr.Length;
bestRightCount := rightArr.Length;
bestScore := weighted;
bestFeature := j;
bestThreshold := threshold;
@ -1431,6 +1517,16 @@ begin
end;
end;
end;
if bestFeature <> -1 then
begin
var wl := bestLeftCount / n;
var wr := bestRightCount / n;
var gain := parentImp - wl * bestLeftImp - wr * bestRightImp;
fFeatureImportances[bestFeature] += gain;
end;
Result.Found := bestFeature <> -1;
Result.Feature := bestFeature;
@ -1479,7 +1575,7 @@ begin
Result := bestClass;
end;
function DecisionTreeClassifier.Gini(y: Vector; indices: array of integer): real;
{function DecisionTreeClassifier.Gini(y: Vector; indices: array of integer): real;
begin
var counts := new integer[fClassCount];
@ -1496,14 +1592,12 @@ begin
end;
Result := 1.0 - sum;
end;
end;}
constructor DecisionTreeClassifier.Create(maxDepth: integer;
minSamplesSplit: integer; minSamplesLeaf: integer);
constructor DecisionTreeClassifier.Create(maxDepth: integer; minSamplesSplit: integer; minSamplesLeaf: integer);
begin
fMaxDepth := maxDepth;
fMinSamplesSplit := minSamplesSplit;
fMinSamplesLeaf := minSamplesLeaf;
inherited Create(maxDepth, minSamplesSplit, minSamplesLeaf);
fCriterion := new GiniCriterion;
end;
function DecisionTreeClassifier.Fit(X: Matrix; y: Vector): IModel;
@ -1513,6 +1607,11 @@ begin
if X.Rows = 0 then
ArgumentError(ER_EMPTY_DATASET);
if fRandomSeed <> 0 then
Randomize(fRandomSeed);
fFeatureImportances := new Vector(X.Cols);
// --- 1. Найти уникальные классы
var unique := new HashSet<integer>;
@ -1547,6 +1646,11 @@ begin
// --- 5. Построить дерево
fRoot := BuildTree(X, yEncoded, indices, 0);
var s := fFeatureImportances.Sum;
if s > 0 then
for var i := 0 to fFeatureImportances.Length-1 do
fFeatureImportances[i] /= s;
fFitted := true;
@ -1605,9 +1709,10 @@ begin
Result := MajorityClass(y, indices);
end;
function DecisionTreeClassifier.NodeImpurity(y: Vector; indices: array of integer): real;
constructor DecisionTreeRegressor.Create(maxDepth: integer; minSamplesSplit: integer; minSamplesLeaf: integer);
begin
Result := Gini(y, indices);
inherited Create(maxDepth, minSamplesSplit, minSamplesLeaf);
fCriterion := new VarianceCriterion;
end;
function DecisionTreeRegressor.LeafValue(y: Vector; indices: array of integer): real;
@ -1619,7 +1724,7 @@ begin
Result := sum / indices.Length;
end;
function DecisionTreeRegressor.NodeImpurity(y: Vector; indices: array of integer): real;
{function DecisionTreeRegressor.NodeImpurity(y: Vector; indices: array of integer): real;
begin
var sum := 0.0;
var sqSum := 0.0;
@ -1634,7 +1739,7 @@ begin
var mean := sum / n;
Result := (sqSum / n) - mean * mean;
end;
end;}
function DecisionTreeRegressor.Fit(X: Matrix; y: Vector): IModel;
begin
@ -1643,12 +1748,23 @@ begin
if X.Rows = 0 then
ArgumentError(ER_EMPTY_DATASET);
if fRandomSeed <> 0 then
Randomize(fRandomSeed);
fFeatureImportances := new Vector(X.Cols);
var indices := new integer[X.Rows];
for var i := 0 to X.Rows - 1 do
indices[i] := i;
fRoot := BuildTree(X, y, indices, 0);
var s := fFeatureImportances.Sum;
if s > 0 then
for var i := 0 to fFeatureImportances.Length-1 do
fFeatureImportances[i] /= s;
fFitted := true;
Result := Self;