qminer
Version:
A C++ based data analytics platform for processing large-scale real-time streams containing structured and unstructured data
473 lines (422 loc) • 19.3 kB
JavaScript
/**
* Copyright (c) 2015, Jozef Stefan Institute, Quintelligence d.o.o. and contributors
* All rights reserved.
*
* This source code is licensed under the FreeBSD license found in the
* LICENSE file in the root directory of this source tree.
*/
// JavaScript source code
var analytics = require('../../index.js').analytics;
var la = require('../../index.js').la;
var assert = require("../../src/nodejs/scripts/assert.js");
//Unit test for SVC
describe("SVC test", function () {
describe("Constructor test", function () {
it("It should return a default constructor", function () {
var SVC = new analytics.SVC();
var SVCjSon = SVC.getParams();
assert.strictEqual(SVCjSon.c, 1);
assert.strictEqual(SVCjSon.j, 1);
assert.strictEqual(SVCjSon.batchSize, 1000);
assert.strictEqual(SVCjSon.maxIterations, 10000);
assert.strictEqual(SVCjSon.maxTime, 1);
assert.eqtol(SVCjSon.minDiff, 1e-6);
assert.strictEqual(SVCjSon.verbose, false);
});
it("It should return a SVC created by Json", function () {
var SVC = new analytics.SVC({ c: 5, j: 5, batchSize: 5, maxIterations: 5, maxTime: 1, minDiff: 1e-10, verbose: true });
var SVCjSon = SVC.getParams();
assert.strictEqual(SVCjSon.c, 5);
assert.strictEqual(SVCjSon.j, 5);
assert.strictEqual(SVCjSon.batchSize, 5);
assert.strictEqual(SVCjSon.maxIterations, 5);
assert.strictEqual(SVCjSon.maxTime, 1);
assert.eqtol(SVCjSon.minDiff, 1e-10);
assert.strictEqual(SVCjSon.verbose, true);
});
it("It should return a SVC created by Json, not all key values are given", function () {
var SVC = new analytics.SVC({ c: 5, batchSize: 5, maxTime: 1, verbose: true });
var SVCjSon = SVC.getParams();
assert.strictEqual(SVCjSon.c, 5);
assert.strictEqual(SVCjSon.j, 1);
assert.strictEqual(SVCjSon.batchSize, 5);
assert.strictEqual(SVCjSon.maxIterations, 10000);
assert.strictEqual(SVCjSon.maxTime, 1);
assert.eqtol(SVCjSon.minDiff, 1e-6);
assert.strictEqual(SVCjSon.verbose, true);
});
it("It should return a SVC created by an empty Json", function () {
var SVC = new analytics.SVC({});
var SVCjSon = SVC.getParams();
assert.strictEqual(SVCjSon.c, 1);
assert.strictEqual(SVCjSon.j, 1);
assert.strictEqual(SVCjSon.batchSize, 1000);
assert.strictEqual(SVCjSon.maxIterations, 10000);
assert.strictEqual(SVCjSon.maxTime, 1);
assert.eqtol(SVCjSon.minDiff, 1e-6);
assert.strictEqual(SVCjSon.verbose, false);
});
it("It should return a SVC created by Json, with added key values", function () {
var SVC = new analytics.SVC({ alpha: 5, beta: 10, s: 3, batchSize: 10000, verbose: true });
var SVCjSon = SVC.getParams();
assert.strictEqual(SVCjSon.c, 1);
assert.strictEqual(SVCjSon.j, 1);
assert.strictEqual(SVCjSon.batchSize, 10000);
assert.strictEqual(SVCjSon.maxIterations, 10000);
assert.strictEqual(SVCjSon.maxTime, 1);
assert.eqtol(SVCjSon.minDiff, 1e-6);
assert.strictEqual(SVCjSon.verbose, true);
});
});
describe("GetParams tests", function () {
it("should return the parameters of the default SVC model as Json", function () {
var SVC = new analytics.SVC();
var SVCjSon = SVC.getParams();
assert.strictEqual(SVCjSon.c, 1);
assert.strictEqual(SVCjSon.j, 1);
assert.strictEqual(SVCjSon.batchSize, 1000);
assert.strictEqual(SVCjSon.maxIterations, 10000);
assert.strictEqual(SVCjSon.maxTime, 1);
assert.eqtol(SVCjSon.minDiff, 1e-6);
assert.strictEqual(SVCjSon.verbose, false);
})
it("should return the parameters of the default SVC model as Json, without some key values", function () {
var SVC = new analytics.SVC({ c: 3, j: 2, maxTime: 1 });
var SVCjSon = SVC.getParams();
assert.strictEqual(SVCjSon.c, 3);
assert.strictEqual(SVCjSon.j, 2);
assert.strictEqual(SVCjSon.batchSize, 1000);
assert.strictEqual(SVCjSon.maxIterations, 10000);
assert.strictEqual(SVCjSon.maxTime, 1);
assert.eqtol(SVCjSon.minDiff, 1e-6);
assert.strictEqual(SVCjSon.verbose, false);
})
it("should return the parameters of the default SVC model as Json, with added key values", function () {
var SVC = new analytics.SVC({ alpha: 3, beta: 3, z: 3 });
var SVCjSon = SVC.getParams();
assert.strictEqual(SVCjSon.c, 1);
assert.strictEqual(SVCjSon.j, 1);
assert.strictEqual(SVCjSon.batchSize, 1000);
assert.strictEqual(SVCjSon.maxIterations, 10000);
assert.strictEqual(SVCjSon.maxTime, 1);
assert.eqtol(SVCjSon.minDiff, 1e-6);
assert.strictEqual(SVCjSon.verbose, false);
})
});
describe("SetParams tests", function () {
