Classification metrics

src/linalg/utils.ts

@@ -1,4 +1,4 @@
-import { Tensor, RecursiveArray, norm, div, max, sub, abs, lessEqual, slice, tensor, isNaN, where, tidy } from '@tensorflow/tfjs-core';
+import { Tensor, RecursiveArray, norm, div, max, sub, abs, lessEqual, slice, tensor, isNaN, where, tidy, stack, squeeze } from '@tensorflow/tfjs-core';
 import { checkArray } from '../utils/validation';
 
 /**
@@ -114,3 +114,21 @@ export const fillNaN = (xData: Tensor | RecursiveArray<number>, fillV = 0): Tens
     return where(cond, fillV, xTensor);
   });
 };
+
+/**
+ * Get the digonal elements in a matrix
+ * @param matrix target matrix
+ * @returns tensor of diagnal elements
+ */
+export const getDiagElements = (matrix: Tensor | number[]): Tensor => {
+  return tidy(() => {
+    const matrixTensor = checkArray(matrix, 'any', 2);
+    const [ m, n ] = matrixTensor.shape;
+    const rank = m > n ? n : m;
+    const diagElements = [];
+    for (let i = 0; i < rank; i++) {
+      diagElements.push(slice(matrixTensor, [ i, i ], [ 1, 1 ]));
+    }
+    return squeeze(stack(diagElements));
+  });
+};

src/metrics/classifier.ts

@@ -1,15 +1,258 @@
-import { Tensor, equal, sum, div } from '@tensorflow/tfjs-core';
-import { checkArray } from '../utils/validation';
+import { Tensor, Tensor1D, equal, sum, div, math, divNoNan, concat, mul, add, cast } from '@tensorflow/tfjs-core';
+import { checkSameLength } from '../utils/validation';
+import { getDiagElements } from '../linalg/utils';
+import { LabelEncoder } from '../preprocess';
 
