@tensorflow/tfjs-core
Version:
Hardware-accelerated JavaScript library for machine intelligence
264 lines • 12.5 kB
JavaScript
;
/**
* @license
* Copyright 2018 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
var __generator = (this && this.__generator) || function (thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
};
var _this = this;
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("../index");
var jasmine_util_1 = require("../jasmine_util");
var test_util_1 = require("../test_util");
/**
* Unit tests for confusionMatrix().
*/
jasmine_util_1.describeWithFlags('confusionMatrix', jasmine_util_1.ALL_ENVS, function () {
// Reference (Python) TensorFlow code:
//
// ```py
// import tensorflow as tf
//
// tf.enable_eager_execution()
//
// labels = tf.constant([0, 1, 2, 1, 0])
// predictions = tf.constant([0, 2, 2, 1, 0])
// out = tf.confusion_matrix(labels, predictions, 3)
//
// print(out)
// ```
it('3x3 all cases present in both labels and predictions', function () { return __awaiter(_this, void 0, void 0, function () {
var labels, predictions, numClasses, out, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');
predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');
numClasses = 3;
out = tf.math.confusionMatrix(labels, predictions, numClasses);
_a = test_util_1.expectArraysEqual;
return [4 /*yield*/, out.data()];
case 1:
_a.apply(void 0, [_b.sent(), [2, 0, 0, 0, 1, 1, 0, 0, 1]]);
expect(out.dtype).toBe('int32');
expect(out.shape).toEqual([3, 3]);
return [2 /*return*/];
}
});
}); });
it('float32 arguments are accepted', function () { return __awaiter(_this, void 0, void 0, function () {
var labels, predictions, numClasses, out, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
labels = tf.tensor1d([0, 1, 2, 1, 0], 'float32');
predictions = tf.tensor1d([0, 2, 2, 1, 0], 'float32');
numClasses = 3;
out = tf.math.confusionMatrix(labels, predictions, numClasses);
_a = test_util_1.expectArraysEqual;
return [4 /*yield*/, out.data()];
case 1:
_a.apply(void 0, [_b.sent(), [2, 0, 0, 0, 1, 1, 0, 0, 1]]);
expect(out.dtype).toBe('int32');
expect(out.shape).toEqual([3, 3]);
return [2 /*return*/];
}
});
}); });
// Reference (Python) TensorFlow code:
//
// ```py
// import tensorflow as tf
//
// tf.enable_eager_execution()
//
// labels = tf.constant([3, 3, 2, 2, 1, 1, 0, 0])
// predictions = tf.constant([2, 2, 2, 2, 0, 0, 0, 0])
// out = tf.confusion_matrix(labels, predictions, 4)
//
// print(out)
// ```
it('4x4 all cases present in labels, but not predictions', function () { return __awaiter(_this, void 0, void 0, function () {
var labels, predictions, numClasses, out, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
labels = tf.tensor1d([3, 3, 2, 2, 1, 1, 0, 0], 'int32');
predictions = tf.tensor1d([2, 2, 2, 2, 0, 0, 0, 0], 'int32');
numClasses = 4;
out = tf.math.confusionMatrix(labels, predictions, numClasses);
_a = test_util_1.expectArraysEqual;
return [4 /*yield*/, out.data()];
case 1:
_a.apply(void 0, [_b.sent(), [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0]]);
expect(out.dtype).toBe('int32');
expect(out.shape).toEqual([4, 4]);
return [2 /*return*/];
}
});
}); });
it('4x4 all cases present in predictions, but not labels', function () { return __awaiter(_this, void 0, void 0, function () {
var labels, predictions, numClasses, out, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
labels = tf.tensor1d([2, 2, 2, 2, 0, 0, 0, 0], 'int32');
predictions = tf.tensor1d([3, 3, 2, 2, 1, 1, 0, 0], 'int32');
numClasses = 4;