it("should return the existing SVC with the changed values", function () {
var SVC = new analytics.SVC();
SVC.setParams({ j: 3, maxTime: 2 });
var SVCjSon = SVC.getParams();
assert.strictEqual(SVCjSon.c, 1);
assert.strictEqual(SVCjSon.j, 3);
assert.strictEqual(SVCjSon.batchSize, 1000);
assert.strictEqual(SVCjSon.maxIterations, 10000);
assert.strictEqual(SVCjSon.maxTime, 2);
assert.eqtol(SVCjSon.minDiff, 1e-6);
assert.strictEqual(SVCjSon.verbose, false);
})
it("should return the existing SVC with the changed, added values", function () {
var SVC = new analytics.SVC();
SVC.setParams({ j: 3, maxTime: 2, alpha: 5, z: 10 });
var SVCjSon = SVC.getParams();
assert.strictEqual(SVCjSon.c, 1);
assert.strictEqual(SVCjSon.j, 3);
assert.strictEqual(SVCjSon.batchSize, 1000);
assert.strictEqual(SVCjSon.maxIterations, 10000);
assert.strictEqual(SVCjSon.maxTime, 2);
assert.eqtol(SVCjSon.minDiff, 1e-6);
assert.strictEqual(SVCjSon.verbose, false);
})
it("should throw an exception if the argument is not Json", function () {
var SVC = new analytics.SVC();
assert.throws(function () {
SVC.setParams(1);
});
})
it("should throw an exception if there is no given argument", function () {
var SVC = new analytics.SVC();
assert.throws(function () {
SVC.setParams();
});
})
it('should throw an exception if the algorithm has changed', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVC = new analytics.SVC();
assert.throws(function () {
SVC.setParams({ algorithm: "LIBSVM", kernel: "RBF" });
});
})
});
describe("Weights tests", function () {
it("should return an empty vector", function () {
var SVC = new analytics.SVC();
var Vec = SVC.weights;
assert.strictEqual(Vec.length, 0);
})
it("should return an empty vector even if the parameters have been changed", function () {
var SVC = new analytics.SVC();
SVC.setParams({ j: 3, maxTime: 2 });
var Vec = SVC.weights;
assert.strictEqual(Vec.length, 0);
})
});
describe("Bias tests", function () {
it("should return zero", function () {
var SVC = new analytics.SVC();
var num = SVC.weights;
assert.strictEqual(num.length, 0);
})
it("should return zero even if the parameters have been changed", function () {
var SVC = new analytics.SVC();
SVC.setParams({ j: 3, maxTime: 2 });
var num = SVC.weights;
assert.strictEqual(num.length, 0);
})
});
describe("GetModel tests", function () {
it("should return parameters of the model", function () {
var SVC = new analytics.SVC();
var Model = SVC.getModel();
assert.strictEqual(Model.weights.length, 0);
})
it("should ignore extra parameters given to the function", function () {
var SVC = new analytics.SVC();
var Model = SVC.getModel(1);
assert.strictEqual(Model.weights.length, 0);
})
});
describe('Fit Tests', function () {
it('should not throw an exception when given the matrix and vector', function () {
var matrix = new la.Matrix([[0, 1, -1, 0], [1, 0, 0, -1]]);
var vec = new la.Vector([1, 1, -1, -1]);
var SVC = new analytics.SVC();
assert.doesNotThrow(function () {
SVC.fit(matrix, vec);
});
})
it('should create a model out of the matrix and vector', function () {
var matrix = new la.Matrix([[0, 1, -1, 0], [1, 0, 0, -1]]);
var vec = new la.Vector([1, 1, -1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
var model = SVC.getModel();
assert.eqtol(model.weights[0], 1, 1e-3);
assert.eqtol(model.weights[1], 1, 1e-3);
})
it('should throw an exception if the number of matrix columns and vector length are not equal', function () {
var matrix = new la.Matrix([[0, 1, -1, 0], [1, 0, 0, -1]]);
var vec = new la.Vector([1, 1, -1]);
var SVC = new analytics.SVC();
assert.throws(function () {
SVC.fit(matrix, vec);
});
})
it('should return a close-zero model', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([-1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
var model = SVC.getModel();
assert.eqtol(model.weights[0], 0, 1e-2);
assert.eqtol(model.weights[1], 0, 1e-2);
})
it('should forget the previous model', function () {
var matrix = new la.Matrix([[0, 1, -1, 0], [1, 0, 0, -1]]);
var vec = new la.Vector([1, 1, -1, -1]);
var SVC = new analytics.SVC();
// first model
SVC.fit(matrix, vec);
var model = SVC.getModel();
assert.eqtol(model.weights[0], 1, 1e-3);
assert.eqtol(model.weights[1], 1, 1e-3);
var matrix2 = new la.Matrix([[1, -1], [0, 0]]);
var vec2 = new la.Vector([1, -1]);
//second model
SVC.fit(matrix2, vec2);
var model = SVC.getModel();
assert.eqtol(model.weights[0], 1, 1e-3);
assert.eqtol(model.weights[1], 0, 1e-3);
})
// testing getModel with fit