-export const accuracyScore = ( yTrue: Tensor | string[] | number[], yPred: Tensor | string[] | number[] ): number => {
-  const yTrueTensor = checkArray(yTrue, 'any', 1);
-  const yPredTensor = checkArray(yPred, 'any', 1);
-  const yTrueCount = yTrueTensor.shape[0];
-  const yPredCount = yPredTensor.shape[0];
-  if (yTrueCount != yPredCount) {
-    throw new Error('Shape of yTrue should match shape of yPred');
-  }
+export type ClassificationReport = {
+  precisions: Tensor;
+  recalls: Tensor;
+  f1s: Tensor;
+  confusionMatrix: Tensor;
+  categories: Tensor;
+  accuracy: number;
+  averagePrecision: number;
+  averageRecall: number;
+  averageF1: number;
+};
+
+export type ClassificationAverageTypes = 'macro' | 'weighted' | 'micro'
+
+export const accuracyScore = (yTrue: Tensor | string[] | number[], yPred: Tensor | string[] | number[]): number => {
+  const [ yTrueTensor, yPredTensor, nLabels ] = checkSameLength(yTrue, yPred);
   // TODO(sugarspectre): Accuaracy score computation
-  const score = div(sum(equal(yPred, yTrue)), yTrueCount).dataSync()[0];
+  const score = div(sum(equal(yPredTensor, yTrueTensor)), nLabels).dataSync()[0];
   return score;
 };
+
+export const getConfusionMatrix = async (yTrue: Tensor | string[] | number[], yPred: Tensor | string[] | number[]): Promise<{ confusionMatrix: Tensor, categories: Tensor }> => {
+  const [ yTrueTensor, yPredTensor ] = checkSameLength(yTrue, yPred);
+  const labelEncoder = new LabelEncoder();
+  await labelEncoder.init(concat([ yTrueTensor, yPredTensor ]));
+  const yTrueEncode = await labelEncoder.encode(yTrueTensor);
+  const yPredEncode = await labelEncoder.encode(yPredTensor);
+  const numClasses = labelEncoder.categories.shape[0];
+  const confusionMatrix = cast(math.confusionMatrix(yTrueEncode as Tensor1D, yPredEncode as Tensor1D, numClasses), 'float32');
+  return { confusionMatrix, categories: labelEncoder.categories };
+};
+
+const getPrecisionScoreByConfusionMatrix = (confusionMatrix: Tensor, average: ClassificationAverageTypes = 'micro'): number => {
+  const confusionDiag = getDiagElements(confusionMatrix);
+  const numClasses = confusionMatrix.shape[0];
+  const precisions = divNoNan(confusionDiag, sum(confusionMatrix, 0));
+  const weights = divNoNan(sum(confusionMatrix, 0), sum(confusionMatrix));
+  const weightsSupport = divNoNan(sum(confusionMatrix, 1), sum(confusionMatrix));
+  switch (average) {
+  case 'micro':
+    return sum(mul(precisions, weights)).dataSync()[0];
+  case 'macro':
+    return divNoNan(sum(precisions), numClasses).dataSync()[0];
+  case 'weighted':
+    return sum(mul(precisions, weightsSupport)).dataSync()[0];
+  default:
+    return sum(mul(precisions, weights)).dataSync()[0];
+  }
+};
+
+const getRecallScoreByConfusionMatrix = (confusionMatrix: Tensor, average: ClassificationAverageTypes = 'micro'): number => {
+  const confusionDiag = getDiagElements(confusionMatrix);
+  const numClasses = confusionMatrix.shape[0];
+  const recalls = divNoNan(confusionDiag, sum(confusionMatrix, 1));
+  const weights = divNoNan(sum(confusionMatrix, 1), sum(confusionMatrix));
+  switch (average) {
+  case 'micro':
+    return sum(mul(recalls, weights)).dataSync()[0];
+  case 'macro':
+    return divNoNan(sum(recalls), numClasses).dataSync()[0];
+  case 'weighted':
+    return sum(mul(recalls, weights)).dataSync()[0];
+  default:
+    return sum(mul(recalls, weights)).dataSync()[0];
+  }
+};
+
+const getF1ScoreByConfusionMatrix = (confusionMatrix: Tensor, average: ClassificationAverageTypes = 'micro'): number => {
+  const confusionDiag = getDiagElements(confusionMatrix);
+  const numClasses = confusionMatrix.shape[0];
+  const precisions = divNoNan(confusionDiag, sum(confusionMatrix, 0));
+  const recalls = divNoNan(confusionDiag, sum(confusionMatrix, 1));
+  const f1s = divNoNan(mul(mul(2, precisions), recalls), add(precisions, recalls));
+  const weights = divNoNan(sum(confusionMatrix, 0), sum(confusionMatrix));
+  const weightsSupport = divNoNan(sum(confusionMatrix, 1), sum(confusionMatrix));
+  switch (average) {
+  case 'micro': {
+    const precision = sum(mul(precisions, weights)).dataSync()[0];
+    const recall = sum(mul(recalls, weightsSupport)).dataSync()[0];
+    return (divNoNan(mul(mul(2, precision), recall), add(precision, recall))).dataSync()[0];
+  }
+  case 'macro':
+    return divNoNan(sum(f1s), numClasses).dataSync()[0];
+  case 'weighted':
+    return sum(mul(f1s, weightsSupport)).dataSync()[0];
+  default:
+    return sum(mul(f1s, weights)).dataSync()[0];
+  }
+};
+
+/**