out = tf.math.confusionMatrix(labels, predictions, numClasses);
_a = test_util_1.expectArraysEqual;
return [4 /*yield*/, out.data()];
case 1:
_a.apply(void 0, [_b.sent(), [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0]]);
expect(out.dtype).toBe('int32');
expect(out.shape).toEqual([4, 4]);
return [2 /*return*/];
}
});
}); });
it('Plain arrays as inputs', function () { return __awaiter(_this, void 0, void 0, function () {
var labels, predictions, numClasses, out, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
labels = [3, 3, 2, 2, 1, 1, 0, 0];
predictions = [2, 2, 2, 2, 0, 0, 0, 0];
numClasses = 4;
out = tf.math.confusionMatrix(labels, predictions, numClasses);
_a = test_util_1.expectArraysEqual;
return [4 /*yield*/, out.data()];
case 1:
_a.apply(void 0, [_b.sent(), [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0]]);
expect(out.dtype).toBe('int32');
expect(out.shape).toEqual([4, 4]);
return [2 /*return*/];
}
});
}); });
it('Int32Arrays as inputs', function () { return __awaiter(_this, void 0, void 0, function () {
var labels, predictions, numClasses, out, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
labels = new Int32Array([3, 3, 2, 2, 1, 1, 0, 0]);
predictions = new Int32Array([2, 2, 2, 2, 0, 0, 0, 0]);
numClasses = 4;
out = tf.math.confusionMatrix(labels, predictions, numClasses);
_a = test_util_1.expectArraysEqual;
return [4 /*yield*/, out.data()];
case 1:
_a.apply(void 0, [_b.sent(), [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0]]);
expect(out.dtype).toBe('int32');
expect(out.shape).toEqual([4, 4]);
return [2 /*return*/];
}
});
}); });
// Reference (Python) TensorFlow code:
//
// ```py
// import tensorflow as tf
//
// tf.enable_eager_execution()
//
// labels = tf.constant([0, 4])
// predictions = tf.constant([4, 0])
// out = tf.confusion_matrix(labels, predictions, 5)
//
// print(out)
// ```
it('5x5 predictions and labels both missing some cases', function () { return __awaiter(_this, void 0, void 0, function () {
var labels, predictions, numClasses, out, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
labels = tf.tensor1d([0, 4], 'int32');
predictions = tf.tensor1d([4, 0], 'int32');
numClasses = 5;
out = tf.math.confusionMatrix(labels, predictions, numClasses);
_a = test_util_1.expectArraysEqual;
return [4 /*yield*/, out.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0
]]);
expect(out.dtype).toBe('int32');
expect(out.shape).toEqual([5, 5]);
return [2 /*return*/];
}
});
}); });
it('Invalid numClasses leads to Error', function () {
expect(function () { return tf.math.confusionMatrix(tf.tensor1d([0, 1]), tf.tensor1d([1, 0]), 2.5); })
.toThrowError(/numClasses .* positive integer.* got 2\.5/);
});
it('Incorrect tensor rank leads to Error', function () {
expect(function () { return tf.math.confusionMatrix(
// tslint:disable-next-line:no-any
tf.scalar(0), tf.scalar(0), 1); })
.toThrowError(/rank .* 1.*got 0/);
expect(function () {
// tslint:disable-next-line:no-any
return tf.math.confusionMatrix(tf.zeros([3, 3]), tf.zeros([9]), 2);
})
.toThrowError(/rank .* 1.*got 2/);
expect(function () {
// tslint:disable-next-line:no-any
return tf.math.confusionMatrix(tf.zeros([9]), tf.zeros([3, 3]), 2);
})
.toThrowError(/rank .* 1.*got 2/);
});
it('Mismatch in lengths leads to Error', function () {
expect(
// tslint:disable-next-line:no-any
function () { return tf.math.confusionMatrix(tf.zeros([3]), tf.zeros([9]), 2); })
.toThrowError(/Mismatch .* 3 vs.* 9/);
});
});
//# sourceMappingURL=confusion_matrix_test.js.map