it('should return the fitted model', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
var model = SVC.getModel();
assert.eqtol(model.weights[0], 1, 1e-3);
assert.eqtol(model.weights[1], 0, 1e-3);
})
// testing setParams
it('shouldn\'t change the model, when setting new parameters', function () {
// creating the model
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
// setting the parameters
SVC.setParams({ c: 10, j: 5, maxTime: 2 });
// seeing if the model is unchanged
var model = SVC.getModel();
assert.eqtol(model.weights[0], 1, 1e-3);
assert.eqtol(model.weights[1], 0, 1e-3);
})
});
describe('Predict Tests', function () {
it('should not throw an exception', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
var vec2 = new la.Vector([3, 0]);
assert.doesNotThrow(function () {
SVC.predict(vec2);
});
})
it('should return 1 for the given vector', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
var vec2 = new la.Vector([3, 0]);
assert.strictEqual(SVC.predict(vec2), 1);
})
it('should throw an exception if the vector is longer', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
var vec2 = new la.Vector([3, 0, 1]);
assert.throws(function () {
SVC.predict(vec2);
});
})
it('should throw an exception if the vector is shorter', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
var vec2 = new la.Vector([3]);
assert.throws(function () {
SVC.predict(vec2);
});
})
it('should return the vector [1, 1, -1] for the given matrix', function () {
var matrix = new la.Matrix([[0, 1, -1, 0], [1, 0, 0, -1]]);
var vec = new la.Vector([1, 1, -1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
var matrix2 = new la.Matrix([[1, 3, -1], [0, 3, -2]]);
var predicted = SVC.predict(matrix2);
assert.strictEqual(predicted[0], 1);
assert.strictEqual(predicted[1], 1);
assert.strictEqual(predicted[2], -1);
})
it('should throw an exception if the matrix has too many rows', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
var matrix2 = new la.Matrix([[1, 3, -1], [0, 3, -2], [1, 1, 2]]);
assert.throws(function () {
SVC.predict(matrix2);
});
})
it('should throw an exception if the matrix has too lesser rows', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
var matrix2 = new la.Matrix([[1, 3, -1]]);
assert.throws(function () {
SVC.predict(matrix2);
});
})
});
describe('DecisionFunction Tests', function () {
it('should not throw an exception', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
var vec2 = new la.Vector([3, 0]);
assert.doesNotThrow(function () {
SVC.decisionFunction(vec2);
});
})
it('should return the distance of the vector from the hyperplane', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
var vec2 = new la.Vector([1, 1]);
var distance = SVC.decisionFunction(vec2);
assert.eqtol(distance, 1, 1e-3);
})
it('should throw an exception if the vector is too long', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
var vec2 = new la.Vector([1, 1, -1]);
assert.throws(function () {
SVC.decisionFunction(vec2);
});
})
it('should throw an exception if the vector is too short', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
var vec2 = new la.Vector([1]);
assert.throws(function () {
SVC.decisionFunction(vec2);
});
})
it('should return a vector of distances if the given parameter is a matrix', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
var matrix2 = new la.Matrix([[1, -1, 0], [2, 1, -3]]);
var distance = SVC.decisionFunction(matrix2);
assert.eqtol(distance[0], 1, 1e-3);
assert.eqtol(distance[1], -1, 1e-3);
assert.eqtol(distance[2], 0, 1e-2);
})
it('should throw an exception if the matrix has too many rows', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
var matrix2 = new la.Matrix([[1, -1, 0], [2, 1, -3], [1, -2, 0]]);
assert.throws(function () {
SVC.decisionFunction(matrix2);
});
})
it('should throw an exception if the matrix has too lesser or rows', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
var matrix2 = new la.Matrix([[1, -1, 0]]);
assert.throws(function () {
SVC.decisionFunction(matrix2);
});
})
});
describe('Serialization Tests', function () {
it('should serialize and deserialize', function () {
var matrix = new la.Matrix([[1, -1], [0, 0]]);
var vec = new la.Vector([1, -1]);
var SVC = new analytics.SVC();
SVC.fit(matrix, vec);
SVC.save(require('../../index.js').fs.openWrite('svc_test.bin')).close();
var SVC2 = new analytics.SVC(require('../../index.js').fs.openRead('svc_test.bin'));
assert.deepEqual(SVC.getParams(), SVC2.getParams());
assert.eqtol(SVC.weights.minus(SVC2.weights).norm(), 0, 1e-8);
})
});
});