+ * Compute the precision score for all classes.
+ * Precision score is the ratio tp / (tp + fp), where tp is the number of true positives
+ * and fp the number of false positives. The precision is intuitively the ability of
+ * the classifier not to label as positive a sample that is negative.
+ * The best value is 1 and the worst value is 0.
+ * @param yTrue Ground truth (correct) target values.
+ * @param yPred Estimated targets as returned by a classifier.
+ * @returns Tensor of precision scores
+ */
+export const getPrecisionScores = async (yTrue: Tensor | string[] | number[], yPred: Tensor | string[] | number[]): Promise<{ precisions: Tensor, categories: Tensor }> => {
+  const { confusionMatrix, categories } = await getConfusionMatrix(yTrue, yPred);
+  const confusionDiag = getDiagElements(confusionMatrix);
+  const precisions = divNoNan(confusionDiag, sum(confusionMatrix, 0));
+  return { precisions: precisions, categories };
+};
+
+/**
+ * Compute the recall score.
+ * Recall score is the ratio tp / (tp + fn), where tp is the number of true positives
+ * and fn the number of false negtive. The recall is intuitively the ability of the
+ * classifier to find all the positive samples.
+ * The best value is 1 and the worst value is 0.
+ * @param yTrue Ground truth (correct) target values.
+ * @param yPred Estimated targets as returned by a classifier.
+ * @returns Tensor of recall scores
+ */
+export const getRecallScores = async (yTrue: Tensor | string[] | number[], yPred: Tensor | string[] | number[]): Promise<{ recalls: Tensor, categories: Tensor }> => {
+  const { confusionMatrix, categories } = await getConfusionMatrix(yTrue, yPred);
+  const confusionDiag = getDiagElements(confusionMatrix);
+  return { recalls: divNoNan(confusionDiag, sum(confusionMatrix, 1)), categories: categories };
+};
+
+/**
+ * Compute the f1 score.
+ * The F1 score can be interpreted as a harmonic mean of the precision and recall,
+ * where an F1 score reaches its best value at 1 and worst score at 0. The relative
+ * contribution of precision and recall to the F1 score are equal. The formula for the F1 score is:
+ * `2 * precision * recall / (precision + recall)`
+  * @param yTrue Ground truth (correct) target values.
+ * @param yPred Estimated targets as returned by a classifier.
+ * @returns Tensor of f1 scores
+ */
+export const getF1Scores = async (yTrue: Tensor | string[] | number[], yPred: Tensor | string[] | number[]): Promise<{ f1s: Tensor, categories: Tensor }> => {
+  const { confusionMatrix, categories } = await getConfusionMatrix(yTrue, yPred);
+  const confusionDiag = getDiagElements(confusionMatrix);
+  const precisions = divNoNan(confusionDiag, sum(confusionMatrix, 0));
+  const recalls = divNoNan(confusionDiag, sum(confusionMatrix, 1));
+  const f1s = divNoNan(mul(mul(2, precisions), recalls), add(precisions, recalls));
+  return { f1s, categories };
+};
+
+/**
+ * Compute the precision score.
+ * Precision score is the ratio tp / (tp + fp), where tp is the number of true positives
+ * and fp the number of false positives. The precision is intuitively the ability of
+ * the classifier not to label as positive a sample that is negative.
+ * The best value is 1 and the worst value is 0.
+ * @param yTrue Ground truth (correct) target values.
+ * @param yPred Estimated targets as returned by a classifier.
+ * @param average \{'micro', 'macro', 'weighted'\}
+ * This parameter is required for multiclass/multilabel targets. This determines the type of averaging
+ * performed on the data, **default='micro'**:
+ * - `'micro'`:
+ *  Calculate metrics globally by counting the total true positives, false negatives and false positives.
+ * - `'macro'`:
+ *  Calculate metrics for each label, and find their unweighted mean. This does not take label
+ *  imbalance into account.
+ * - `'weighted'`:
+ *  Calculate metrics for each label, and find their average weighted by support (the number of true
+ * instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an
+ * F-score that is not between precision and recall.
+ * @returns Tensor of precision scores
+ */
+export const getPrecisionScore = async (yTrue: Tensor | string[] | number[], yPred: Tensor | string[] | number[], average: ClassificationAverageTypes = 'micro'): Promise<number> => {
+  const { confusionMatrix } = await getConfusionMatrix(yTrue, yPred);
+  return getPrecisionScoreByConfusionMatrix(confusionMatrix, average);
+};
+
+
+/**
+ * Compute the recall score.
+ * Recall score is the ratio tp / (tp + fn), where tp is the number of true positives
