| 1 | {"version":3,"file":"confusion_matrix.js","sourceRoot":"","sources":["../../src/ops/confusion_matrix.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAGH,OAAO,EAAC,eAAe,EAAC,MAAM,oBAAoB,CAAC;AAEnD,OAAO,KAAK,IAAI,MAAM,SAAS,CAAC;AAEhC,OAAO,EAAC,IAAI,EAAC,MAAM,QAAQ,CAAC;AAC5B,OAAO,EAAC,MAAM,EAAC,MAAM,WAAW,CAAC;AACjC,OAAO,EAAC,MAAM,EAAC,MAAM,WAAW,CAAC;AACjC,OAAO,EAAC,EAAE,EAAC,MAAM,aAAa,CAAC;AAC/B,OAAO,EAAC,SAAS,EAAC,MAAM,aAAa,CAAC;AAEtC;;;;;;;;;;;;;;;;;;;;;;;;;;;;GA4BG;AACH,MAAM,UAAU,gBAAgB,CAC5B,MAA2B,EAAE,WAAgC,EAC7D,UAAkB;IACpB,MAAM,OAAO,GAAG,eAAe,CAAC,MAAM,EAAE,QAAQ,EAAE,iBAAiB,CAAC,CAAC;IACrE,MAAM,YAAY,GACd,eAAe,CAAC,WAAW,EAAE,aAAa,EAAE,iBAAiB,CAAC,CAAC;IAEnE,IAAI,CAAC,MAAM,CACP,UAAU,IAAI,IAAI,IAAI,UAAU,GAAG,CAAC,IAAI,MAAM,CAAC,SAAS,CAAC,UAAU,CAAC,EACpE,GAAG,EAAE,CAAC,sDAAsD;QACxD,WAAW,UAAU,EAAE,CAAC,CAAC;IACjC,IAAI,CAAC,MAAM,CACP,OAAO,CAAC,IAAI,KAAK,CAAC,EAClB,GAAG,EAAE,CAAC,gDAAgD,OAAO,CAAC,IAAI,EAAE,CAAC,CAAC;IAC1E,IAAI,CAAC,MAAM,CACP,YAAY,CAAC,IAAI,KAAK,CAAC,EACvB,GAAG,EAAE,CAAC,4CAA4C;QAC9C,WAAW,YAAY,CAAC,IAAI,EAAE,CAAC,CAAC;IACxC,IAAI,CAAC,MAAM,CACP,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,YAAY,CAAC,KAAK,CAAC,CAAC,CAAC,EAC1C,GAAG,EAAE,CAAC,sCAAsC;QACxC,GAAG,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,QAAQ,YAAY,CAAC,KAAK,CAAC,CAAC,CAAC,IAAI;QACpD,iEAAiE,CAAC,CAAC;IAC3E,IAAI,CAAC,MAAM,CACP,UAAU,GAAG,CAAC,IAAI,MAAM,CAAC,SAAS,CAAC,UAAU,CAAC,EAC9C,GAAG,EAAE,CAAC,2DAA2D;QAC7D,GAAG,UAAU,EAAE,CAAC,CAAC;IACzB,mEAAmE;IACnE,oEAAoE;IAEpE,MAAM,YAAY,GAAG,MAAM,CAAC,IAAI,CAAC,OAAO,EAAE,OAAO,CAAC,EAAE,UAAU,CAAa,CAAC;IAC5E,MAAM,iBAAiB,GACnB,MAAM,CAAC,IAAI,CAAC,YAAY,EAAE,OAAO,CAAC,EAAE,UAAU,CAAa,CAAC;IAChE,MAAM,aAAa,GAAa,SAAS,CAAC,YAAY,CAAC,CAAC;IACxD,MAAM,OAAO,GAAa,MAAM,CAAC,aAAa,EAAE,iBAAiB,CAAC,CAAC;IACnE,OAAO,IAAI,CAAC,OAAO,EAAE,OAAO,CAAC,CAAC;AAChC,CAAC;AAED,MAAM,CAAC,MAAM,eAAe,GAAG,EAAE,CAAC,EAAC,gBAAgB,EAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {cast} from './cast';\nimport {matMul} from './mat_mul';\nimport {oneHot} from './one_hot';\nimport {op} from './operation';\nimport {transpose} from './transpose';\n\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * // [0, 1, 1],\n * // [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n * for the classes. The shape is `[numExamples]`, where\n * `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n * 0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n * Its value must be larger than the largest element in `labels` and\n * `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n * row `r` and column `c` is the number of times examples of actual class\n * `r` were predicted as class `c`.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nexport function confusionMatrix_(\n labels: Tensor1D|TensorLike, predictions: Tensor1D|TensorLike,\n numClasses: number): Tensor2D {\n const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n const $predictions =\n convertToTensor(predictions, 'predictions', 'confusionMatrix');\n\n util.assert(\n numClasses == null || numClasses > 0 && Number.isInteger(numClasses),\n () => `If provided, numClasses must be a positive integer, ` +\n `but got ${numClasses}`);\n util.assert(\n $labels.rank === 1,\n () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n util.assert(\n $predictions.rank === 1,\n () => `Expected the rank of predictions to be 1, ` +\n `but got ${$predictions.rank}`);\n util.assert(\n $labels.shape[0] === $predictions.shape[0],\n () => `Mismatch in the number of examples: ` +\n `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n `Labels and predictions should have the same number of elements.`);\n util.assert(\n numClasses > 0 && Number.isInteger(numClasses),\n () => `numClasses is required to be a positive integer, but got ` +\n `${numClasses}`);\n // TODO(cais): In the future, if oneHot supports tensors inputs for\n // `numClasses`, `confusionMatrix` can make `numClasses` optional.\n\n const oneHotLabels = oneHot(cast($labels, 'int32'), numClasses) as Tensor2D;\n const oneHotPredictions =\n oneHot(cast($predictions, 'int32'), numClasses) as Tensor2D;\n const oneHotLabelsT: Tensor2D = transpose(oneHotLabels);\n const product: Tensor2D = matMul(oneHotLabelsT, oneHotPredictions);\n return cast(product, 'int32');\n}\n\nexport const confusionMatrix = op({confusionMatrix_});\n"]} |