+ * and fn the number of false negtive. The recall is intuitively the ability of the
+ * classifier to find all the positive samples.
+ * The best value is 1 and the worst value is 0.
+ * @param yTrue Ground truth (correct) target values.
+ * @param yPred Estimated targets as returned by a classifier.
+ * @param average \{'micro', 'macro', 'weighted'\}
+ * This parameter is required for multiclass/multilabel targets. This determines the type of averaging
+ * performed on the data, **default='micro'**:
+ * - `'micro'`:
+ *  Calculate metrics globally by counting the total true positives, false negatives and false positives.
+ * - `'macro'`:
+ *  Calculate metrics for each label, and find their unweighted mean. This does not take label
+ *  imbalance into account.
+ * - `'weighted'`:
+ *  Calculate metrics for each label, and find their average weighted by support (the number of true
+ * instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an
+ * F-score that is not between precision and recall.
+ * @returns precision score
+ */
+export const getRecallScore = async (yTrue: Tensor | string[] | number[], yPred: Tensor | string[] | number[], average: ClassificationAverageTypes = 'micro'): Promise<number> => {
+  const { confusionMatrix } = await getConfusionMatrix(yTrue, yPred);
+  return getRecallScoreByConfusionMatrix(confusionMatrix, average);
+};
+
+
+/**
+ * Compute the f1 score.
+ * The F1 score can be interpreted as a harmonic mean of the precision and recall,
+ * where an F1 score reaches its best value at 1 and worst score at 0. The relative
+ * contribution of precision and recall to the F1 score are equal. The formula for the F1 score is:
+ * `2 * precision * recall / (precision + recall)`
+ * @param yTrue Ground truth (correct) target values.
+ * @param yPred Estimated targets as returned by a classifier.
+ * @param average \{'micro', 'macro', 'weighted'\}
+ * This parameter is required for multiclass/multilabel targets. This determines the type of averaging
+ * performed on the data, **default='micro'**:
+ * - `'micro'`:
+ *  Calculate metrics globally by counting the total true positives, false negatives and false positives.
+ * - `'macro'`:
+ *  Calculate metrics for each label, and find their unweighted mean. This does not take label
+ *  imbalance into account.
+ * - `'weighted'`:
+ *  Calculate metrics for each label, and find their average weighted by support (the number of true
+ * instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an
+ * F-score that is not between precision and recall.
+ * @returns precision score
+ */
+export const getF1Score = async (yTrue: Tensor | string[] | number[], yPred: Tensor | string[] | number[], average: ClassificationAverageTypes = 'micro'): Promise<number> => {
+  const { confusionMatrix } = await getConfusionMatrix(yTrue, yPred);
+  return getF1ScoreByConfusionMatrix(confusionMatrix, average);
+};
+
+/**
+ * Generate classification report
+ * @param yTrue true labels
+ * @param yPred predicted labels
+ * @returns classification report object, the struct of report will be like following
+ */
+export const getClassificationReport = async (yTrue: Tensor | string[] | number[], yPred: Tensor | string[] | number[], average: ClassificationAverageTypes = 'weighted'): Promise<ClassificationReport> => {
+  const { confusionMatrix, categories } = await getConfusionMatrix(yTrue, yPred);
+  const confusionDiag = getDiagElements(confusionMatrix);
+  const precisions = divNoNan(confusionDiag, sum(confusionMatrix, 0));
+  const recalls = divNoNan(confusionDiag, sum(confusionMatrix, 1));
+  const f1s = mul(divNoNan(mul(precisions, recalls), add(precisions, recalls)), 2);
+  const accuracy = accuracyScore(yTrue, yPred);
+  const averagePrecision = getPrecisionScoreByConfusionMatrix(confusionMatrix, average);
+  const averageRecall = getRecallScoreByConfusionMatrix(confusionMatrix, average);
+  const averageF1 = getF1ScoreByConfusionMatrix(confusionMatrix, average);
+  return {
+    precisions,
+    recalls,
+    f1s,
+    confusionMatrix,
+    categories,
+    accuracy,
+    averageF1,
+    averagePrecision,
+    averageRecall
+  };
+};

src/metrics/index.ts

@@ -1 +1,19 @@
-export { accuracyScore } from './classifier';
+export {
+  accuracyScore,
+  getClassificationReport,
+  getF1Score,
+  getF1Scores,
+  getPrecisionScore,
+  getPrecisionScores,
+  getRecallScore,
+  getRecallScores,
+  getConfusionMatrix
+} from './classifier';
+
+export {
+  getRSquare,
+  getAICLM,
+  getMeanSquaredError,
+  getAdjustedRSquare,
+  getResidualVariance
+} from './regression';